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Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing

Jan 14, 2022
Philipp Haindl, Georg Buchgeher, Maqbool Khan, Bernhard Moser

With the proliferation of AI-enabled software systems in smart manufacturing, the role of such systems moves away from a reactive to a proactive role that provides context-specific support to manufacturing operators. In the frame of the EU funded Teaming.AI project, we identified the monitoring of teaming aspects in human-AI collaboration, the runtime monitoring and validation of ethical policies, and the support for experimentation with data and machine learning algorithms as the most relevant challenges for human-AI teaming in smart manufacturing. Based on these challenges, we developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning with a particular focus on its scalability. Our approach uses knowledge graphs to capture product- and process specific knowledge in the manufacturing process and to utilize it for relational machine learning. This allows for context-specific recommendations for actions in the manufacturing process for the optimization of product quality and the prevention of physical harm. The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain. In this paper we discuss the identified challenges for such a reference software architecture, present its preliminary status, and sketch our further research vision in this project.

* Conference: ICSE-NIER 2022 - The 44th International Conference on Software Engineering, 5 pages, 1 figure 

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Efficient-Dyn: Dynamic Graph Representation Learning via Event-based Temporal Sparse Attention Network

Jan 04, 2022
Yan Pang, Chao Liu

Static graph neural networks have been widely used in modeling and representation learning of graph structure data. However, many real-world problems, such as social networks, financial transactions, recommendation systems, etc., are dynamic, that is, nodes and edges are added or deleted over time. Therefore, in recent years, dynamic graph neural networks have received more and more attention from researchers. In this work, we propose a novel dynamic graph neural network, Efficient-Dyn. It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure. Therefore, while avoiding the use of snapshots to cause information loss, it also achieves a finer time granularity, which is close to what continuous networks could provide. In addition, we also designed a lightweight module, Sparse Temporal Transformer, to compute node representations through both structural neighborhoods and temporal dynamics. Since the fully-connected attention conjunction is simplified, the computation cost is far lower than the current state-of-the-arts. Link prediction experiments are conducted on both continuous and discrete graph datasets. Through comparing with several state-of-the-art graph embedding baselines, the experimental results demonstrate that Efficient-Dyn has a faster inference speed while having competitive performance.


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Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types

Mar 24, 2021
Thomas Mensink, Jasper Uijlings, Alina Kuznetsova, Michael Gygli, Vittorio Ferrari

Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e. pre-training a model for image classification on the ILSVRC dataset, and then fine-tune on any target task. However, previous systematic studies of transfer learning have been limited and the circumstances in which it is expected to work are not fully understood. In this paper we carry out an extensive experimental exploration of transfer learning across vastly different image domains (consumer photos, autonomous driving, aerial imagery, underwater, indoor scenes, synthetic, close-ups) and task types (semantic segmentation, object detection, depth estimation, keypoint detection). Importantly, these are all complex, structured output tasks types relevant to modern computer vision applications. In total we carry out over 1200 transfer experiments, including many where the source and target come from different image domains, task types, or both. We systematically analyze these experiments to understand the impact of image domain, task type, and dataset size on transfer learning performance. Our study leads to several insights and concrete recommendations for practitioners.

* submitted to TPAMI 

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OAG-BERT: Pre-train Heterogeneous Entity-augmented Academic Language Models

Mar 23, 2021
Xiao Liu, Da Yin, Xingjian Zhang, Kai Su, Kan Wu, Hongxia Yang, Jie Tang

To enrich language models with domain knowledge is crucial but difficult. Based on the world's largest public academic graph Open Academic Graph (OAG), we pre-train an academic language model, namely OAG-BERT, which integrates massive heterogeneous entities including paper, author, concept, venue, and affiliation. To better endow OAG-BERT with the ability to capture entity information, we develop novel pre-training strategies including heterogeneous entity type embedding, entity-aware 2D positional encoding, and span-aware entity masking. For zero-shot inference, we design a special decoding strategy to allow OAG-BERT to generate entity names from scratch. We evaluate the OAG-BERT on various downstream academic tasks, including NLP benchmarks, zero-shot entity inference, heterogeneous graph link prediction, and author name disambiguation. Results demonstrate the effectiveness of the proposed pre-training approach to both comprehending academic texts and modeling knowledge from heterogeneous entities. OAG-BERT has been deployed to multiple real-world applications, such as reviewer recommendations and paper tagging in the AMiner system. It is also available to the public through the CogDL package.


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Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product

Sep 15, 2020
Tiangang Zhu, Yue Wang, Haoran Li, Youzheng Wu, Xiaodong He, Bowen Zhou

Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product recommendations, and product retrieval. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, we propose a multimodal method to jointly predict product attributes and extract values from textual product descriptions with the help of the product images. We argue that product attributes and values are highly correlated, e.g., it will be easier to extract the values on condition that the product attributes are given. Thus, we jointly model the attribute prediction and value extraction tasks from multiple aspects towards the interactions between attributes and values. Moreover, product images have distinct effects on our tasks for different product attributes and values. Thus, we selectively draw useful visual information from product images to enhance our model. We annotate a multimodal product attribute value dataset that contains 87,194 instances, and the experimental results on this dataset demonstrate that explicitly modeling the relationship between attributes and values facilitates our method to establish the correspondence between them, and selectively utilizing visual product information is necessary for the task. Our code and dataset will be released to the public.

