OpenRaiser/Intern-Atlas huggingface dataset:
Geometry-Aware Planar Embedding of Treelike Structures
Venue: arXiv cs.GR 2022
Year: 2022

The growing complexity of spatial and structural information in 3D data makes data inspection and visualization a challenging task. We describe a method to create a planar embedding of 3D treelike structures using their skeleton representations. Our method maintains the original geometry, without overlaps, to the best extent possible, allowing exploration of the topology within a single view. We present a novel camera view generation method which maximizes the visible geometric attributes (segment shape and relative placement between segments). Camera views are created for individual segments and are used to determine local bending angles at each node by projecting them to 2D. The final embedding is generated by minimizing an energy function (the weights of which are user adjustable) based on branch length and the 2D angles, while avoiding intersections. The user can also interactively modify segment placement within the 2D embedding, and the overall embedding will update accordingly. A global to local interactive exploration is provided using hierarchical camera views that are created for subtrees within the structure. We evaluate our method both qualitatively and quantitatively and demonstrate our results by constructing planar visualizations of line data (traced neurons) and volume data (CT vascular and bronchial data
Human-in-the-loop Machine Learning: A Macro-Micro Perspective
Venue: arXiv cs.HC 2022
Year: 2022

Though technical advance of artificial intelligence and machine learning has enabled many promising intelligent systems, many computing tasks are still not able to be fully accomplished by machine intelligence. Motivated by the complementary nature of human and machine intelligence, an emerging trend is to involve humans in the loop of machine learning and decision-making. In this paper, we provide a macro-micro review of human-in-the-loop machine learning. We first describe major machine learning challenges which can be addressed by human intervention in the loop. Then we examine closely the latest research and findings of introducing humans into each step of the lifecycle of machine learning. Finally, we analyze current research gaps and point out future research directions.
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning
Venue: arXiv cs.CE 2022
Year: 2022

Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of multiscale architectures. The model-centric research trend, however, lacks principled frameworks dedicated to data acquisition, whose quality propagates into the downstream tasks. Often built by naive space-filling design in shape descriptor space, metamaterial datasets suffer from property distributions that are either highly imbalanced or at odds with design tasks of interest. To this end, we present t-METASET: an active-learning-based data acquisition framework aiming to guide both diverse and task-aware data generation. Distinctly, we seek a solution to a commonplace yet frequently overlooked scenario at early stages of data-driven design of metamaterials: when a massive (~O(10^4 )) shape-only library has been prepared with no properties evaluated. The key idea is to harness a data-driven shape descriptor learned from generative models, fit a sparse regressor as a start-up agent, and leverage metrics related to diversity to drive data acquisition to areas that help designers fulfill design goals. We validate the proposed framework in three deployment cases, which encompass general use, task-specific use, and tailorable use. Two large-scale mechanical metamaterial datasets are used to demonstrate the efficacy. Applicable to general image-based design representations, t-METASET could boost future advancements in data-driven design.
An Architecture for Web 3.0 and the Emergence of Spontaneous Time Order
Venue: arXiv cs.CY 2022
Year: 2022

In this study, we proposed an architecture for Web 3.0, which is based on the hashed interactions among user nodes that can transform bilateral trusts into collective time order, which is the major achievement of blockchain technology, without the expensive Proof of Work or the questionable Proof of Stake.
Hidden bawls, whispers, and yelps: can text be made to sound more than just its words?
Venue: arXiv cs.HC 2022
Year: 2022

Whether a word was bawled, whispered, or yelped, captions will typically represent it in the same way. If they are your only way to access what is being said, subjective nuances expressed in the voice will be lost. Since so much of communication is carried by these nuances, we posit that if captions are to be used as an accurate representation of speech, embedding visual representations of paralinguistic qualities into captions could help readers use them to better understand speech beyond its mere textual content. This paper presents a model for processing vocal prosody (its loudness, pitch, and duration) and mapping it into visual dimensions of typography (respectively, font-weight, baseline shift, and letter-spacing), creating a visual representation of these lost vocal subtleties that can be embedded directly into the typographical form of text. An evaluation was carried out where participants were exposed to this speech-modulated typography and asked to match it to its originating audio, presented between similar alternatives. Participants (n=117) were able to correctly identify the original audios with an average accuracy of 65%, with no significant difference when showing them modulations as animated or static text. Additionally, participants' comments showed their mental models of speech-modulated typography varied widely.
Shape-Haptics: Planar & Passive Force Feedback Mechanisms for Physical Interfaces
Venue: arXiv cs.HC 2022
Year: 2022

