Holy Lovenia, Rahmad Mahendra, Salsabil Maulana Akbar, Lester James V. Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno P. Kampman, Joel Ruben Antony Moniz, Muhammad Ravi Shulthan Habibi, Frederikus Hudi, Railey Montalan, Ryan Ignatius, Joanito Agili Lopo, William Nixon, Börje F. Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Amadeus, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Halim Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Adha Ryanda, Sonny Lazuardi Hermawan, Dan John Velasco, Muhammad Dehan Al Kautsar, Willy Fitra Hendria, Yasmin Moslem, Noah Flynn, Muhammad Farid Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Reza Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Ngee Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Fikri Aji, Sedrick Keh, Genta Indra Winata, Ruochen Zhang, Fajri Koto, Zheng-Xin Yong, Samuel Cahyawijaya. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Deep learning models have shown excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. However, the opacity of the decision-making mechanisms within these models hamper their credibility in practical applications. Therefore, numerous explainable artificial intelligence (XAI) methods have been developed to interpret the models. Among them, the randomized input sampling for explanation (RISE) method introduces input random pixel perturbations to observe resulting changes in the output by generating saliency heatmaps. However, unlike optical images, SAR images have their unique physical properties. This paper introduces the attributed scattering center (ASC) into the RISE method, known as the ASC-RISE method guided by physical information, to explain the model. Experimental results demonstrate that the heatmaps generated by ASC-RISE effectively locate the model’s decision features and provide corresponding physical information.

Olfactory interfaces are pivotal in HCI, yet their development is hindered by limited application scenarios, stifling the discovery of new research opportunities. This challenge primarily stems from existing design tools focusing predominantly on odor display devices and the creation of standalone olfactory experiences, rather than enabling rapid adaptation to various contexts and tasks. Addressing this, we introduce Mul-O, a novel task-oriented development platform crafted to aid semi-professionals in navigating the diverse requirements of potential application scenarios and effectively prototyping ideas. Mul-O facilitates the swift association and integration of olfactory experiences into functional designs, system integrations, and concept validations. Comprising a web UI for task-oriented development, an API server for seamless third-party integration, and wireless olfactory display hardware, Mul-O significantly enhances the ideation and prototyping process in multisensory tasks. This was verified by a 15-day workshop attended by 30 participants. The workshop produced seven innovative projects, underscoring Mul-O’s efficacy in fostering olfactory innovation.

Deep learning has significantly enhanced the performance of automatic target recognition (ATR) in synthetic aperture radar (SAR). However, the concept of model overinterpretation, characterized by classifiers discerning strong class evidence within image regions that lack semantically salient features related to target (e.g., background clutter in SAR images), has undermined confidence in the reliability of deep learning models. Previous studies predominantly relied solely on one interpretability method to qualitatively identify the key input pixels, without assessing the efficiency of these features in decision-making process, posing a significant hurdle in evaluating the model overinterpretation. In this paper, we propose necessity-sufficiency index (NSI) to select models’ decision-making basis among all the key regions identified by multiple interpretability methods and segmentation algorithm. Furthermore, we propose weighted composition ratio statistic (WCRS) method to quantitatively analyze the model overinterpretation by incorporating the NSI as weighted average weights. The experimental results indicate that our methods are capable of accurately identifying the decision-making features and quantitatively analyzing the models’ tendency towards overinterpretation.

Most of the conventional ship detection methods based on synthetic aperture radar (SAR) intends to process the focused images, which does not take full advantages of the intermediate data in the SAR imaging process. In this paper, we introduce a new framework that treats a two-dimensional point target as multiple one-dimensional sequences in the range-compressed domain, and then employs a Long Short-Term Memory (LSTM)-based network to perform the ship detection, thus reducing the computational burden and improving efficiency significantly. To validate the effectiveness of our proposed method, we conduct experiments on real SAR data. The results demonstrate the superiority of our framework in ship detection tasks.

Targeted sentiment analysis investigates the sentiment polarities on given target mentions from input texts. Different from sentence level sentiment, it offers more fine-grained knowledge on each entity mention. While early work leveraged syntactic information, recent research has used neural representation learning to induce features automatically, thereby avoiding error propagation of syntactic parsers, which are particularly severe on social media texts. We study a method to leverage syntactic information without explicitly building the parser outputs, by training an encoder-decoder structure parser model on standard syntactic treebanks, and then leveraging its hidden encoder layers when analysing tweets. Such hidden vectors do not contain explicit syntactic outputs, yet encode rich syntactic features. We use them to augment the inputs to a baseline state-of-the-art targeted sentiment classifier, observing significant improvements on various benchmark datasets. We obtain the best accuracies on all test sets.