The utilization of artificial intelligence tools and methods in agriculture has increased over the last few years. However, farmers and agronomists are uncertain about trusting such tools and the findings of machine/deep learning model predictions. This work develops a novel eXplainable AI (XAI) technique for smart farming yield prediction that can more effectively explain prediction outcomes when compared with state-of-the-art current explainers, LIME and SHAP. We call the proposed model, which considers both attributes and time lag, the Duo Attention eXplainable Mechanism (DAXM). We have developed and tested the model with two separate farming data sets and the results of our experiments demonstrate the effectiveness of prediction features for a three-week window on both tomato and strawberry yield prediction. We show that the explanation of such features can be achieved more effectively through our proposed DAXM model while these explanations significantly differ at the 95% confidence level from those generated by LIME and SHAP. DAXM is also more aligned with expert opinion with a higher degree of agreement with expert-reported feature importance measures as compared with LIME and SHAP. The proposed XAI approach for smart farming yield prediction offers effective explanations that can enrich user adaption of the cutting-edge neural models in the domain.

Information cascade popularity prediction is critical for many applications, including but not limited to identifying fake news and accurate recommendations. Traditional feature-based methods heavily rely on handcrafted features, which are domain-specific and lack generalizability to new domains. To address this problem, researchers have turned to neural network-based approaches. However, most existing methods follow a sampling-based modeling approach, potentially losing continuous dynamic information that emerges during the information diffusion process. In this paper, we propose Hierarchical Temporal Graph Attention Networks for cascade popularity prediction (HierCas), which operates on the entire cascade graph by a dynamic graph modeling approach. By leveraging time-aware node embedding, graph attention mechanisms, and hierarchical pooling structures, HierCas effectively captures the popularity trend implicit in the complex cascade. Extensive experiments conducted on two real-world datasets in different scenarios demonstrate that our HierCas significantly outperforms the state-of-the-art approaches. We have released our code at https://github.com/Daisy-zzz/HierCas.

Video Corpus Moment Retrieval aims to retrieve the relevant video from a large corpus and localize the corresponding moment within the target video based on a specific query. Existing methods have achieved promising accuracy and efficiency through dedicated model designs. However, we suggest these methods overly exploit dataset biases instead of semantics. This will lead to exaggerated performances on biased datasets and implicit a significant deficiency in generalizability, which is an important metric yet not considered in existing studies. In this paper, we observe the degradation caused by a spurious dependency and design a model to mitigate this harm. Specifically, we generate an Out-Of-Distributed (OOD) test set from a widely used TV Retrieval dataset, revealing the existing models' erroneous dependency on the temporal locations of target moments. Therefore, we utilize a theoretical Structural Causal Model (SCM) to dig into the roots of this dependency by constructing causal paths for the models. Furthermore, we propose a concrete Clip Location Deconfounding Model (CLDM) to disentangle the confounded video features into the content part and the location confounder part, then produce results with causal intervention. Experiments show that CLDM significantly alleviates the impact brought by dataset biases thus providing advanced generalizability among existing works.

Abstract Artificial neural networks ( $${{{\texttt {ANN}}}}$$ ANN ) are widely used machine learning models. Their widespread use has attracted a lot of interest in their robustness. Many studies show that ’s performance can be highly vulnerable to input manipulation such as adversarial attacks and covariate drift. Therefore, various techniques that focus on improving $${{{\texttt {ANN}}}}$$ ANN ’s robustness have been proposed in the last few years. However, most of these works have mostly focused on image data. In this paper, we investigate the role of discretization in improving $${{{\texttt {ANN}}}}$$ ANN ’s robustness on tabular datasets. Two custom $${{{\texttt {ANN}}}}$$ ANN layers– and (collectively called ) are proposed. The two layers integrate discretization during the training phase to improve $${{{\texttt {ANN}}}}$$ ANN ’s ability to defend against adversarial attacks. Additionally, integrates dynamic discretization during testing phase as well, to provide a unified strategy to handle adversarial attacks and covariate drift. The experimental results on 24 publicly available datasets show that our proposed add much-needed robustness to $${{{\texttt {ANN}}}}$$ ANN for tabular datasets.