* Accepted by EMNLP 2020 

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Grading video interviews with fairness considerations

Jul 02, 2020
Abhishek Singhania, Abhishek Unnam, Varun Aggarwal

There has been considerable interest in predicting human emotions and traits using facial images and videos. Lately, such work has come under criticism for poor labeling practices, inconclusive prediction results and fairness considerations. We present a careful methodology to automatically derive social skills of candidates based on their video response to interview questions. We, for the first time, include video data from multiple countries encompassing multiple ethnicities. Also, the videos were rated by individuals from multiple racial backgrounds, following several best practices, to achieve a consensus and unbiased measure of social skills. We develop two machine-learning models to predict social skills. The first model employs expert-guidance to use plausibly causal features. The second uses deep learning and depends solely on the empirical correlations present in the data. We compare errors of both these models, study the specificity of the models and make recommendations. We further analyze fairness by studying the errors of models by race and gender. We verify the usefulness of our models by determining how well they predict interview outcomes for candidates. Overall, the study provides strong support for using artificial intelligence for video interview scoring, while taking care of fairness and ethical considerations.

* Submitted to NeurIPS2020 

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Multi-Objective Generalized Linear Bandits

May 30, 2019
Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang

In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives. MOB has found many real-world applications as varied as online recommendation and network routing. On the other hand, these applications typically contain contextual information that can guide the learning process which, however, is ignored by most of existing work. To utilize this information, we associate each arm with a context vector and assume the reward follows the generalized linear model (GLM). We adopt the notion of Pareto regret to evaluate the learner's performance and develop a novel algorithm for minimizing it. The essential idea is to apply a variant of the online Newton step to estimate model parameters, based on which we utilize the upper confidence bound (UCB) policy to construct an approximation of the Pareto front, and then uniformly at random choose one arm from the approximate Pareto front. Theoretical analysis shows that the proposed algorithm achieves an $\tilde O(d\sqrt{T})$ Pareto regret, where $T$ is the time horizon and $d$ is the dimension of contexts, which matches the optimal result for single objective contextual bandits problem. Numerical experiments demonstrate the effectiveness of our method.


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Designing and Implementing Data Warehouse for Agricultural Big Data

May 29, 2019
Vuong M. Ngo, Nhien-An Le-Khac, M-Tahar Kechadi

In recent years, precision agriculture that uses modern information and communication technologies is becoming very popular. Raw and semi-processed agricultural data are usually collected through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, farmers and agribusinesses, etc. Besides, agricultural datasets are very large, complex, unstructured, heterogeneous, non-standardized, and inconsistent. Hence, the agricultural data mining is considered as Big Data application in terms of volume, variety, velocity and veracity. It is a key foundation to establishing a crop intelligence platform, which will enable resource efficient agronomy decision making and recommendations. In this paper, we designed and implemented a continental level agricultural data warehouse by combining Hive, MongoDB and Cassandra. Our data warehouse capabilities: (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) replication and recovery; (9) consistency, availability and partition tolerant; (10) distributed and cloud deployment. We also evaluate the performance of our data warehouse.

* BigData 2019 
* Business intelligent, data warehouse, constellation schema, Big Data, precision agriculture 

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Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings

Apr 01, 2019
Ciosici, Manuel, Sommer, Tobias, Assent, Ira

As abbreviations often have several distinct meanings, disambiguating their intended meaning in context is important for Machine Reading tasks such as document search, recommendation and question answering. Existing approaches mostly rely on labelled examples of abbreviations and their correct long forms, which is costly to generate and limits their applicability and flexibility. Importantly, they need to be subjected to a full empirical evaluation, which is cumbersome in practice. In this paper, we present an entirely unsupervised abbreviation disambiguation method (called UAD) that picks up abbreviation definitions from text. Creating distinct tokens per meaning, we learn context representations as word embeddings. We demonstrate how to further boost abbreviation disambiguation performance by obtaining better context representations from additional unstructured text. Our method is the first abbreviation disambiguation approach which features a transparent model that allows performance analysis without requiring full-scale evaluation, making it highly relevant for real-world deployments. In our thorough empirical evaluation, UAD achieves high performance on large real world document data sets from different domains and outperforms both baseline and state-of-the-art methods. UAD scales well and supports thousands of abbreviations with many different meanings with a single model.


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Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding

Dec 02, 2016
Yinchong Yang, Peter A. Fasching, Markus Wallwiener, Tanja N. Fehm, Sara Y. Brucker, Volker Tresp

With the introduction of the Electric Health Records, large amounts of digital data become available for analysis and decision support. When physicians are prescribing treatments to a patient, they need to consider a large range of data variety and volume, making decisions increasingly complex. Machine learning based Clinical Decision Support systems can be a solution to the data challenges. In this work we focus on a class of decision support in which the physicians' decision is directly predicted. Concretely, the model would assign higher probabilities to decisions that it presumes the physician are more likely to make. Thus the CDS system can provide physicians with rational recommendations. We also address the problem of correlation in target features: Often a physician is required to make multiple (sub-)decisions in a block, and that these decisions are mutually dependent. We propose a solution to the target correlation problem using a tensor factorization model. In order to handle the patients' historical information as sequential data, we apply the so-called Encoder-Decoder-Framework which is based on Recurrent Neural Networks (RNN) as encoders and a tensor factorization model as a decoder, a combination which is novel in machine learning. With experiments with real-world datasets we show that the proposed model does achieve better prediction performances.


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