Reviewerly/exHarmony huggingface dataset:
Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs

Node classification in graph-structured data aims to classify the nodes where labels are only available for a subset of nodes. This problem has attracted considerable research efforts in recent years. In real-world applications, both graph topology and node attributes evolve over time. Existing techniques, however, mainly focus on static graphs and lack the capability to simultaneously learn both temporal and spatial/structural features. Node classification in temporal attributed graphs is challenging for two major aspects. First, effectively modeling the spatio-temporal contextual information is hard. Second, as temporal and spatial dimensions are entangled, to learn the feature representation of one target node, it’s desirable and challenging to differentiate the relative importance of different factors, such as different neighbors and time periods. In this paper, we propose STAR, a spatio-temporal attentive recurrent network model, to deal with the above challenges. STAR extracts the vector representation of neighborhood by sampling and aggregating local neighbor nodes. It further feeds both the neighborhood representation and node attributes into a gated recurrent unit network to jointly learn the spatio-temporal contextual information. On top of that, we take advantage of the dual attention mechanism to perform a thorough analysis on the model interpretability. Extensive experiments on real datasets demonstrate the effectiveness of the STAR model.
Deep Reinforcement Learning of Navigation in a Complex and Crowded Environment with a Limited Field of View

Mobile robots are required to navigate freely in a complex and crowded environment in order to provide services to humans. For this navigation ability, deep reinforcement learning (DRL)-based methods are gaining increasing attentions. However, existing DRL methods require a wide field of view (FOV), which imposes the usage of high-cost lidar devices. In this paper, we explore the possibility of replacing expensive lidar devices with affordable depth cameras which have a limited FOV. First, we analyze the effect of a limited field of view in the DRL agents. Second, we propose a LSTM agent with Local-Map Critic (LSTM-LMC), which is a novel DRL method to learn efficient navigation in a complex environment with a limited FOV. Lastly, we introduce the dynamics randomization technique to improve the robustness of the DRL agents in the real world. We found that our method with a limited FOV can outperform the methods having a wide FOV but limited memory. We provide the empirical evidence that our method learns to implicitly model the surrounding environment and dynamics of other agents. We also show that a robot with a single depth camera can navigate through a complex real-world environment using our method.
Classification Accuracy Score for Conditional Generative Models

Deep generative models (DGMs) of images are now sufficiently mature that they produce nearly photorealistic samples and obtain scores similar to the data distribution on heuristics such as Frechet Inception Distance (FID). These results, especially on large-scale datasets such as ImageNet, suggest that DGMs are learning the data distribution in a perceptually meaningful space and can be used in downstream tasks. To test this latter hypothesis, we use class-conditional generative models from a number of model classes—variational autoencoders, autoregressive models, and generative adversarial networks (GANs)—to infer the class labels of real data. We perform this inference by training an image classifier using only synthetic data and using the classifier to predict labels on real data. The performance on this task, which we call Classification Accuracy Score (CAS), reveals some surprising results not identified by traditional metrics and constitute our contributions. First, when using a state-of-the-art GAN (BigGAN-deep), Top-1 and Top-5 accuracy decrease by 27.9% and 41.6%, respectively, compared to the original data; and conditional generative models from other model classes, such as Vector-Quantized Variational Autoencoder-2 (VQ-VAE-2) and Hierarchical Autoregressive Models (HAMs), substantially outperform GANs on this benchmark. Second, CAS automatically surfaces particular classes for which generative models failed to capture the data distribution, and were previously unknown in the literature. Third, we find traditional GAN metrics such as Inception Score (IS) and FID neither predictive of CAS nor useful when evaluating non-GAN models. Furthermore, in order to facilitate better diagnoses of generative models, we open-source the proposed metric.
Fuzzy logic systems and medical applications

The combination of Artificial Neural Networks and Fuzzy Logic Systems enables the representation of real-world problems via the creation of intelligent and adaptive systems. By adapting the interconnections between layers, Artificial Neural networks are able to learn. A computing framework based on the concept of fuzzy set and rules as well as fuzzy reasoning is offered by fuzzy logic inference systems. The fusion of the aforementioned adaptive structures is called a “Neuro-Fuzzy” system. In this paper, the main elements of said structures are examined. Researchers have noticed that this fusion could be applied for pattern recognition in medical applications.
A deep neural network approach for behind-the-meter residential PV size, tilt and azimuth estimation

