Includes bibliographical references.

[Article Title: Conditional Text Generation for Harmonious Human-Machine Interaction / B. Guo, H. Wang, Y. Ding, W. Wu, S. Hao, Y. Sun, and Z. Yu, p. 14 - 14:50]

Abstract: In recent years, with the development of deep learning, text-generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text-generation technology, that is, the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional Text Generation (CTG) has thus become a research hotspot. As a promising research field, we find that much attention has been paid to exploring it. Therefore, we aim to give a comprehensive review of the new research trends of CTG. We first summarize several key techniques and illustrate the technical evolution route in the field of neural text generation, based on the concept model of CTG. We further make an investigation of existing CTG fields and propose several general learning models for CTG. Finally, we discuss the open issues and promising research directions of CTG.

https://doi.org/10.1145/3439816

[Article Title: Aspect-Aware Response Generation for Multimodal Dialogue System / M. Firdaus, N. Thakur, and A. Ekbal, p. 15 - 15:33]

Abstract: Multimodality in dialogue systems has opened up new frontiers for the creation of robust conversational agents. Any multimodal system aims at bridging the gap between language and vision by leveraging diverse and often complementary information from image, audio, and video, as well as text. For every task-oriented dialog system, different aspects of the product or service are crucial for satisfying the user’s demands. Based upon the aspect, the user decides upon selecting the product or service. The ability to generate responses with the specified aspects in a goal-oriented dialogue setup facilitates user satisfaction by fulfilling the user’s goals. Therefore, in our current work, we propose the task of aspect controlled response generation in a multimodal task-oriented dialog system. We employ a multimodal hierarchical memory network for generating responses that utilize information from both text and images. As there was no readily available data for building such multimodal systems, we create a Multi-Domain Multi-Modal Dialog (MDMMD++) dataset. The dataset comprises the conversations having both text and images belonging to the four different domains, such as hotels, restaurants, electronics, and furniture. Quantitative and qualitative analysis on the newly created MDMMD++ dataset shows that the proposed methodology outperforms the baseline models for the proposed task of aspect controlled response generation.

https://doi.org/10.1145/3430752

[Article Title: RHUPS: Mining Recent High Utility Patterns with Sliding Window–based Arrival Time Control over Data Streams / Y. Baek, U. Yun, H. Kim, H. Nam, H. Kim, J. C. -W. Lin, B. Vo, and W. Pedrycz, p. 16 -16:27]

Abstract: Databases that deal with the real world have various characteristics. New data is continuously inserted over time without limiting the length of the database, and a variety of information about the items constituting the database is contained. Recently generated data has a greater influence than the previously generated data. These are called the time-sensitive non-binary stream databases, and they include databases such as web-server click data, market sales data, data from sensor networks, and network traffic measurement. Many high utility pattern mining and stream pattern mining methods have been proposed so far. However, they have a limitation that they are not suitable to analyze these databases, because they find valid patterns by analyzing a database with only some of the features described above.

https://doi.org/10.1145/3430767

[Article Title: Constraint-based Scheduling for Paint Shops in the Automotive Supply Industry / F. Winter and N. Musliu, p. 17 - 17:25]

Abstract: Factories in the automotive supply industry paint a large number of items requested by car manufacturing companies on a daily basis. As these factories face numerous constraints and optimization objectives, finding a good schedule becomes a challenging task in practice, and full-time employees are expected to manually create feasible production plans. In this study, we propose novel constraint programming models for a real-life paint shop scheduling problem.

https://doi.org/10.1145/3430710

[Article Title: Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data / J. Dahmen and D.J. Cook, p. 18 - 18:18]

Abstract: Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection.

https://doi.org/10.1145/3439870

[Article Title: Flatter Is Better: Percentile Transformations for Recommender Systems / M. Mansoury, R. Burke, and B. Mobasher, p. 19 - 19:16]

Abstract: It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models.

https://doi.org/10.1145/3437910

[Article Title: Disentangled Item Representation for Recommender Systems / Z. Cui, F. Yu, S. Wu, Q. Liu, and L. Wang, p. 20 - 20:20]

Abstract: Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information for items (e.g., category, price, and style of clothing). Utilizing this attribute information for better item representations is popular in recent years. Some studies use the given attribute information as side information, which is concatenated with the item latent vector to augment representations.

https://doi.org/10.1145/3445811

[Article Title: Predicting Attributes of Nodes Using Network Structure / S. Ali, M. H. Shakeel, I. Khan, S. Faizullah, and M. A. Khan, p. 21 - 21:23]

Abstract: In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attribute values can be predicted by treating each node as a data point described by attributes and employing classification/regression algorithms.

https://doi.org/10.1145/3442390

[Article Title: Feature Grouping–based Trajectory Outlier Detection over Distributed Streams / J. Mao, J. Liu, C. Jin, and A. Zhou, p. 22 - 22:23]

Abstract: Owing to a wide variety of deployment of GPS-enabled devices, tremendous amounts of trajectories have been generated in distributed stream manner. It opens up new opportunities to track and analyze the moving behaviors of the entities. In this work, we focus on the issue of outlier detection over distributed trajectory streams, where the outliers refer to a few entities whose motion behaviors are significantly different from their local neighbors.

https://doi.org/10.1145/3444753

[Article Title: Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems / Z. Li, R. Cai, H. W. Ng, M. Winslett, T. Z. J. Fu, B. Xu, X. Yang, and Z. Zhang, p. 23 - 23:21]

Abstract: Data-driven models are becoming essential parts in modern mechanical systems, commonly used to capture the behavior of various equipment and varying environmental characteristics. Despite the advantages of these data-driven models on excellent adaptivity to high dynamics and aging equipment, they are usually hungry for massive labels, mostly contributed by human engineers at a high cost.

https://doi.org/10.1145/3445033

[Article Title: Active Learning for Effectively Fine-Tuning Transfer Learning to Downstream Task / Md. A. Bashar and R. Nayak, p. 24 - 24:24]

Abstract: Language model (LM) has become a common method of transfer learning in Natural Language Processing (NLP) tasks when working with small labeled datasets. An LM is pretrained using an easily available large unlabelled text corpus and is fine-tuned with the labelled data to apply to the target (i.e., downstream) task.

https://doi.org/10.1145/3446343

[Article Title: Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network / J. Chen, K. Li, K. Li, P.S. Yu, and Z. Zeng, p. 25 - 25:22]

Abstract: Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network.

https://doi.org/10.1145/3446342

[Article Title: Attentive Excitation and Aggregation for Bilingual Referring Image Segmentation / Q. Zhou, T. Hui, R. Wang, H. Hu, and S. Liu, p. 26 - 26:17]

Abstract: The goal of referring image segmentation is to identify the object matched with an input natural language expression. Previous methods only support English descriptions, whereas Chinese is also broadly used around the world, which limits the potential application of this task. Therefore, we propose to extend existing datasets with Chinese descriptions and preprocessing tools for training and evaluating bilingual referring segmentation models.

https://doi.org/10.1145/3446345