Miracle-dz/UniFAR-FTUs huggingface dataset:
What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts

Reading a visualization is like reading a paragraph. Each sentence is a comparison: the mean of these is higher than those; this difference is smaller than that. What determines which comparisons are made first? The viewer's goals and expertise matter, but the way that values are visually grouped together within the chart also impacts those comparisons. Research from psychology suggests that comparisons involve multiple steps. First, the viewer divides the visualization into a set of units. This might include a single bar or a grouped set of bars. Then the viewer selects and compares two of these units, perhaps noting that one pair of bars is longer than another. Viewers might take an additional third step and perform a second-order comparison, perhaps determining that the difference between one pair of bars is greater than the difference between another pair. We create a visual comparison taxonomy that allows us to develop and test a sequence of hypotheses about which comparisons people are more likely to make when reading a visualization. We find that people tend to compare two groups before comparing two individual bars and that second-order comparisons are rare. Visual cues like spatial proximity and color can influence which elements are grouped together and selected for comparison, with spatial proximity being a stronger grouping cue. Interestingly, once the viewer grouped together and compared a set of bars, regardless of whether the group is formed by spatial proximity or color similarity, they no longer consider other possible groupings in their comparisons.
Collective group drift in a PDE-based opinion dynamics model with biased perception kernels

In the age of technology, individuals accelerate their biased gathering of information which in turn leads to a population becoming extreme and more polarized. Here we study a partial differential equation model for opinion dynamics that exhibits collective behavior subject to nonlocal interactions. We developed a new interaction kernel function to represent biased information gathering. Through a linear stability analysis, we show that biased populations can still form opinionated groups. However, a population that is too heavily biased can no longer come to a consensus, that is, the initial homogeneous mixed state becomes stable. Numerical simulations with biased information gathering show the ability for groups to collectively drift towards one end of the opinion space. This means that a small bias in each individual will collectively lead to groups of individuals becoming extreme together. The characteristic time scale for a groups existence is captured from numerical experiments using the temporal correlation function. Supplementing this, we included a measure of how different each population is after regular time intervals using a form of the Manhattan and Euclidean distance metrics. We conclude by exploring how wall boundary conditions induce pattern formation initially on the most extreme sides of the domain.
Considering the Single and Binary Origins of the Type IIP SN 2017eaw

Current population synthesis modeling suggests that 30%–50% of Type II supernovae originate from binary progenitors; however, the identification of a binary progenitor is challenging. One indicator of a binary progenitor is that the surrounding stellar population is too old to contain a massive single star. Measurements of the progenitor mass of SN 2017eaw are starkly divided between observations made temporally close to core collapse, which show a progenitor mass of 13–15 M ⊙ (final helium-core mass MHe,core=4.4–6.0M⊙ —which is a more informative property than initial mass) and those from the stellar population surrounding the SN, which find M ≤ 10.8 M ⊙ ( MHe,core≤3.4M⊙ ). In this paper, we reanalyze the surrounding stellar population with improved astrometry and photometry, finding a median age of 16.8−1.0+3.2 Myr for all stars younger than 50 Myr ( MHe,core=4.7M⊙ ) and 85.9−6.5+3.2 Myr for stars younger than 150 Myr. 16.8 Myr is now consistent with the helium-core mass range derived from the temporally near-explosion observations for single stars. Applying the combined constraints to population synthesis models, we determine that the probability of the progenitor of SN 2017eaw being an initially single star is 65% compared to 35% for prior binary interaction. 85.9 Myr is inconsistent with any formation scenarios. We demonstrate that combining progenitor age constraints with helium-core mass estimates from red supergiant SED modeling, late-time spectra, and indirectly from light-curve modeling can help to differentiate single and binary progenitor scenarios and provide a framework for the application of this technique to future observations.
Assessing the Benefits of Reducing Fire Risk in the Wildland- Urban Interface: A Contingent Valuation Approach

Wildland-urban interface (WUI) residents in Michigan were interviewed using a contingent valuation protocol to assess their willingness-to-pay (WTP) for incremental reductions in the risk of losing their homes to wild-fire. WTP was elicited using a probability model which segments the risk of structure loss into “public” and “private” components. Most respondents expressed positive WTP for publicly funded risk reduction activities. These respondents were characterized by tolerance for property taxes, perception of significant risk, high ranking of fire risk relative to other hazards, and high objective estimates of existing risk, and their WTP amounts were positively correlated with income and property value. Given that 97% of the respondents were insured against property loss, the large number of positive WTP responses suggests that substantial non-market and unreimbursed losses are experienced when structures are destroyed by wildfires.
Fate of the nitrification inhibitor dicyandiamide (DCD) sprayed on a grazed pasture: effect of rate and time of application