Abstract Large scale categorical datasets are ubiquitous in machine learning and the success of most deployed machine learning models rely on how effectively the features are engineered. For large-scale datasets, parametric methods are generally used, among which three strategies for feature engineering are quite common. The first strategy focuses on managing the breadth (or width) of a network, e.g., generalized linear models (aka. ). The second strategy focuses on the depth of a network, e.g., Artificial Neural networks or (aka. ). The third strategy relies on factorizing the interaction terms, e.g., Factorization Machines (aka. ). Each of these strategies brings its own advantages and disadvantages. Recently, it has been shown that for categorical data, combination of various strategies leads to excellent results. For example, -Learning, , etc., leads to state-of-the-art results. Following the trend, in this work, we have proposed another learning framework—-Learning, based on the combination of , , , and a newly introduced component named (). is in the form of a Bayesian network classifier whose structure is learned apriori, and parameters are learned by optimizing a joint objective function along with , and parts. We denote the learning of parameters as . Additionally, the parameters of are constrained to be actual probabilities—therefore, it is extremely interpretable. Furthermore, one can sample or generate data from , which can facilitate learning and provides a framework for knowledge-guided machine learning. We demonstrate that our proposed framework possesses the resilience to maintain excellent classification performance when confronted with biased datasets. We evaluate the efficacy of our framework in terms of classification performance on various benchmark large-scale categorical datasets and compare against state-of-the-art methods. It is shown that, framework (a) exhibits superior performance on classification tasks, (b) boasts outstanding interpretability and (c) demonstrates exceptional resilience and effectiveness in scenarios involving skewed distributions.

This paper introduces an effective and efficient framework for retinal vessel segmentation. First, we design a Transformer-CNN hybrid model in which a Transformer module is inserted inside the U-Net to capture long-range interactions. Second, we design a dual-path decoder in the U-Net framework, which contains two decoding paths for multi-task outputs. Specifically, we train the extra decoder to predict vessel skeletons as an auxiliary task which helps the model learn balanced features. The proposed framework, named as TSNet, not only achieves good performances in a fully supervised learning manner but also enables a rough skeleton annotation process. The annotators only need to roughly delineate vessel skeletons instead of giving precise pixel-wise vessel annotations. To learn with rough skeleton annotations plus a few precise vessel annotations, we propose a skeleton semi-supervised learning scheme. We adopt a mean teacher model to produce pseudo vessel annotations and conduct annotation correction for roughly labeled skeletons annotations. This learning scheme can achieve promising performance with fewer annotation efforts. We have evaluated TSNet through extensive experiments on five benchmarking datasets. Experimental results show that TSNet yields state-of-the-art performances on retinal vessel segmentation and provides an efficient training scheme in practice.

Tabular data generation has seen renewed interest with the advent of Generative Adversarial Networks (GAN). Recently, it has been shown that one can use a Bayesian network as either a generator or a discriminator in the GAN framework, resulting in an algorithm known as GANBLR. It has been shown that GANBLR gives state of the art results for tabular data generation. However, the model has one limitation. It uses class attributes during model training. For example, a supervised Bayesian network is needed as a generator at training time. This makes GANBLR inapplicable for cases where we do not have access to class information. Addressing this shortcoming of GANBLR has been the main motivation of this work. In this work, we have proposed a new model of tabular data generation – Masked Ensemble Tabular Generator (MEG), which does not require class labels to generate tabular data. The proposed models rely on a novel strategy of using a collection of Bayesian networks as part of the generator, and relies on masking operations to train the generator efficiently. It also uses a group-based similarity measure to adjust the number of samples generated from each Bayesian network in the collection. We perform extensive experiments on a variety of datasets and demonstrate that MEG not only outperforms baselines that do not have class information during training, such as CTGAN and TVAE, but also outperforms baselines that provide access to class information during training, such as TableGAN and CtabGANmethods. It has almost similar performance in terms of machine learning utility to GANBLR, and of course is greatly advantaged by being truly unsupervised in nature. We highlight this by demonstrating its applicability to a clustering task. We also investigate the privacy preserving capabilities of MEG and demonstrate its superior performance compared to other baselines.

The sensing capability of a Frequency Modulated Continuous Wave (FMCW) radar system is primarily determined by the hardware. However, we found that the mappings from high-resolution radar to low-resolution radar are not arbitrary, especially in the range-azimuth domain. We exploit such mappings to elevate the sensing power of an FMCW sensing system without modifying the hardware using a data-driven super-resolution approach. By training a super-resolution deep neural network with paired low-resolution range-azimuth maps and their corresponding high-resolution range-azimuth maps, we show that the trained super-resolution network can be used as a generic FMCW radar pre-processing module which improves the effective range resolution and the angular resolution of the raw inputs. The proposed super-resolution approach is not hardware dependent due to our proposed down-sampling data generation method. Downstream sensing tasks can be readily benefited from the elevated range-azimuth maps. We evaluated our approach on public and private FMCW human activity recognition (HAR) datasets and saw significant accuracy improvement.

