Stars Are All You Need: A Distantly Supervised Pyramid Network for Unified Sentiment Analysis

Data for the Rating Prediction (RP) sentiment analysis task such as star reviews are readily available. However, data for aspect-category detection (ACD) and aspect-category sentiment analysis (ACSA) is often desired because of the fine-grained nature but are expensive to collect. In this work, we propose Unified Sentiment Analysis (Uni-SA) to understand aspect and review sentiment in a unified manner. Specifically, we propose a Distantly Supervised Pyramid Network (DSPN) to efficiently perform ACD, ACSA, and RP using only RP labels for training. We evaluate DSPN on multi-aspect review datasets in English and Chinese and find that in addition to the internal efficiency of sample size, DSPN also performs comparably well to a variety of benchmark models. We also demonstrate the interpretability of DSPN's outputs on reviews to show the pyramid structure inherent in unified sentiment analysis.Comment: 15 pages, 3 figures, 5 table

ROME: Testing Image Captioning Systems via Recursive Object Melting

Image captioning (IC) systems aim to generate a text description of the salient objects in an image. In recent years, IC systems have been increasingly integrated into our daily lives, such as assistance for visually-impaired people and description generation in Microsoft Powerpoint. However, even the cutting-edge IC systems (e.g., Microsoft Azure Cognitive Services) and algorithms (e.g., OFA) could produce erroneous captions, leading to incorrect captioning of important objects, misunderstanding, and threats to personal safety. The existing testing approaches either fail to handle the complex form of IC system output (i.e., sentences in natural language) or generate unnatural images as test cases. To address these problems, we introduce Recursive Object MElting (Rome), a novel metamorphic testing approach for validating IC systems. Different from existing approaches that generate test cases by inserting objects, which easily make the generated images unnatural, Rome melts (i.e., remove and inpaint) objects. Rome assumes that the object set in the caption of an image includes the object set in the caption of a generated image after object melting. Given an image, Rome can recursively remove its objects to generate different pairs of images. We use Rome to test one widely-adopted image captioning API and four state-of-the-art (SOTA) algorithms. The results show that the test cases generated by Rome look much more natural than the SOTA IC testing approach and they achieve comparable naturalness to the original images. Meanwhile, by generating test pairs using 226 seed images, Rome reports a total of 9,121 erroneous issues with high precision (86.47%-92.17%). In addition, we further utilize the test cases generated by Rome to retrain the Oscar, which improves its performance across multiple evaluation metrics.Comment: Accepted by ISSTA 202

Privacy-Aware UAV Flights through Self-Configuring Motion Planning

During flights, an unmanned aerial vehicle (UAV) may not be allowed to move across certain areas due to soft constraints such as privacy restrictions. Current methods on self-adaption focus mostly on motion planning such that the trajectory does not trespass predetermined restricted areas. When the environment is cluttered with uncertain obstacles, however, these motion planning algorithms are not flexible enough to find a trajectory that satisfies additional privacy-preserving requirements within a tight time budget during the flights. In this paper, we propose a privacy risk aware motion planning method through the reconfiguration of privacy-sensitive sensors. It minimises environmental impact by re-configuring the sensor during flight, while still guaranteeing the hard safety and energy constraints such as collision avoidance and timeliness. First, we formulate a model for assessing privacy risks of dynamically detected restricted areas. In case the UAV cannot find a feasible solution to satisfy both hard and soft constraints from the current configuration, our decision making method can then produce an optimal reconfiguration of the privacy-sensitive sensor with a more efficient trajectory. We evaluate the proposal through various simulations with different settings in a virtual environment and also validate the approach through real test flights on DJI Matrice 100 UAV

A 2030 United States Macro Grid Unlocking Geographical Diversity to Accomplish Clean Energy Goals

Some U.S. states have set clean energy goals and targets in an effort to decarbonize their electricity sectors. There are many reasons for such goals and targets, including the increasingly apparent effects of climate change. A handful of states (Washington, California, New York, and Virginia) are aiming for deep decarbonization by 2050 or earlier, a mere 30 years or less from today. The urgency of substantial carbon emissions reduction (50% or more by 2030) needed to avoid catastrophic climate impacts requires even more ambitious efforts than some of the original targets (e.g., a 30% renewable portfolio standard) set for between now and 2030. With the cost of solar and wind energy falling faster than expected in recent years, economics are also driving rapid expansion of clean energy investments. With this in mind, this report examines combinations of interregional AC and High-Voltage DC (HVDC) transmission upgrades and additions to evaluate the benefits of large-scale transmission expansion

NASGEM: Neural Architecture Search via Graph Embedding Method

Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architectures and their performance to enable scalable and flexible search. However, existing estimator-based methods encode the architecture into a latent space without considering graph similarity. Ignoring graph similarity in node-based search space may induce a large inconsistency between similar graphs and their distance in the continuous encoding space, leading to inaccurate encoding representation and/or reduced representation capacity that can yield sub-optimal search results. To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method. NASGEM is driven by a novel graph embedding method equipped with similarity measures to capture the graph topology information. By precisely estimating the graph distance and using an auxiliary Weisfeiler-Lehman kernel to guide the encoding, NASGEM can utilize additional structural information to get more accurate graph representation to improve the search efficiency. GEMNet, a set of networks discovered by NASGEM, consistently outperforms networks crafted by existing search methods in classification tasks, i.e., with 0.4%-3.6% higher accuracy while having 11%- 21% fewer Multiply-Accumulates. We further transfer GEMNet for COCO object detection. In both one-stage and twostage detectors, our GEMNet surpasses its manually-crafted and automatically-searched counterparts