Warning: This paper may contain triggering language for some readers, especially survivors of sexual violence. Survivors of sexual violence sometimes share their experiences on social media, revealing their feelings and emotions and seeking advice. On platforms such as Reddit, some stories can be long---up to 40,000 characters. We posit that such long stories are demanding for helpers to read and respond to. Prior research has indicated that parts of these stories describing the incident, the effects on the poster, and advice requested by the poster are important. Highlighting those parts can draw helpers' attention toward key information and assist them in reading and responding to long stories. We first examine the stories posted on Reddit for the prevalence of these parts. Second, we develop a computational model to highlight these parts of a story. On ten-fold cross-validation of a dataset, our model achieves a macro F1 score of 0.82. In addition, we contribute METHREE, a dataset comprising 8,947 labeled sentences for these parts from Reddit stories. A survey of users who are helpers on some relevant subreddits shows that the parts highlighted by our tool represent important information and assist them while reading and responding to long stories. We find that these tool-generated highlights statistically significantly reduce the demandingness of long stories. Moreover, almost all helpers felt that highlighted stories are helpful and easier to read, understand, and respond to than nonhighlighted ones. In particular, on a 4-point Likert scale, there is about 0.7 point reduction in demandingess when stories were presented with highlights.

Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual annotation for accurate labeling is time-consuming and labor-intensive. To address these challenges, we propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation. Our approach produces a multidomain benchmark comprising 4,498 topical claims and 30,961 arguments from three sources, spanning 21 domains. We benchmark the dataset in fully supervised, zero-shot, and few-shot settings, shedding light on the strengths and limitations of different methodologies.

Moral reasoning reflects how people acquire and apply moral rules in particular situations. With social interactions increasingly happening online, social media provides an unprecedented opportunity to assess in-the-wild moral reasoning. We investigate the commonsense aspects of morality empirically using data from a Reddit subcommunity (i.e., a subreddit), r/AmITheAsshole, where an author describes their behavior in a situation and seeks comments about whether that behavior was appropriate. A commenter judges and provides reasons for whether an author or others’ behaviors were wrong. We focus on the novel problem of understanding the moral reasoning implicit in user comments about the propriety of an author’s behavior. Specifically, we explore associations between the common elements of the indicated rationale and the extractable social factors. Our results suggest that a moral response depends on the author’s gender and the topic of a post. Typical situations and behaviors include expressing anger emotion and using sensible words (e.g., f-ck, hell, and damn) in work-related situations. Moreover, we find that commonly expressed reasons also depend on commenters’ interests.

This paper presents a 96.5% peak efficiency adaptive ON-time controlled buck converter with 0.001-500 mA load range for internet of things. A novel positive feedback controlled comparator is presented, with its bias current dynamically increased to accelerate the transient responses while maintaining low power consumption. Besides, the power gating scheme and a sub-nA voltage reference is implemented to achieve ultra-low overall quiescent current, leading to an outstanding conversion efficiency. Designed and simulated in a 0.18μm CMOS process, the prototype shows an undershoot voltage of 43 mV and recovery time of 2.8 µs during a load transient from 10 μA to 500 mA, with a total quiescent current of 60 nA. The proposed design shows efficiencies of 95.4%, 96.5% and 91.2% when the load current is 10 μA, 100 μA, and 500 mA, respectively.

This article presents a novel Dickson hybrid boost converter for thermoelectric energy harvesting that utilizes a fixed ratio switched-capacitor (SC) converter combined with a traditional switched-inductor design. This unique approach makes high conversion ratio (CR) more attainable. Besides, the converter features an on-chip LC oscillator for cold-starting the harvester without requiring any external components. To improve the efficiency of the harvester, a lookup table-based maximum power point tracking (MPPT) and a zero current detection (ZCD) circuit are also implemented. The prototype was designed and fabricated using the 55 nm low power (LP) CMOS process, with a compact 5.6-$\mu $H inductor. Measurement results verify that the harvester can cold start at an open-circuit voltage ofV$_{\mathbf{TG}}$$=$110mV, achieve a peak efficiency of 90.5% atV$_{\mathbf{TG}}$$=$140mV, and maintain operation at a 5-mV input voltage with a 200-nA load. Furthermore, the converter maintains end-to-end efficiencies above 80% for a wide range ofV$_{\mathbf{TG}}$$=$40$\sim$200mV. With a wide input range of 5$\sim$100mV and load range of 200nA$\sim$4.52mA, the proposed converter represents a significant improvement in performance over previous state-of-the-art designs, with a decent power efficiency, compact form factor and wide output power range.