We present Shape-Haptics, an approach for designers to rapidly design and fabricate passive force feedback mechanisms for physical interfaces. Such mechanisms are used in everyday interfaces and tools, and they are challenging to design. Shape-Haptics abstracts and broadens the haptic expression of this class of force feedback systems through 2D laser cut configurations that are simple to fabricate. They leverage the properties of polyoxymethylene plastic and comprise a compliant spring structure that engages with a sliding profile during tangible interaction. By shaping the sliding profile, designers can easily customize the haptic force feedback delivered by the mechanism. We provide a computational design sandbox to facilitate designers to explore and fabricate Shape-Haptics mechanisms. We also propose a series of applications that demonstrate the utility of Shape-Haptics in creating and customizing haptics for different physical interfaces.
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges
Venue: arXiv cs.CV 2022
Year: 2022

This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2022. The 3rd ABAW Competition is a continuation of the Competitions held at ICCV 2021, IEEE FG 2020 and IEEE CVPR 2017 Conferences, and aims at automatically analyzing affect. This year the Competition encompasses four Challenges: i) uni-task Valence-Arousal Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit Detection, and iv) Multi-Task-Learning. All the Challenges are based on a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one to be annotated in terms of valence-arousal, expressions and action units. In this paper, we present the four Challenges, with the utilized Competition corpora, we outline the evaluation metrics and present the baseline systems along with their obtained results.
Automatically Generating Counterfactuals for Relation Classification
Venue: arXiv cs.CL 2022
Year: 2022

The goal of relation classification (RC) is to extract the semantic relations between/among entities in the text. As a fundamental task in natural language processing, it is crucial to ensure the robustness of RC models. Despite the high accuracy current deep neural models have achieved in RC tasks, they are easily affected by spurious correlations. One solution to this problem is to train the model with counterfactually augmented data (CAD) such that it can learn the causation rather than the confounding. However, no attempt has been made on generating counterfactuals for RC tasks. In this paper, we formulate the problem of automatically generating CAD for RC tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. Specifically, we exploit two elementary topological properties, i.e., the centrality and the shortest path, in syntactic and semantic dependency graphs, to first identify and then intervene on the contextual causal features for entities. We conduct a comprehensive evaluation on four RC datasets by combining our proposed approach with a variety of backbone RC models. The results demonstrate that our approach not only improves the performance of the backbones, but also makes them more robust in the out-of-domain test.
Transition Matrix Representation of Trees with Transposed Convolutions
Venue: arXiv cs.LG 2022
Year: 2022

How can we effectively find the best structures in tree models? Tree models have been favored over complex black box models in domains where interpretability is crucial for making irreversible decisions. However, searching for a tree structure that gives the best balance between the performance and the interpretability remains a challenging task. In this paper, we propose TART (Transition Matrix Representation with Transposed Convolutions), our novel generalized tree representation for optimal structural search. TART represents a tree model with a series of transposed convolutions that boost the speed of inference by avoiding the creation of transition matrices. As a result, TART allows one to search for the best tree structure with a few design parameters, achieving higher classification accuracy than those of baseline models in feature-based datasets.
Submodlib: A Submodular Optimization Library
Venue: arXiv cs.LG 2022
Year: 2022

Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has applied submodular optimization to find optimal subsets in various contexts. Some examples include data summarization for efficient human consumption, finding effective smaller subsets of training data to reduce the model development time (training, hyper parameter tuning), finding effective subsets of unlabeled data to reduce the labeling costs, etc. A recent work has also leveraged submodular functions to propose submodular information measures which have been found to be very useful in solving the problems of guided subset selection and guided summarization. In this work, we present Submodlib which is an open-source, easy-to-use, efficient and scalable Python library for submodular optimization with a C++ optimization engine. Submodlib finds its application in summarization, data subset selection, hyper parameter tuning, efficient training and more. Through a rich API, it offers a great deal of flexibility in the way it can be used. Source of Submodlib is available at https://github.com/decile-team/submodlib.
Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models
Venue: arXiv cs.LG 2022
Year: 2022

Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is that the data will be collected with high spatial and temporal diversity to best characterize a building and the occupant's preferences. But in reality, leaving the occupant to respond in an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant data. This paper outlines a scenario-based (virtual experiment) method for optimizing data sampling using a smartwatch to achieve comparable accuracy in a personal thermal preference model with fewer data. This method uses BIM-extracted spatial data and Graph Neural Network-based (GNN) modeling to find regions of similar comfort preference to identify the best scenarios for triggering the occupant to give feedback. This method is compared to two baseline scenarios that use conventional zoning and a generic 4x4 square meter grid method from two field-based data sets. The results show that the proposed Build2Vec method has an 18-23\% higher overall sampling quality than the spaces-based and square-grid-based sampling methods. The Build2Vec method also performs similar to the baselines when removing redundant occupant feedback points but with better scalability potential.
Social Computational Design Method for Generating Product Shapes with GAN and Transformer Models
Venue: arXiv cs.AI 2022
Year: 2022