The objectives of this study were to improve our understanding of seasonal variation in the biophysical disappearance of the nitrification inhibitor dicyandiamide (DCD) in soil and the key regulatory factors. Changes in DCD concentrations in the soil and plant canopy were measured following application to dairy-grazed pasture soil and non-grazed pasture soil. Treatments included two levels of DCD alone (10 and 20 kg ha–1) applied to non-grazed pasture field plots, and DCD (10 kg ha–1) applied with urine and with urea fertiliser. DCD (10 kg ha–1) was also applied in grazed farmlets following grazing. About 4–40% of the DCD applied was intercepted and stayed on the plant canopy from <6 and up to 16 days, depending on the subsequent timing and intensity of rainfall. In this poorly drained soil, <10% of applied DCD leached below 10 cm depth. Neither the level of DCD nor the N source had any significant effect on the half-life of DCD in soil. The half-life of DCD did vary with season, ranging from 7 to 13 days in March to November respectively, and showed a linear decrease with observed increase in soil temperature between 10.7 and 16.5°C. The results suggest that to maintain an optimum effective DCD concentration in soil, different DCD application rates and frequency may be required in different seasons.
Leveraging Ensemble Techniques and Metadata for Subjective Knowledge-grounded Conversational Systems

The goal of DSTC11 track 5 is to build task-oriented dialogue systems that can effectively utilize external knowledge sources such as FAQs and reviews. This year’s challenge differs from previous ones as it includes subjective knowledge snippets and requires multiple snippets for a single turn. We propose a pipeline system for the challenge focusing on entity tracking, knowledge selection and response generation. Specifically, we devise a novel heuristic to ensemble the outputs from the rule-based method and neural model for entity tracking and knowledge selection. We also leverage metadata information in the knowledge source to handle fine-grained user queries. Our approach achieved the first place in objective evaluation and the third place in human evaluation of DSTC11 track 5.
Keeping an Eye on Context: Attention Allocation over Input Partitions in Referring Expression Generation

In Referring Expression Generation, model inputs are often composed of different representations, including the visual properties of the intended referent, its relative position and size, and the visual context. Yet, the extent to which this information influences the generation process of black-box neural models is largely unclear. We investigate the relative weighting of target, location, and context information in the attention components of a Transformer-based generation model. Our results show a general target bias, which, however, depends on the content of the generated expressions, pointing to interesting directions for future research.
EchoNose: Sensing Mouth, Breathing and Tongue Gestures inside Oral Cavity using a Non-contact Nose Interface

Sensing movements and gestures inside the oral cavity has been a long-standing challenge for the wearable research community. This paper introduces EchoNose, a novel nose interface that explores a unique sensing approach to recognize gestures related to mouth, breathing, and tongue by analyzing the acoustic signal reflections inside the nasal and oral cavities. The interface incorporates a speaker and a microphone placed at the nostrils, emitting inaudible acoustic signals and capturing the corresponding reflections. These received signals were processed using a customized data processing and machine learning pipeline, enabling the distinction of 16 gestures involving speech, tongue, and breathing. A user study with 10 participants demonstrates that EchoNose achieves an average accuracy of 93.7% in recognizing these 16 gestures. Based on these promising results, we discuss the potential opportunities and challenges associated with applying this innovative nose interface in various future applications.
Team Iterate @ AutoMin 2023 - Experiments with Iterative Minuting

This report describes the development of our system for automatic minuting created for the AutoMin 2023 Task A. As a baseline, we utilize a system based on the BART encoder-decoder model paired with a preprocessing pipeline similar to the one introduced by the winning solutions at AutoMin 2021. We then further explore the possibilities for iterative summarization by constructing an iterative minuting dataset from the provided data, finetuning on it and feeding the model previously generated minutes. We also experiment with adding more context by utilizing the Longformer encoder-decoder model and finetuning it on the SAMSum dataset. Our submitted solution is of the baseline approach, since we were unable to match its performance with our iterative variants. With the baseline, we achieve a ROUGE-1 score of 0.368 on the ELITR minuting corpus development set. We finally explore the performance of Vicuna 13B quantized language model for summarization.
CopyT5: Copy Mechanism and Post-Trained T5 for Speech-Aware Dialogue State Tracking System

In a real-world environment, Dialogue State Tracking (DST) should use speech recognition results to perform tasks. However, most existing DST research has been conducted in text-based environments. This study aims to build a model that efficiently performs Automatic Speech Recognition-based DST. To operate robustly against speech noise, we used CopyT5, which adopted a copy mechanism, and trained the model using augmented data including speech noise. Furthermore, CopyT5 performed post-training using the masked language modeling method with the MultiWOZ dataset in T5 in order to learn the dialogue context better. The copy mechanism also mitigated name entity errors that may occur during DST generation. Experiments confirmed that data augmentation, post-training, and the copy mechanism effectively improve DST performance.
Retrieval of vertical profiles of ozone from high‐resolution infrared solar spectra at Rikubetsu, Japan

