Miracle-dz/UniFAR-FTUs huggingface dataset:
Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets

Several datasets have recently been constructed to expose brittleness in models trained on existing benchmarks. While model performance on these challenge datasets is significantly lower compared to the original benchmark, it is unclear what particular weaknesses they reveal. For example, a challenge dataset may be difficult because it targets phenomena that current models cannot capture, or because it simply exploits blind spots in a model’s specific training set. We introduce inoculation by fine-tuning, a new analysis method for studying challenge datasets by exposing models (the metaphorical patient) to a small amount of data from the challenge dataset (a metaphorical pathogen) and assessing how well they can adapt. We apply our method to analyze the NLI “stress tests” (Naik et al., 2018) and the Adversarial SQuAD dataset (Jia and Liang, 2017). We show that after slight exposure, some of these datasets are no longer challenging, while others remain difficult. Our results indicate that failures on challenge datasets may lead to very different conclusions about models, training datasets, and the challenge datasets themselves.
ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors

Instance segmentation aims to detect and segment individual objects in a scene. Most existing methods rely on precise mask annotations of every category. However, it is difficult and costly to segment objects in novel categories because a large number of mask annotations is required. We introduce ShapeMask, which learns the intermediate concept of object shape to address the problem of generalization in instance segmentation to novel categories. ShapeMask starts with a bounding box detection and gradually refines it by first estimating the shape of the detected object through a collection of shape priors. Next, ShapeMask refines the coarse shape into an instance level mask by learning instance embeddings. The shape priors provide a strong cue for object-like prediction, and the instance embeddings model the instance specific appearance information. ShapeMask significantly outperforms the state-of-the-art by 6.4 and 3.8 AP when learning across categories, and obtains competitive performance in the fully supervised setting. It is also robust to inaccurate detections, decreased model capacity, and small training data. Moreover, it runs efficiently with 150ms inference time on a GPU and trains within 11 hours on TPUs. With a larger backbone model, ShapeMask increases the gap with state-of-the-art to 9.4 and 6.2 AP across categories. Code will be publicly available at: https://sites.google. com/view/shapemask/home.
On the Mass Accretion Rate and Infrared Excess in Herbig Ae/Be Stars

The present study makes use of the unprecedented capability of the Gaia mission to obtain the stellar parameters such as distance, age, and mass of HAeBe stars. The accuracy of Gaia DR2 astrometry is demonstrated from the comparison of the Gaia DR2 distances of 131 HAeBe stars with the previously estimated values from the literature. This is one of the initial studies to estimate the age and mass of a confirmed sample of HAeBe stars using both the photometry and distance from the Gaia mission. Mass accretion rates are calculated from Hα line flux measurements of 106 HAeBe stars. Since we used distances and the stellar masses derived from the Gaia DR2 data in the calculation of the mass accretion rate, our estimates are more accurate than previous studies. The mass accretion rate is found to decay exponentially with age, from which we estimated a disk dissipation timescale of 1.9 ± 0.1 Myr. The mass accretion rate and stellar mass exhibit a power-law relation of the form . From the distinct distribution in the values of the infrared spectral index, , we suggest the possibility of difference in the disk structure between Herbig Be and Herbig Ae stars.
Evaluation of Greek Word Embeddings

Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is critical. Most research efforts are focusing on English word embeddings. This paper addresses the problem of training and evaluating such models for the Greek language. We present a new word analogy test set considering the original English Word2vec analogy test set and some specific linguistic aspects of the Greek language as well. Moreover, we create a Greek version of WordSim353 test collection for a basic evaluation of word similarities. Produced resources are available for download. We test seven word vector models and our evaluation shows that we are able to create meaningful representations. Last, we discover that the morphological complexity of the Greek language and polysemy can influence the quality of the resulting word embeddings.
Impact of ASR on Alzheimer’s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others

Automatic Speech Recognition (ASR) is a critical component of any fully-automated speech-based dementia detection model. However, despite years of speech recognition research, little is known about the impact of ASR accuracy on dementia detection. In this paper, we experiment with controlled amounts of artificially generated ASR errors and investigate their influence on dementia detection. We find that deletion errors affect detection performance the most, due to their impact on the features of syntactic complexity and discourse representation in speech. We show the trend to be generalisable across two different datasets for cognitive impairment detection. As a conclusion, we propose optimising the ASR to reflect a higher penalty for deletion errors in order to improve dementia detection performance.
On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU

Traditional optimizing compilers rely on rewrite rules to iteratively apply program transformations. This iterative approach hides optimization opportunities behind intermediate transformation steps. For instance, vectorization can only be applied to the innermost loop in a nest: one must first perform a loop interchange before even considering vectorization of an outer loop. In contrast, we propose an implementation framework representing programs as sets of possible implementation decisions. Specifying one decision can have an impact on others in a bidirectional manner: specifying that a loop must be vectorized prevents other loops from being nested inside it; conversely, specifying a loop as an outer loop will prevent it from being vectorized. These optimization decisions commute, obviating the pass ordering problem. We present a constraint programming system to formally define, represent and explore such implementation spaces. We also propose an exploration strategy combining tree search and branch-and-bound; the strength and novelty of this strategy reside in an analytical model of the lower bound on the execution time of a set of possible implementations. We showcase our approach on the construction and exploration of an implementation space for linear algebra kernels running on GPUs. We show this search space is expressive enough to represent complex decisions that fundamentally change the structure of the generated code. We also present preliminary results competitive with the performance of native GPU libraries.
Quasi-Direct Drive for Low-Cost Compliant Robotic Manipulation

Robots must cost less and be force-controlled to enable widespread, safe deployment in unconstrained human environments. We propose Quasi-Direct Drive actuation as a capable paradigm for robotic force-controlled manipulation in human environments at low-cost. Our prototype - Blue - is a human scale 7 Degree of Freedom arm with 2kg payload. Blue can cost less than $5000. We show that Blue has dynamic properties that meet or exceed the needs of human operators: the robot has a nominal position-control bandwidth of 7.5Hz and repeatability within 4mm. We demonstrate a Virtual Reality based interface that can be used as a method for telepresence and collecting robot training demonstrations. Manufacturability, scaling, and potential use-cases for the Blue system are also addressed. Videos and additional information can be found online at berkeleyopenarms.github. io.
Incremental embedding for temporal networks

Prediction over edges and nodes in graphs requires appropriate and efficiently achieved data representation. Recent research on representation learning for dynamic networks resulted in a significant progress. However, the more precise and accurate methods, the greater computational and memory complexity. Here, we introduce ICMEN - the first-in-class incremental meta-embedding method that produces vector representations of nodes respecting temporal dependencies in the graph. ICMEN efficiently constructs nodes' embedding from historical representations by linearly convex combinations making the process less memory demanding than state-of-the-art embedding algorithms. The method is capable of constructing representation for inactive and new nodes without a need to re-embed. The results of link prediction on several real-world datasets shown that applying ICMEN incremental meta-method to any base embedding approach, we receive similar results and save memory and computational power. Taken together, our work proposes a new way of efficient online representation learning in dynamic complex networks.
A characterization of maximal 2-dimensional subgraphs of transitive graphs

A transitive graph is 2-dimensional if it can be represented as the intersection of two linear orders. Such representations make answering of reachability queries trivial, and allow many problems that are NP-hard on arbitrary graphs to be solved in polynomial time. One may therefore be interested in finding 2-dimensional graphs that closely approximate a given graph of arbitrary order dimension. In this paper we show that the maximal 2-dimensional subgraphs of a transitive graph G are induced by the optimal near-transitive orientations of the complement of G. The same characterization holds for the maximal permutation subgraphs of a transitively orientable graph. We provide an algorithm that enables this problem reduction in near-linear time, and an approach for enlarging non-maximal 2-dimensional subgraphs, such as trees.
To Alert or Not to Alert? That Is the Question

Most of crises, environmental, humanitarian, economic or even social, occur after different presaging signals that permit to trigger warnings. These warnings can help to prevent damages and harm if they are issued timely and provide information that helps responders and population to adequately prepare for the disaster to come. Today, there are many systems based on Information and Communication Technologies that are designed to recognize foreboding signals of crises to limit their consequences. Warning system are part of them, they have proved to be effective, but as for all systems including human beings, a part of unpredictable remains. In this article, we provide a method of data analysis that allows decision makers in crisis cells to have answer elements to the question of alerting or not populations in a given geographical area. This method is based on a selection of factors that influence population behaviors, for which we establish a list of relevant indicators that can be informed in the preliminary phase of a crisis into warning systems. From these indicators, we propose a tool for decision support (based on a decision tree as a possible representation).
Planar Geometry and Latest Scene Recovery from a Single Motion Blurred Image

