Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Visualization Insights through Entity-Based Interaction: An Exploratory Study

One of the primary purposes of visualization is to assist users in discovering insights. While there has been much research in information visualization aiming at complex data transformation and novel presentation techniques, relatively little has been done to understand how users derive insights through interactive visualization of data. This paper presents a crowdsourced study with 158 participants investigating the relation between entity-based interaction (an action + its target entity) and the resulting insight. To this end, we generalized the interaction with an existing CO2 Explorer as entity-based interaction and enabled users to input notes and refer to relevant entities to assist their narratives. We logged interactions of users freely exploring the visualization and characterized their externalized insights about the data. Using entity-based interactions and references to infer insight characteristics (category, overview versus detail, and prior knowledge), we found evidence that compared with interactions, entity references improved insight characterization from slight/fair to fair/moderate agreements. To interpret prediction outcomes, feature importance and correlation analysis indicated that, e.g., detailed insights tended to have more mouse-overs in the chart area and cite the vertical reference lines in the line chart as evidence. We discuss study limitations and implications on knowledge-assisted visualization, e.g., insight recommendations based on user exploration.
In-plane dynamic crushing and energy absorption capacity of self-similar hierarchical honeycombs

The in-plane dynamic crushing behavior and energy absorption capacity of self-similar hierarchical honeycombs under different impact velocities are numerically studied using ANSYS/LS-DYNA. First, the hierarchical honeycomb models with uniform cell-wall thickness are constructed by replacing every three-edge structure nodes of a regular honeycomb with smaller self-similar hexagons of the same orientation. The respective influences of hierarchical parameters, bulk materials, and impact velocities on the macro-/micro-deformation behaviors, the dynamic strength, and the specific absorbed energy of hierarchical honeycombs are explored in detail. The results show that the crushing strengths and energy-absorbing capacities of honeycombs significantly improve when adding the hierarchy into conventional cellular structures. The variation of hierarchical parameter changes the local dynamic evolution of stress waves, which further results in different macro-/micro-deformation properties. Through the proper choice of hierarchical parameters and bulk materials, the optimal crushing strength and the maximum absorbing energy could be obtained.
LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing

This paper addressed the problem of structured sentiment analysis using a bi-affine semantic dependency parser, large pre-trained language models, and publicly available translation models. For the monolingual setup, we considered: (i) training on a single treebank, and (ii) relaxing the setup by training on treebanks coming from different languages that can be adequately processed by cross-lingual language models. For the zero-shot setup and a given target treebank, we relied on: (i) a word-level translation of available treebanks in other languages to get noisy, unlikely-grammatical, but annotated data (we release as much of it as licenses allow), and (ii) merging those translated treebanks to obtain training data. In the post-evaluation phase, we also trained cross-lingual models that simply merged all the English treebanks and did not use word-level translations, and yet obtained better results. According to the official results, we ranked 8th and 9th in the monolingual and cross-lingual setups.
Framework for disruptive AI/ML Innovation

The revenue forecast for 2022 for worldwide AI software is $62.5 billion (an increment of 21.3% compared to 2021); by 2030, recent market research estimates that AI will represent $13 trillion in value creation. Therefore, a framework for disruptive AI/ML Innovation (AMI) has never been more prominent. This framework enables C-suite executive leaders to deﬁne a business plan and manage a set of technological dependencies for building AI/ML Solutions. The business plan of this framework provides components and background information to deﬁne strategy and analyze cost; furthermore, the business plan represents the fundamentals of AMI and AI/ML Solutions. Therefore, the framework provides a menu for managing and investing in AI/ML. Finally, this framework is constructed with an interdisci-plinary and holistic view of AMI and builds on advances in business strategy in harmony with technological progress for AI/ML. This framework incorporates an organization’s value chains, supply chain, and ecosystem strategies. or to one or more factors speciﬁc to the physical, physiological, mental, cultural or social identity of that natural The an integral of rights and the and is for any organization that provides goods or services in the Every resident in the has the right to access, the right to rectiﬁcation, restrict processing, portability, and the right to object concerning their personal information. The GDPR impacts the way organizations need to perform data governance and system design. An initial step in data governance is to ensure all business activities are mapped with PII data: corresponding data ﬂows, data owners, compliance reviewers, and stakeholders.
A 3-Approximation Algorithm for a Particular Case of the Hamiltonian p-Median Problem