There is an ever-growing number of photovoltaic (PV) installations in the US and worldwide. Many utilities do not have complete or up-to-date information of the PVs present within their grids. This research presents a deep neural network approach for estimating PV size, tilt, and azimuth using only behind-the-meter data. It is found that the proposed deep neural network (DNN) method can estimate PV size with an error of 2.09% in a data set with fixed tilt and azimuth values and 3.98% in a data set with varying tilt and azimuths. This is a lower error than the benchmark linear regression approach. A net load data resolution of 1 min provides the lowest error when estimating the PV size. The proposed DNN is also reasonably robust to erroneous training data. When applied to estimate PV tilt and azimuth, the proposed method achieves a mean absolute percentage error of 10.1% and 2.8% respectively. These error metrics are 2.0× and 3.7× lower, respectively, than the benchmark linear regression achieves. It was observed that a higher data resolution (1 min) does not provide significant gains in accuracy. It is recommended that a data resolution of 60 min is used to reduce the effects of phenomena such as cloud enhancements. The proposed deep neural network approach is also highly robust, maintaining reasonable accuracy with high levels of mislabeled training data.
How bugs are born: a model to identify how bugs are introduced in software components

Abstract When identifying the origin of software bugs, many studies assume that “a bug was introduced by the lines of code that were modified to fix it”. However, this assumption does not always hold and at least in some cases, these modified lines are not responsible for introducing the bug. For example, when the bug was caused by a change in an external API. The lack of empirical evidence makes it impossible to assess how important these cases are and therefore, to which extent the assumption is valid. To advance in this direction, and better understand how bugs “are born”, we propose a model for defining criteria to identify the first snapshot of an evolving software system that exhibits a bug. This model, based on the perfect test idea, decides whether a bug is observed after a change to the software. Furthermore, we studied the model’s criteria by carefully analyzing how 116 bugs were introduced in two different open source software projects. The manual analysis helped classify the root cause of those bugs and created manually curated datasets with bug-introducing changes and with bugs that were not introduced by any change in the source code. Finally, we used these datasets to evaluate the performance of four existing SZZ-based algorithms for detecting bug-introducing changes. We found that SZZ-based algorithms are not very accurate, especially when multiple commits are found; the F-Score varies from 0.44 to 0.77, while the percentage of true positives does not exceed 63%. Our results show empirical evidence that the prevalent assumption, “a bug was introduced by the lines of code that were modified to fix it”, is just one case of how bugs are introduced in a software system. Finding what introduced a bug is not trivial: bugs can be introduced by the developers and be in the code, or be created irrespective of the code. Thus, further research towards a better understanding of the origin of bugs in software projects could help to improve design integration tests and to design other procedures to make software development more robust.
Towards Explainable Conversational Recommendation

Recent studies have shown that both accuracy and explainability are important for recommendation. In this paper, we introduce explainable conversational recommendation, which enables incremental improvement of both recommendation accuracy and explanation quality through multi-turn user-model conversation. We show how the problem can be formulated, and design an incremental multi-task learning framework that enables tight collaboration between recommendation prediction, explanation generation, and user feedback integration. We also propose a multi-view feedback integration method to enable effective incremental model update. Empirical results demonstrate that our model not only consistently improves the recommendation accuracy but also generates explanations that fit user interests reflected in the feedbacks.
Agents and robots for collaborating and supporting physicians in healthcare scenarios

Monitoring patients through robotics telehealth systems is an interesting scenario where patients’ conditions, and their environment, are dynamic and unknown variables. We propose to improve telehealth systems’ features to include the ability to serve patients with their needs, operating as human caregivers. The objective is to support the independent living of patients at home without losing the opportunity to monitor their health status. Application scenarios are several, and they spread from simple clinical assisting scenarios to an emergency one. For instance, in the case of a nursing home, the system would support in continuously monitoring the elderly patients. In contrast, in the case of an epidemic diffusion, such as COVID-19 pandemic, the system may help in all the early triage phases, significantly reducing the risk of contagion. However, the system has to let medical assistants perform actions remotely such as changing therapies or interacting with patients that need support. The paper proposes and describes a multi-agent architecture for intelligent medical care. We propose to use the beliefs-desires-intentions agent architecture, part of it is devised to be deployed in a robot. The result is an intelligent system that may allow robots the ability to select the most useful plan for unhandled situations and to communicate the choice to the physician for his validation and permission.
Performance Evaluation of Machine Learning Techniques for Mustard Crop Yield Prediction from Soil Analysis