Driven by plummeting camera prices and advances of video inference algorithms, video cameras are deployed ubiquitously and organizations usually rely on live video analytics to retrieve key information, such as the locations and identities of target objects. However, analyzing real-time video poses severe challenges to today’s network and computation systems. To balance accuracy, bandwidth usage, and latency, we present EVA, an edge-assisted real-time video analytics framework, which coordinates computationally weak cameras with more powerful edge servers to enable video analytics under the accuracy and latency requirements of applications. EVA treats the region where a target object is located as a fine-grained transmission unit and exploits the redundancies in both spatial and temporal domains to reduce the bandwidth usage. Based on the framework, we design an adaptive offloading algorithm, which coordinates the recognition process between the camera and the server. To adapt to complex environments, we then design a threshold adjustment algorithm to tune the confidence threshold dynamically. Experiments on real-world video feeds show that compared to several recent baselines on multiple video genres, EVA maintains high accuracy while reducing bandwidth usage by up to 90%.

Abstract Generative adversarial network () models have been successfully utilized in a wide range of machine learning applications, and tabular data generation domain is not an exception. Notably, some state-of-the-art models of tabular data generation, such as , , , etc. are based on models. Even though these models have resulted in superior performance in generating artificial data when trained on a range of datasets, there is a lot of room (and desire) for improvement. Not to mention that existing methods do have some weaknesses other than performance. For example, the current methods focus only on the performance of the model, and limited emphasis is given on the interpretation of the model. Secondly, the current models operate on raw features only, and hence they fail to exploit any prior knowledge on explicit feature interactions that can be utilized during data generation process. To alleviate the two above-mentioned limitations, in this work, we propose a novel tabular data generation model— G enerative A dversarial Network modelling inspired from N aive B ayes and L ogistic R egression’s relationship ( $${ { \texttt {GANBLR} } }$$ GANBLR ), which not only address the interpretation limitation of existing tabular -based models but provides capability to handle explicit feature interactions as well. Through extensive evaluations on wide range of datasets, we demonstrate $${ { \texttt {GANBLR} } }$$ GANBLR ’s superior performance as well as better interpretable capability (explanation of feature importance in the synthetic generation process) as compared to existing state-of-the-art tabular data generation models.

Generative Adversarial Networks (GAN) models have led to a major breakthrough in data generation of various sorts. Over the years, we have seen several applications of GAN-based learning for tabular data generation as well. Very recently, GAN-based learning by incorporating Bayesian Networks (BN) as generator and discriminator – GANBLR, has shown to lead to state-of-the-art (SOTA) results for tabular data generation. Despite the impressive performance, GANBLR has an inherent weakness that it can only generate data with categorical attributes. Additionally, the model is trained and tested only with a restricted Bayesian Network. In this work, we have proposed an extension over GANBLR framework – GANBLR++, that has the capacity to generate numeric attributes, by leveraging Dirichlet Mixture Model. We also leverage unrestricted BN in GANBLR framework, and discuss how the use of unrestricted BN can lead to better quality data, as well as more interpretable model. We evaluate the effectiveness of GANBLR++ on wide range of datasets by demonstrating that it generates data of better quality as compared to existing SOTA models for tabular (numeric and categorical) data generation such as CTGAN, MedGAN and TableGAN.

Generative Adversarial Network (GAN) models have shown to be effective in a wide range of machine learning applications, and tabular data generation process has not been an exception. Notably, some state-of-the-art models of tabular data generation, such as CTGAN, TableGan, MedGAN, etc. are based on GAN models. Even though these models have resulted in superiour performance in generating artificial data when trained on a range of datasets, there is a lot of room (and desire) for improvement. Not to mention that existing methods do have some weaknesses other than performance. E.g., the current methods focus only on the performance of the model, and limited emphasis is given to the interpretation of the model. Secondly, the current models operate on raw features only, and hence they fail to exploit any prior knowledge on explicit feature interactions that can be utilized during data generation process. To alleviate the two above-mentioned limitations, in this work, we propose a novel tabular data generation model– Generative Adversarial Network modelling inspired from Naive Bayes and Logistic Regression’s relationship (GANBLR), which can not only address the interpretation limitation in existing tabular GAN-based models but can provide capability to handle explicit feature interactions. By extensively evaluating on wide range of datasets, we demonstrate GANBLR’S superiour performance as well as better interpretable capability (explanation of feature importance in the synthetic generation process) as compared to existing state-of-the-art tabular data generation models.