Given a weighted graph $G$ with $n$ vertices and $m$ edges, and a positive integer $p$, the Hamiltonian $p$-median problem consists in finding $p$ cycles of minimum total weight such that each vertex of $G$ is in exactly one cycle. We introduce an $O(n^6)$ 3-approximation algorithm for the particular case in which $p \leq \lceil \frac{n-2\lceil \frac{n}{5} \rceil}{3} \rceil$. An approximation ratio of 2 might be obtained depending on the number of components in the optimal 2-factor of $G$. We present computational experiments comparing the approximation algorithm to an exact algorithm from the literature. In practice much better ratios are obtained. For large values of $p$, the exact algorithm is outperformed by our approximation algorithm.
Faster online calibration without randomization: interval forecasts and the power of two choices

We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998), nature is often modeled as an adaptive adversary who sees all activity of the forecaster except the randomization that the forecaster may deploy. A number of papers have proposed randomized forecasting strategies that achieve an $\epsilon$-calibration error rate of $O(1/\sqrt{T})$, which we prove is tight in general. On the other hand, it is well known that it is not possible to be calibrated without randomization, or if nature also sees the forecaster's randomization; in both cases the calibration error could be $\Omega(1)$. Inspired by the equally seminal works on the"power of two choices"and imprecise probability theory, we study a small variant of the standard online calibration problem. The adversary gives the forecaster the option of making two nearby probabilistic forecasts, or equivalently an interval forecast of small width, and the endpoint closest to the revealed outcome is used to judge calibration. This power of two choices, or imprecise forecast, accords the forecaster with significant power -- we show that a faster $\epsilon$-calibration rate of $O(1/T)$ can be achieved even without deploying any randomization.
Expanding the Latent Space of StyleGAN for Real Face Editing

Recently, a surge of face editing techniques have been proposed to employ the pretrained StyleGAN for semantic manipulation. To successfully edit a real image, one must first convert the input image into StyleGAN's latent variables. However, it is still challenging to find latent variables, which have the capacity for preserving the appearance of the input subject (e.g., identity, lighting, hairstyles) as well as enabling meaningful manipulations. In this paper, we present a method to expand the latent space of StyleGAN with additional content features to break down the trade-off between low-distortion and high-editability. Specifically, we proposed a two-branch model, where the style branch first tackles the entanglement issue by the sparse manipulation of latent codes, and the content branch then mitigates the distortion issue by leveraging the content and appearance details from the input image. We confirm the effectiveness of our method using extensive qualitative and quantitative experiments on real face editing and reconstruction tasks.
Crowdsourcing Felt Reports Using the MyShake Smartphone App

MyShake is a free citizen science smartphone app that provides a range of features related to earthquakes. Features available globally include rapid postearthquake notifications, live maps of earthquake damage as reported by MyShake users, safety tips, and various educational features. The app also uses the accelerometer in the mobile device to detect earthquake shaking, and to record and submit waveforms to a central archive. In addition, MyShake delivers earthquake early warning alerts in California, Oregon, and Washington. In this study, we compare the felt shaking reports provided by MyShake users in California with the U.S. Geological Survey’s (USGSs) “Did You Feel It? ” intensity reports. The MyShake app simply asks, “What strength of shaking did you feel? ” and users report on a five-level scale. When the MyShake reports are averaged in spatial or time bins, we find strong correlation with the Modified Mercalli Intensity scale values reported by the USGS based on the DYFI surveys. The MyShake felt reports can therefore contribute to the creation of shaking intensity maps.
Cosmology from the kinetic polarized Sunyaev Zel'dovich effect