Existing works on motion deblurring either ignore the effects of depth-dependent blur or work with the assumption of a multi-layered scene wherein each layer is modeled in the form of fronto-parallel plane. In this work, we consider the case of 3D scenes with piecewise planar structure i.e., a scene that can be modeled as a combination of multiple planes with arbitrary orientations. We first propose an approach for estimation of normal of a planar scene from a single motion blurred observation. We then develop an algorithm for automatic recovery of a number of planes, the parameters corresponding to each plane, and camera motion from a single motion blurred image of a multiplanar 3D scene. Finally, we propose a first-of-its-kind approach to recover the planar geometry and latent image of the scene by adopting an alternating minimization framework built on our findings. Experiments on synthetic and real data reveal that our proposed method achieves state-of-the-art results.
Adaptive Morphological Reconstruction for Seeded Image Segmentation

Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. First, AMR can adaptively filter out useless seeds while preserving meaningful ones. Second, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, the AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that the AMR is useful for improving performance of algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time.
Influence on Intention to Adopt Green IS: Boosting Endogenous Motivations with Persuasive Systems Design

Because pro-environmental behavior is often perceived as burdensome, encouraging sustainable habits can be a challenging task. Green IS provide additional means to instill proper sustainable manners in its users. However, even adoption of Green IS is not necessarily easy and is likely to require both motivation and persuasion. In this paper, we analyze the impact of Persuasive Systems Design on endogenous motivations linked to the attitude formation and subsequent intention to adopt Green IS. Based on the presented theoretical background, we construct a research model capturing relationships among persuasive design categories, different types of motivations, attitude and intention to adopt Green IS. Using structural equation modeling, we analyze the data collected with the survey. Findings of our study prove that the researched concepts are interrelated showing impact of computerhuman dialogue support, system credibility support, and social support on extrinsic motivation and suggesting importance of enhancing Green IS with Persuasive Systems Design.
Connecting the Scales: Large Area High-resolution Ammonia Mapping of NGC 1333

We use NH3 inversion transitions to trace the dense gas in the NGC 1333 region of the Perseus molecular cloud. NH3 (1, 1) and NH3 (2, 2) maps covering an area of 102 square arcminutes at an angular resolution of ∼3. ″7 are produced by combining Very Large Array interferometric observations with Green Bank Telescope single-dish maps. The combined maps have a spectral resolution of 0.14 km s−1 and a sensitivity of 4 mJy/beam. We produce integrated intensity maps, peak intensity maps, and dispersion maps of NH3 (1, 1) and NH3 (2, 2) and a line-of-sight velocity map of NH3 (1, 1). These are used to derive the optical depth for the NH3 (1, 1) main component, the excitation temperature of NH3 (1, 1), and the rotational temperature, kinetic temperature, and column density of NH3 over the mapped area. We compare these observations with the CARMA J = 1–0 observations of N2H+ and H13CO+ and conclude that they all trace the same material in these dense star-forming regions. From the NH3 (1, 1) velocity map, we find that a velocity gradient ridge extends in an arc across the entire southern part of NGC 1333. We propose that a large-scale turbulent cell is colliding with the cloud, which could result in the formation of a layer of compressed gas. This region along the velocity gradient ridge is dotted with Class 0/I young stellar objects, which could have formed from local overdensities in the compressed gas leading to gravitational instabilities. The NH3 (1, 1) velocity dispersion map also has relatively high values along this region, thereby substantiating the shock layer argument.
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders

We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence. During training we use dynamic programming to consider all possible binary trees over the sentence, and for inference we use the CKY algorithm to extract the highest scoring parse. DIORA outperforms previously reported results for unsupervised binary constituency parsing on the benchmark WSJ dataset.
Aggregated kernel based tests for signal detection in a regression model