Vertical profiles of volume mixing ratios (VMRs) of O 3 were retrieved from high-resolution, infrared solar absorption spectra taken at Rikubetsu, Japan (43.5°N), by a ground-based Fourier transform infrared spectrometer (FTIR). The VMR profile retrieval is based on an iterative inversion algorithm, which utilizes line-by-line, layer-by-layer calculations. Two different microwindow regions (1146 and 1163 cm -1 ) were used for the retrieval calculations. Eighty ozone vertical profiles were retrieved from the two spectral regions from 40 spectra and were compared with 17 ozonesonde measurements from a nearby station. Average differences between 18 and 34 km were less than ±12% for both 1146 and 1163 cm -1 retrievals. The standard deviation of the average differences between 18 and 34 km were less than 19% and 14% for respective windows. Effects of several different factors on errors of profile retrievals, such as initial guess, temperature profiles, and instrument line shape functions, were evaluated by numerical simulation. The estimated errors were extremely consistent with the observed errors between 18 and 34 km, suggesting that the FTIR was optically well adjusted. In general, retrieval of the O 3 profile gives VMRs in an accuracy better than 40% at 14 km, improving to 10-15% between 18 and 30 km, with precision of 30% and 20% for these altitude ranges, respectively.
Learning Multimodal Cues of Children’s Uncertainty

Understanding uncertainty plays a critical role in achieving common ground (Clark et al., 1983). This is especially important for multimodal AI systems that collaborate with users to solve a problem or guide the user through a challenging concept. In this work, for the first time, we present a dataset annotated in collaboration with developmental and cognitive psychologists for the purpose of studying nonverbal cues of uncertainty. We then present an analysis of the data, studying different roles of uncertainty and its relationship with task difficulty and performance. Lastly, we present a multimodal machine learning model that can predict uncertainty given a real-time video clip of a participant, which we find improves upon a baseline multimodal transformer model. This work informs research on cognitive coordination between human-human and human-AI and has broad implications for gesture understanding and generation. The anonymized version of our data and code will be publicly available upon the completion of the required consent forms and data sheets.
EdgeTorrent: Real-time Temporal Graph Representations for Intrusion Detection

Anomaly-based intrusion detection aims to learn the normal behaviors of a system and detect activity that deviates from it. One of the best ways to represent the behavior of a computer network is through provenance graphs: dynamic networks of entity interactions over time. When provenance graphs deviate from their normal behaviors, it could be indicative of a malicious actor attempting to compromise the network. However, efficiently characterizing the normal behavior of large temporal graphs is challenging. To do this, we propose EdgeTorrent, an end-to-end anomaly-based intrusion detection system for provenance graph analysis. EdgeTorrent leverages a novel high-performance message passing neural network for graph embedding over a stream of edges to capture both temporal and topological changes in the system. These embeddings are then processed by a novel adversarially trained sequence analyzer that alerts when a series of graph embeddings changes in an unexpected way. EdgeTorrent preserves temporal ordering during message passing, and its streaming-focused design allows users to conduct out-of-core inference on billion-edge graphs, faster than real-time. We show that our method outperforms state-of-the-art graph-kernel approaches on several host monitoring data sets; notably, it is the first intrusion detection system to perfectly classify the StreamSpot data set. Additionally, we show it is the best-performing method on a real-world, billion-edge data set encompassing 11 days of benign and attack data.
Construction of Traditional Culture Ontology Based on Representation and Role

Traditional culture refers to a culture that has evolved from civilization and can reflect the characteristics and spirit of a nation. However, at present, the traditional cultural ontology only focuses on modeling and data organization of a certain type of traditional culture, which is a certain distance from the massive cultural information and urgent cultural needs. This article starts from the perspective of protecting and inheriting traditional culture, and based on representation, incorporates role theory to construct a cultural ontology centered on poetry, calligraphy, and painting. The traditional cultural ontology constructed in this article can further contribute to the field of artificial intelligence.
Towards a Co-creative System for Creating, Suggesting, and Assessing Material Textures for 3D Renderings During Design Reviews in Industrial Design