The cosmic microwave background (CMB) photons that scatter off free electrons in the large-scale structure induce a linear polarization pattern proportional to the remote CMB temperature quadrupole observed in the electrons' rest frame. The associated blackbody polarization anisotropies are known as the polarized Sunyaev Zel'dovich (pSZ) effect. Relativistic corrections to the remote quadrupole field give rise to a non-blackbody polarization anisotropy proportional to the square of the transverse peculiar velocity field; this is the kinetic polarized Sunyaev Zel'dovich (kpSZ) effect. In this paper, we forecast the ability of future CMB and galaxy surveys to detect the kpSZ effect, finding that a statistically significant detection is within the reach of planned experiments. We further introduce a quadratic estimator for the square of the peculiar velocity field based on a galaxy survey and CMB polarization. Finally, we outline how the kpSZ effect is a probe of cosmic birefringence and primordial non-Gaussianity, forecasting the reach of future experiments.
Personalized Driving Behaviors and Fuel Economy Over Realistic Commute Traffic: Modeling, Correlation, and Prediction

Drivers have distinctively diverse behaviors when operating vehicles in natural traffic flow, such as preferred pedal position, car-following distance, preview time headway, etc. These highly personalized behavioral variations are known to impact vehicle fuel economy qualitatively. Nevertheless, the quantitative relationship between driving behaviors and vehicle fuel consumption remains obscure. Addressing this critical missing link will contribute to the improvement of transportation sustainability, as well as understanding drivers’ behavioral diversity. This study proposed an integrated microscopic driver behavior and fuel consumption model to assess and predict vehicle fuel economy with naturalistic highway and local commuting traffic data. Through extensive Monte Carlo simulations, significant correlation results are revealed between specific individual driving preferences and fuel economy over drivers’ frequent commuting routes. Correlation results indicate that the differences in fuel consumption incurred by various driving behaviors, even in the same traffic conditions, can be as much as 29% for a light-duty truck and 15% for a passenger car. A Gaussian Process Regression model is further trained, validated, and tested under different traffic and vehicle conditions to predict fuel consumption based on drivers’ personalized behaviors. Such a quantitative and personalized model can be used to identify and recommend fuel-friendly driving behaviors and routes, demonstrating a strong incentive for relevant stakeholders.
Boundary Smoothing for Named Entity Recognition

Neural named entity recognition (NER) models may easily encounter the over-confidence issue, which degrades the performance and calibration. Inspired by label smoothing and driven by the ambiguity of boundary annotation in NER engineering, we propose boundary smoothing as a regularization technique for span-based neural NER models. It re-assigns entity probabilities from annotated spans to the surrounding ones. Built on a simple but strong baseline, our model achieves results better than or competitive with previous state-of-the-art systems on eight well-known NER benchmarks. Further empirical analysis suggests that boundary smoothing effectively mitigates over-confidence, improves model calibration, and brings flatter neural minima and more smoothed loss landscapes.
Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study

This paper compares the performances of three supervised machine learning algorithms in terms of predictive ability and model interpretation on structured or tabular data. The algorithms considered were scikit-learn implementations of extreme gradient boosting machines (XGB) and random forests (RFs), and feedforward neural networks (FFNNs) from TensorFlow. The paper is organized in a findings-based manner, with each section providing general conclusions supported by empirical results from simulation studies that cover a wide range of model complexity and correlation structures among predictors. We considered both continuous and binary responses of different sample sizes. bias
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing

Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. Recent studies typically represent the entire document by sequence- or graph-based models to predict the relations of all entity pairs. However, we find that such a model is not robust and exhibits bizarre behaviors: it predicts correctly when an entire test document is fed as input, but errs when non-evidence sentences are removed. To this end, we propose a Sentence Importance Estimation and Focusing (SIEF) framework for DocRE, where we design a sentence importance score and a sentence focusing loss, encouraging DocRE models to focus on evidence sentences. Experimental results on two domains show that our SIEF not only improves overall performance, but also makes DocRE models more robust. Moreover, SIEF is a general framework, shown to be effective when combined with a variety of base DocRE models.
Relevance-based Margin for Contrastively-trained Video Retrieval Models

Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries to web-scale video search. Learning the cross-similarity of video and text in a joint embedding space is the dominant approach. To do so, a contrastive loss is usually employed because it organizes the embedding space by putting similar items close and dissimilar items far. This framework leads to competitive recall rates, as they solely focus on the rank of the groundtruth items. Yet, assessing the quality of the ranking list is of utmost importance when considering intelligent retrieval systems, since multiple items may share similar semantics, hence a high relevance. Moreover, the aforementioned framework uses a fixed margin to separate similar and dissimilar items, treating all non-groundtruth items as equally irrelevant. In this paper we propose to use a variable margin: we argue that varying the margin used during training based on how much relevant an item is to a given query, i.e. a relevance-based margin, easily improves the quality of the ranking lists measured through nDCG and mAP. We demonstrate the advantages of our technique using different models on EPIC-Kitchens-100 and YouCook2. We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance. Finally, extensive ablation studies and qualitative analysis support the robustness of our approach. Code will be released at \urlhttps://github.com/aranciokov/RelevanceMargin-ICMR22.
Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This article presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described.
STRUCTURAL ANALYSIS OF THE DOUBLE BOTTOM STRUCTURE DURING SHIP GROUNDING BY FINITE ELEMENT APPROACH

CONCERN OF WATER-TRANSPORTATION MODE WHEN EXPERIENCES NUMEROUS ACCIDENTAL LOADS IS PREDICTED LIMITLESS AND ALWAYS DEMAND SUSTAINABLE ANALYSIS. IMPACT PHENOMENON RAISES AS MAIN REASON OF ENVIRONMENTAL DAMAGE, WHICH GROUNDING CONTRIBUTES IN FORM OF SHORT-TIME IMPACT AND PRODUCES MASSIVE DAMAGE ON BOTTOM STRUCTURE OF MARINE VESSELS, E.G. SHIP. IN THIS WORK, A SERIES OF NUMERICAL EXPERIMENT IS CONDUCTED WITH CONSIDERING CONTRIBUTION OF SEVERAL PARAMETERS, NAMELY IMPACT LOCATION AND SEABED TOPOLOGY ON THE TARGET OBJECT WHICH IS DETERMINED TO BE THE BOTTOM STRUCTURE OF A TANKER SHIP. THE INITIAL STAGE IS CONDUCTED TO OBTAIN VALIDATION OF NUMERICAL METHOD AND CONFIGURATION USING IMPACT TEST TO ENSURE RELIABILITY OF THE PRESENT METHODOLOGY. IN THE SECOND STAGE, PROPOSED SCENARIOS ARE CALCULATED AND OVERALL EVALUATION OF DAMAGE EXTENT IS PERFORMED TO OBSERVE RESISTANCE AND BEHAVIOUR OF THE BOTTOM STRUCTURE. STRUCTURAL CONDITION AFTER GROUNDING, RUPTURE ENERGY, AND CRUSHING FORCE ARE PRESENTED TO OBSERVE HISTORY OF GROUNDING. CONTRIBUTION OF NUMERICAL PARAMETER IN FORM OF MESHING SIZE IS PRESENTED TO PROVIDE ADEQUATE CONSIDERATION IN BOTH OF PHYSICAL AND NUMERICAL PARA-METERS. FINALLY, TENDENCY OF THE TARGET STRUCTURE IS SUMMARIZED TO PROVIDE PREDICTION OF FURTHER BEHAVIOUR DURING THE BOTTOM STRUCTURE EXPERIENCES GROUNDING WITH OTHER SCENARIO. AN ALTERNATIVE SOLUTION IN RESPECTING TIME COST IS PROPOSED AND CAN BE CONSIDERED FOR OTHER IMPACT SIMULATION.
The Host Galaxy of the Recoiling Black Hole Candidate in 3C 186: An Old Major Merger Remnant at the Center of a z = 1 Cluster

