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As social robots and other intelligent machines enter the home, artificial emotional intelligence (AEI) is taking center stage to address users’ desire for deeper, more meaningful human-machine interaction. To accomplish such efficacious interaction, the next-generation AEI needs comprehensive human emotion models for training. Unlike theory of emotion, which has been the historical focus in psychology, emotion models are a descriptive tool. In practice, the strongest models need robust coverage, which means defining the smallest core set of emotions from which all others can be derived. To achieve the desired coverage, we turn to word embeddings from natural language processing. Using unsupervised clustering techniques, our experiments show that with as few as 15 discrete emotion categories, we can provide maximum coverage across six major languages–Arabic, Chinese, English, French, Spanish, and Russian. In support of our findings, we also examine annotations from two large-scale emotion recognition datasets to assess the validity of existing emotion models compared to human perception at scale. Because robust, comprehensive emotion models are foundational for developing real-world affective computing applications, this work has broad implications in social robotics, human-machine interaction, mental healthcare, computational psychology, and entertainment. [ABSTRACT FROM AUTHOR]

We exploit the quasi-random assignment of federal bank examiners to syndicated loans to study the effect of supervision on corporate lending. Following supervisory rating downgrades, banks decrease credit commitments and downgrade internal risk assessments. Borrowers face larger commitment reductions whenever banks have low ex ante screening and monitoring incentives or whenever examiners' assessments contain more information than banks' assessments, suggesting that examinations complement bank monitoring. Although public firms can offset the loss of bank credit by tapping external capital markets, smaller and more opaque private firms draw on internal cash balances instead and reduce investment and sales growth. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4854. [ABSTRACT FROM AUTHOR]

Emerging computing paradigms provide field‐level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform‐independent, low‐resource‐consumption operating environment for edge service. The image‐pulling time of Docker is a crucial factor affecting the start‐up speed of edge services. The layer reuse mechanism of native Docker cannot fully utilize the duplicate data of node local images. In this paper, we propose a chunk reuse mechanism (CRM), which effectively targets node‐local duplicate data during container updates and reduces the volume of data transmission required for image building. We orchestrate the CRM process for cloud and remote‐cloud nodes to ensure that the resource overhead from container update data preparation and image reconstruction is within an acceptable range. The experimental results show that the CRM proposed in this paper can effectively utilize the node local duplicate data in the synchronous update of containers in multiple nodes, reduce the volume of data transmission, and significantly improve container update efficiency. [ABSTRACT FROM AUTHOR]

We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research. Categories and Subject Descriptors: H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing--Indexing methods; 1.4.9 [Image Processing and Computer Vision]: Applications General Terms: Algorithms, Documentation, Performance Additional Key Words and Phrases: Content-based image retrieval, annotation, tagging, modeling, learning ACM Reference Format: Datta, R., Joshi, D., Li, J., and Wang, J. Z. 2008. Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40, 2, Article 5 (April 2008), 60 pages DOI = 10.1145/1348246.1348248 http:// doi.acm.org/10.1145/1348246.1348248

Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (Multiple-Instance Learning via Embedded instance Selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence, 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty. Index Terms--Multiple-instance learning, feature subset selection, 1-norm support vector machine, image categorization, object recognition, drug activity prediction.

A computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) is explored and its applications to satellite image segmentation are demonstrated. Experiments reveal the computational efficiency of the proposed parameter technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images.