Considering a regression model, we address the question of testing the nullity of the regression function. The testing procedure is available when the variance of the observations is unknown and does not depend on any prior information on the alternative. We first propose a single testing procedure based on a general symmetrickernel and an estimation of the variance of the observations. The corresponding critical values are constructed to obtain non asymptotic level-? tests. We then introduce an aggregation procedure to avoid the difficult choice of the kernel and of the parameters of the kernel. The multiple tests satisfy non-asymptotic properties and are adaptive in the minimax sense over several classes of regular alternatives.
Teaching Multiple Concepts to Forgetful Learners

How can we help a forgetful learner learn multiple concepts within a limited time frame? For long-term learning, it is crucial to devise teaching strategies that leverage the underlying forgetting mechanisms of the learners. In this paper, we cast the problem of adaptively teaching a forgetful learner as a novel discrete optimization problem, where we seek to optimize a natural objective function that characterizes the learner's expected performance throughout the teaching session. We then propose a simple greedy teaching strategy and derive strong performance guarantees based on two intuitive data-dependent parameters, which characterize the degree of diminishing returns of teaching each concept. We show that, given some assumptions of the learner's memory model, one can efficiently compute the performance bounds. Furthermore, we identify parameter settings of our memory models where greedy is guaranteed to achieve high performance. We have deployed our approach in two concrete applications, namely (1) an educational app for online vocabulary teaching and (2) an app for teaching novices how to recognize bird species. We demonstrate the effectiveness of our algorithm using simulations along with user studies.
Exploring the Attack Surface of Blockchain: A Systematic Overview

In this paper, we systematically explore the attack surface of the Blockchain technology, with an emphasis on public Blockchains. Towards this goal, we attribute attack viability in the attack surface to 1) the Blockchain cryptographic constructs, 2) the distributed architecture of the systems using Blockchain, and 3) the Blockchain application context. To each of those contributing factors, we outline several attacks, including selfish mining, the 51% attack, Domain Name System (DNS) attacks, distributed denial-of-service (DDoS) attacks, consensus delay (due to selfish behavior or distributed denial-of-service attacks), Blockchain forks, orphaned and stale blocks, block ingestion, wallet thefts, smart contract attacks, and privacy attacks. We also explore the causal relationships between these attacks to demonstrate how various attack vectors are connected to one another. A secondary contribution of this work is outlining effective defense measures taken by the Blockchain technology or proposed by researchers to mitigate the effects of these attacks and patch associated vulnerabilities
Eliciting New Wikipedia Users' Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start

Every day, thousands of users sign up as new Wikipedia contributors. Once joined, these users have to decide which articles to contribute to, which users to seek out and learn from or collaborate with, etc. Any such task is a hard and potentially frustrating one given the sheer size of Wikipedia. Supporting newcomers in their first steps by recommending articles they would enjoy editing or editors they would enjoy collaborating with is thus a promising route toward converting them into long-term contributors. Standard recommender systems, however, rely on users’ histories of previous interactions with the platform. As such, these systems cannot make high-quality recommendations to newcomers without any previous interactions—the so-called cold-start problem. The present paper addresses the cold-start problem on Wikipedia by developing a method for automatically building short questionnaires that, when completed by a newly registered Wikipedia user, can be used for a variety of purposes, including article recommendations that can help new editors get started. Our questionnaires are constructed based on the text of Wikipedia articles as well as the history of contributions by the already onboarded Wikipedia editors. We assess the quality of our questionnaire-based recommendations in an offline evaluation using historical data, as well as an online evaluation with hundreds of real Wikipedia newcomers, concluding that our method provides cohesive, human-readable questions that perform well against several baselines. By addressing the cold-start problem, this work can help with the sustainable growth and maintenance of Wikipedia’s diverse editor community.
Building Dynamic Capabilities with the Internet of Things

In this study, we propose internet of things (IoT) capabilities as dynamic capabilities through their effect on a firm’s competitive advantage. We argue that the importance of the IoT lies on its ability to identify new opportunities, address them, and reconfigure the existing and/or new technology assets in rapid technology change environments. Firms with strong IoT capabilities will be able to create, (re)shape, and transform their business ecosystems through innovation. Using data collected from 184 companies, the proposed framework was tested. The results show IoT capabilities, manifested in sensing, seizing, and reconfiguring capabilities, are positively associated with a firm’s competitive advantage. This study can help scholars and practitioners understand the elements of the IoT that may lead to competitive advantage from the dynamic capabilities perspective.