3C 186, a radio-loud quasar at z = 1.0685, was previously reported to have both velocity and spatial offsets from its host galaxy, and has been considered as a promising candidate for a gravitational wave recoiling black hole triggered by a black hole merger. Another possible scenario is that 3C 186 is in an ongoing galaxy merger, exhibiting a temporary displacement. In this study, we present analyses of new deep images from the Hubble Space Telescope WFC3-IR and Advanced Camera for Surveys, aiming to characterize the host galaxy and test this alternative scenario. We carefully measure the light-weighted center of the host and reveal a significant spatial offset from the quasar core (11.1 ± 0.1 kpc). The direction of the confirmed offset aligns almost perpendicularly to the radio jet. We do not find evidence of a recent merger, such as a young starburst in disturbed outskirts, but only marginal light concentration in F160W at ∼30 kpc. The host consists of mature (≳200 Myr) stellar populations and one compact star-forming region. We compare with hydrodynamical simulations and find that those observed features are consistently seen in late-stage merger remnants. Taken together, those pieces of evidence indicate that the system is not an ongoing/young merger remnant, suggesting that the recoiling black hole scenario is still a plausible explanation for the puzzling nature of 3C 186.
Bounds for Synchronizing Markov Decision Processes

We consider Markov decision processes with synchronizing objectives, which require that a probability mass of 1 − ε accumulates in a designated set of target states, either once, always, inﬁnitely often, or always from some point on, where ε = 0 for sure synchronizing, and ε → 0 for almost-sure and limit-sure synchronizing. We introduce two new qualitative modes of synchronizing, where the probability mass should be either positive , or bounded away from 0. They can be viewed as dual synchronizing objectives. We present al-gorithms and tight complexity results for the problem of deciding if a Markov decision process is positive, or bounded synchronizing, and we provide explicit bounds on ε in all synchronizing modes. In particular, we show that deciding positive and bounded synchronizing always from some point on, is coNP-complete.
Unify Local and Global Information for Top-N Recommendation

Knowledge graph (KG), integrating complex information and containing rich semantics, is widely considered as side information to enhance the recommendation systems. However, most of the existing KG-based methods concentrate on encoding the structural information in the graph, without utilizing the collaborative signals in user-item interaction data, which are important for understanding user preferences. Therefore, the representations learned by these models are insufficient for representing semantic information of users and items in the recommendation environment. The combination of both kinds of data provides a good chance to solve this problem, but it faces the following challenges: i) the inner correlations in user-item interaction data are difficult to capture from one side of the user or item; ii) capturing the knowledge associations on the whole KG would introduce noises and variously influence the recommendation results; iii) the semantic gap between both kinds of data is hard to alleviate. To tackle this research gap, we propose a novel duet representation learning framework named KADM to fuse local information (user-item interaction data) and global information (external knowledge graph) for the top-N recommendation, which is composed of two separate sub-models. One learns the local representations by discovering the inner correlations in local information with a knowledge-aware co-attention mechanism, and another learns the global representations by encoding the knowledge associations in global information with a relation-aware attention network. The two sub-models are jointly trained as part of the semantic fusion network to compute the user preferences, which discriminates the contribution of the two sub-models under the special context. We conduct experiments on two real-world datasets, and the evaluations show that KADM significantly outperforms state-of-art methods. Further ablation studies confirm that the duet architecture performs significantly better than either sub-model on the recommendation tasks.