Soil is an important parameter affecting crop yield prediction. Analysis of soil nutrients can aid farmers and soil analysts to get higher yield of the crops by making prior arrangements.In this paper, various machine learning techniques have been implemented in order to predict Mustard Crop yield in advance from soil analysis.Data for the experimental set-up has been collected from Department of Agriculture Department, Talab Tillo, Jammu; comprising soil samples of different districts of Jammu region for Mustard crop.For the current study, five supervised machine learning techniques namely K-Nearest Neighbor (KNN), Naïve Bayes, Multinomial Logistic Regression, Artificial Neural Network (ANN) and Random Forest have been applied on the collected data.To assess the performance of each technique under study; five parameters namely accuracy, recall, precision, specificity and f-score have been evaluated.Experimentation has been carried out to make known the most accurate technique for mustard crop yield prediction.From experimental results, it has been predicted that KNN and ANN (among the undertaken ML techniques for the study) found to be most accurate techniques for mustard crop yield prediction.
Sentiment Analysis About Investors and Consumers in Energy Market Based on BERT-BiLSTM

With the rapid development of social media, the number of online comments has exploded, and more and more people are willing to express their attitudes and feelings on the Internet. Under the influence of a series of major events all over the world, production order is facing a serious challenge, which severely impact on energy market. In 2020, a large number of investors' and consumers' comments related to social events began to appear on the Internet from China. However, the style and quality of online comments vary greatly, making it difficult to accurately extract users' views and tendencies. Based on the investors' and consumers' statements published on Chinese Internet, in this paper, we use statistical methods to classify the sentiment orientation of the Netizens firstly, and then use Bidirectional Encoder Representations from Transformer-Bidirectional Long Short Term Memory (BERT-BiLSTM) which is the combination forecasting method of Bidirectional Encoder Representations from Transformer (BERT) and Bidirectional Long Short Term Memory (BiLSTM), to model and forecast the sentiment orientation of users' statements, as well as being compared with its based models, BERT and BiLSTM. Among them, the accuracy and recall, which represent the predictive abilities on the overall samples and on the focus(the samples of Label 1) respectively, of BERT-BiLSTM model are 0.8620 and 0.7078 respectively, which are superior to those of BERT model (0.8559 and 0.5576) and LSTM model (0.7775 and 0.0747). The research results can accurately predict the sentiment orientation of Internet users during the social events so as to provide technical support for grasping the energy market trend.
Modulations of saliency signals at two hierarchical levels of priority computation revealed by spatial statistical distractor learning.

Many attention theories assume that selection is guided by a preattentive, spatial representation of the scene that combines bottom-up stimulus information with top-down influences (task goals and prior experience) to code for potentially relevant locations (priority map). At which level(s) of priority computation top-down influences modulate bottom-up stimulus signals is an open question. In a visual-search task, here we induced experience-driven spatial suppression (statistical learning) by presenting 1 of 2 salient distractors more frequently in one display region than the other. When a distractor standing out in the same dimension as the target was spatially biased in Experiment 1, processing of both the target and another, spatially unbiased distractor standing out in a different dimension was likewise hampered in the suppressed region. This indicates that constraining spatial suppression to a specific distractor feature is not possible, and participants instead resort to purely space-based (distractor-feature-independent) suppression at a supradimensional, overall-priority map. In line with a common locus of suppression, a novel computational model of distraction in visual search captures all 3 location effects with a single spatial-weighting parameter. In contrast, when the different-dimension distractor was spatially biased in Experiment 2, processing of other objects in the suppressed region was unaffected, indicating suppression constrained to a subordinate, dimension-specific level of priority computation. In sum, we demonstrate experience-driven top-down modulations of saliency signals at the overall-priority and dimension-specific levels that do not reach down to the specific distractor features. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Workflow techniques for the robust use of bayes factors.

Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions and it's unclear whether the details of the computational implementation (such as bridge sampling) are unbiased for complex analyses. Here, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors; the first-ever use of simulation-based calibration to test the accuracy and bias of Bayes factor estimates using bridge sampling; a study of the stability of Bayes factors against different MCMC draws and sampling variation in the data; and a look at the variability of decisions based on Bayes factors using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Self-supervised Learning: Generative or Contrastive

Deep supervised learning has achieved great success in the last decade.However, its deficiencies of dependence on manual labels and vulnerability toattacks have driven people to explore a better solution. As an alternative,self-supervised learning attracts many researchers for its soaring performanceon representation learning in the last several years. Self-supervisedrepresentation learning leverages input data itself as supervision and benefitsalmost all types of downstream tasks. In this survey, we take a look into newself-supervised learning methods for representation in computer vision, naturallanguage processing, and graph learning. We comprehensively review the existingempirical methods and summarize them into three main categories according totheir objectives: generative, contrastive, and generative-contrastive(adversarial). We further investigate related theoretical analysis work toprovide deeper thoughts on how self-supervised learning works. Finally, webriefly discuss open problems and future directions for self-supervisedlearning.
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining

Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. However, previous NAS methods search for architectures under one set of training hyper-parameters (i.e., a training recipe), overlooking superior architecture-recipe combinations. To address this, we present Neural Architecture-Recipe Search (NARS) to search both (a) architectures and (b) their corresponding training recipes, simultaneously. NARS utilizes an accuracy predictor that scores architecture and training recipes jointly, guiding both sample selection and ranking. Furthermore, to compensate for the enlarged search space, we leverage "free" architecture statistics (e.g., FLOP count) to pretrain the predictor, significantly improving its sample efficiency and prediction reliability. After training the predictor via constrained iterative optimization, we run fast evolutionary searches in just CPU minutes to generate architecturerecipe pairs for a variety of resource constraints, called FBNetV3. FBNetV3 makes up a family of state-of-the-art compact neural networks that outperform both automatically and manually-designed competitors. For example, FB-NetV3 matches both EfficientNet and ResNeSt accuracy on ImageNet with up to 2.0× and 7.1 × fewer FLOPs, respectively. Furthermore, FBNetV3 yields significant performance gains for downstream object detection tasks, improving mAP despite 18% fewer FLOPs and 34% fewer parameters than EfficientNet-based equivalents.
Hierarchical Bayesian Models for Latent Attribute Detection in Social Media

We present several novel minimally-supervised models for detecting latent attributes of social media users, with a focus on ethnicity and gender. Previouswork on ethnicity detection has used coarse-grained widely separated classes of ethnicity and assumed the existence of large amounts of training data such as the US census, simplifying the problem. Instead, we examine content generated by users in addition to name morpho-phonemics to detect ethnicity and gender. Further, weaddress this problem in a challenging setting where the ethnicity classes are more fine grained -- ethnicity classes in Nigeria -- and with very limited training data.
Learning a discriminant graph-based embedding with feature selection for image categorization

Graph-based embedding methods are very useful for reducing the dimension of high-dimensional data and for extracting their relevant features. In this paper, we introduce a novel nonlinear method called Flexible Discriminant graph-based Embedding with feature selection (FDEFS). The proposed algorithm aims to classify image sample data in supervised learning and semi-supervised learning settings. Specifically, our method incorporates the Manifold Smoothness, Margin Discriminant Embedding and the Sparse Regression for feature selection. The weights add ℓ2,1-norm regularization for local linear approximation. The sparse regression implicitly performs feature selection on the original features of data matrix and of the linear transform. We also provide an effective solution method to optimize the objective function. We apply the algorithm on six public image datasets including scene, face and object datasets. These experiments demonstrate the effectiveness of the proposed embedding method. They also show that proposed the method compares favorably with many competing embedding methods.
Deep Latent Low-Rank Representation for Face Sketch Synthesis

Face sketch synthesis is useful and profitable in digital entertainment. Most existing face sketch synthesis methods rely on the assumption that facial photographs/sketches form a low-dimensional manifold. Once the training data are insufficient, the manifold could not characterize the identity-specific information that is included in a test photograph but excluded in the training data. Thus, the synthesized sketch would lose this information, such as glasses, earrings, hairstyles, and hairpins. To provide the sufficient data and satisfy the assumption on manifold, we propose a novel face sketch synthesis framework based on deep latent low-rank representation (DLLRR) in this paper. The DLLRR induces the hidden training sketches with the identity-specific information as the hidden data to the insufficient original training sketches as the observed data. And it searches the lowest rank representation on the candidates of a test photograph from the both hidden and observed data. For the strong representational capability of the coupled autoencoder, we leverage it to reveal the hidden data. Experiment results on face photograph-sketch database illustrate that the proposed method can successfully provide the sufficient training data with the identity-specific information. And compared to the state of the arts, the proposed method synthesizes more clean and vivid face sketches.