Material selection is an important task in industrial design as they affect several aspects of a product including its sensory characteristics and feasibility. This paper presents an early iteration of a co-creative system that uses generative AI to change, suggest, and provide feedback on a 3D rendering’s materials. The system is aimed to be used during design review sessions to assist designers in quickly exploring alternatives, and converging on suitable materials before construction. We first interviewed industrial designers on how they assess materials in various deliverables (e.g., sketches, and 3D renderings) during design review sessions. Based on our findings, we then develop a prototype of the co-creative system for generating, suggesting, and providing feedback on a 3D rendering’s material textures. We believe that using this system can assist designers in not only creating textures for their 3D renderings but also in providing material-aware feedback to create the product feasibly.
The impact of overall light-level on the reverse Pulfrich effect

The Pulfrich Effect is an illusion characterized by the misperception of the depth and 3D direction of moving objects. Interocular luminance differences cause the Classic Pulfrich effect; the darker image is processed more slowly. Interocular blur differences cause the Reverse Pulfrich effect; the blurrier image is processed more quickly. A common correction for presbyopia—monovision—intentionally induces the optical conditions that cause the Reverse Pulfrich Effect. The effect sizes, and the fact that tens of millions of people wear these corrections every day, raise concerns about public safety. However, although the impact of overall light-level (e.g., nighttime vs. daytime) on the Classic Pulfrich effect has been well-characterized, its impact on the Reverse Pulfrich effect is unknown. Here, using a custom binocular 4f tunable lens optical system that allows the decoupling of retinal illuminance and retinal blur, we report how the Classic and Reverse Pulfrich effects scale with overall light-level. Both effects increase logarithmically with decreases in light-level. These results motivate a characterization of how light level interacts with other optical factors (e.g., higher-order aberrations) that are likely to impact the Reverse Pulfrich effect, and hence the perceptual consequences of monovision corrections. Commercial disclosure V. Rodriguez Lopez: None; B. Chin: None; J. Burge: None.
Security Analysis of the 3MF Data Format

3D printing is a well-established technology with rapidly increasing usage scenarios both in the industry and consumer context. The growing popularity of 3D printing has also attracted security researchers, who have analyzed possibilities for weakening 3D models or stealing intellectual property from 3D models. We extend these important aspects and provide the first comprehensive security analysis of 3D printing data formats. We performed our systematic study on the example of the 3D Manufacturing Format (3MF), which offers a large variety of features that could lead to critical attacks. Based on 3MF’s features, we systematized three attack goals: Data Exfiltration (dex), Denial of Service, and UI Spoofing (uis). We achieve these goals by exploiting the complexity of 3MF, which is based on the Open Packaging Conventions (OPC) format and uses XML to define 3D models. In total, our analysis led to 352 tests. To create and run these tests automatically, we implemented an open-source tool named 3MF Analyzer (tool), which helped us evaluate 20 applications.
Partial Failure Resilient Memory Management System for (CXL-based) Distributed Shared Memory

The efficiency of distributed shared memory (DSM) has been greatly improved by recent hardware technologies. But, the difficulty of distributed memory management can still be a major obstacle to the democratization of DSM, especially when a partial failure of the participating clients (e.g., due to crashed processes or machines) should be tolerated. In this paper, we present CXL-SHM, an automatic distributed memory management system based on reference counting. The reference count maintenance in CXL-SHM is implemented with a special era-based non-blocking algorithm. Thus, there are no blocking synchronization, memory leak, double free, and wild pointer problems, even if some participating clients unexpectedly fail without freeing their possessed memory references. We evaluated our system on real CXL hardware with both micro-benchmarks and end-to-end applications, which demonstrate the efficiency of CXL-SHM and the simplicity/flexibility of using CXL-SHM to build efficient distributed applications.
The Effects of User Interface Design for Mobile Learning Application on Learner's Extraneous Cognitive Load: A Conceptual Framework

In today's fast-paced society, when learning can take place anytime and anywhere, mobile learning is extremely relevant. Nonetheless, despite these significant benefits, there are numerous problems that could slow down the learning process. Due to the small screen size and user interface that are inappropriately designed for mobile devices, learners are experiencing an increase in their cognitive load. This study examines the relationships between mobile learning user interface design and their effects on learners' cognitive load, specifically the extraneous load. First, the mobile learning user interface design criteria that affect the learner's cognitive load are identified. Next, a study on cognitive load measurement is implemented to further understand the type, method and criteria of cognitive load. Both of these findings are important in understanding the relationship between the mobile learning user interface design and learner's cognitive load. This study presented a conceptual framework for the effects of user interface design for mobile learning application on learner's extraneous cognitive load. This conceptual framework will be the foundation of a user interface design framework that aims at improving learner's cognitive load when implementing mobile learning apps. This framework will offer guidance for those interested in designing mobile learning applications, that give learners an effective knowledge and mobile learning experience, hence improving the success of the mobile learning process.