Psychological studies on morality have proposed underlying linguistic and semantic factors. However, current empirical studies often lack the nuances and complexity of real life. This article examines how well the findings of prior studies generalize to a corpus of over 30000 narratives of tense social situations submitted to a popular social media forum. A poster describes interpersonal moral situations or misgivings; other users judge from the post whether the poster (protagonist) or an opposing side (antagonist) is morally culpable. We extend and apply natural language processing (NLP) techniques to understand the effects of descriptions of the people involved in these posts. We conduct extensive experiments to investigate the effect sizes of features to understand how they affect the assignment of blame on social media. Our findings show that aggregating psychological theories enables understanding real-life moral situations. We also find evidence of bias blame assignment on social media, such as that males are likelier to receive blame no matter whether they are protagonists or antagonists.

Test2SQL, a natural language interface to database querying, has seen considerable improvement, in part due to advances in deep learning. However, despite recent improvement, existing Text2SQL proposals allow only input in the form of complete questions. This leaves behind users who struggle to formulate complete questions, e.g., because they lack database expertise or are unfamiliar with the underlying database schema. To address this shortcoming, we study the novel problem of Text2SQL Auto-Completion (TSAC) that extends Text2SQL to also take partial or incomplete questions as input. Specifically, the TSAC problem is to predict the complete, executable SQL query. To solve the problem, we propose a novel Relation-aware Historical Bridging Network (RHB-Net) that consists of a relation-aware union encoder and an extraction-generation sensitive decoder. RHB-Net models relations between questions and database schemas and predicts the ambiguous intents expressed in partial queries. We also propose two optimization strategies: historical query bridging that fuses historical database queries, and a dynamic context construction that prevents repeated generation of the same SQL elements. Extensive experiments with real-world data offer evidence that RHB-Net is capable of outperforming baseline algorithms.

In global navigation satellite system reflectometry (GNSS-R) applications, water level variations could be determined by analyzing one of three types of data: signal-to-noise ratios (SNRs), combinations of carrier-phase observations and combinations of pseudorange and carrier-phase observations. In this study, a new GNSS-R method based on the analysis of a posteriori residual of undifferenced and uncombined (UC) Precise Point Positioning (PPP) and ionosphere free (IF) PPP was proposed. Double peaks were found in the Lomb-Scargle periodogram (LSP) estimates of dual-frequency carrier-phase residuals generated with UC PPP, the pseudorange and carrier-phase residuals generated with IF PPP and the detrended S2 time series. To solve this problem, we present a strict quality control strategy to screen the correct retrievals. Two experimental datasets from a dam and a bridge-deformation monitoring system, respectively, were used to evaluate the performance of this new method in water-level retrieval. The results show that the water level retrievals from the pseudorange and carrier-phase residuals generated by the UC PPP and IF PPP positioning method show a good agreement with the in-situ references, with correlation coefficient exceeding 0.95 and 0.97 in the two experiments, which is comparable to the SNR method. The retrievals showed a root mean square error (RMSE) of 6-7cm in the reservoir experiment and 13-16cm in the Ganjiang river experiment. Meanwhile, the inter-frequency bias for about 0.2m was found in the Xilongchi reservoir water level retrieval experiment, however, it is not the case for the Ganjiang River water level retrieval experiment. The new method enables the simultaneous determination of displacements and water-level variation and is thus applicable for in-situ displacement monitoring of civil engineering structures, such as dam, bridge etc.

Online car-hailing services have gained substantial popularity. An effective taxi fleet management strategy should not only increase taxi utilization by reducing taxi idle time, but should also improve passenger satisfaction by minimizing passenger waiting time. We demonstrate a fleet management system called SOUP that aims at minimizing taxi idle time and that monitors the fleet movement status. SOUP includes a passenger request prediction model called ST-GCSL that predicts the number of requests in the near future, and it includes a demand-aware route planning algorithm called DROP that provides idle taxis with search routes to serve potential requests. In addition, SOUP supports visualizing and analyzing historical passenger requests, simulating fleet movement, and computing evaluation metrics. We demonstrate how SOUP accurately predicts passenger demand and significantly reduces taxi idle time.