A social computational design method is established, aiming at taking advantages of the fast-developing artificial intelligence technologies for intelligent product design. Supported with multi-agent system, shape grammar, Generative adversarial network, Bayesian network, Transformer, etc., the method is able to define the design solution space, prepare training samples, and eventually acquire an intelligent model that can recommend design solutions according to incomplete solutions for given design tasks. Product shape design is used as entry point to demonstrate the method, however, the method can be applied to tasks rather than shape design when the solutions can be properly coded.
Explicit Regularization via Regularizer Mirror Descent
Venue: arXiv cs.LG 2022
Year: 2022

Despite perfectly interpolating the training data, deep neural networks (DNNs) can often generalize fairly well, in part due to the "implicit regularization" induced by the learning algorithm. Nonetheless, various forms of regularization, such as "explicit regularization" (via weight decay), are often used to avoid overfitting, especially when the data is corrupted. There are several challenges with explicit regularization, most notably unclear convergence properties. Inspired by convergence properties of stochastic mirror descent (SMD) algorithms, we propose a new method for training DNNs with regularization, called regularizer mirror descent (RMD). In highly overparameterized DNNs, SMD simultaneously interpolates the training data and minimizes a certain potential function of the weights. RMD starts with a standard cost which is the sum of the training loss and a convex regularizer of the weights. Reinterpreting this cost as the potential of an "augmented" overparameterized network and applying SMD yields RMD. As a result, RMD inherits the properties of SMD and provably converges to a point "close" to the minimizer of this cost. RMD is computationally comparable to stochastic gradient descent (SGD) and weight decay, and is parallelizable in the same manner. Our experimental results on training sets with various levels of corruption suggest that the generalization performance of RMD is remarkably robust and significantly better than both SGD and weight decay, which implicitly and explicitly regularize the $\ell_2$ norm of the weights. RMD can also be used to regularize the weights to a desired weight vector, which is particularly relevant for continual learning.
Robust and Provable Guarantees for Sparse Random Embeddings
Venue: arXiv cs.LG 2022
Year: 2022

In this work, we improve upon the guarantees for sparse random embeddings, as they were recently provided and analyzed by Freksen at al. (NIPS'18) and Jagadeesan (NIPS'19). Specifically, we show that (a) our bounds are explicit as opposed to the asymptotic guarantees provided previously, and (b) our bounds are guaranteed to be sharper by practically significant constants across a wide range of parameters, including the dimensionality, sparsity and dispersion of the data. Moreover, we empirically demonstrate that our bounds significantly outperform prior works on a wide range of real-world datasets, such as collections of images, text documents represented as bags-of-words, and text sequences vectorized by neural embeddings. Behind our numerical improvements are techniques of broader interest, which improve upon key steps of previous analyses in terms of (c) tighter estimates for certain types of quadratic chaos, (d) establishing extreme properties of sparse linear forms, and (e) improvements on bounds for the estimation of sums of independent random variables.
One-shot Scene Graph Generation
Venue: arXiv cs.CV 2022
Year: 2022

As a structured representation of the image content, the visual scene graph (visual relationship) acts as a bridge between computer vision and natural language processing. Existing models on the scene graph generation task notoriously require tens or hundreds of labeled samples. By contrast, human beings can learn visual relationships from a few or even one example. Inspired by this, we design a task named One-Shot Scene Graph Generation, where each relationship triplet (e.g., "dog-has-head") comes from only one labeled example. The key insight is that rather than learning from scratch, one can utilize rich prior knowledge. In this paper, we propose Multiple Structured Knowledge (Relational Knowledge and Commonsense Knowledge) for the one-shot scene graph generation task. Specifically, the Relational Knowledge represents the prior knowledge of relationships between entities extracted from the visual content, e.g., the visual relationships "standing in", "sitting in", and "lying in" may exist between "dog" and "yard", while the Commonsense Knowledge encodes "sense-making" knowledge like "dog can guard yard". By organizing these two kinds of knowledge in a graph structure, Graph Convolution Networks (GCNs) are used to extract knowledge-embedded semantic features of the entities. Besides, instead of extracting isolated visual features from each entity generated by Faster R-CNN, we utilize an Instance Relation Transformer encoder to fully explore their context information. Based on a constructed one-shot dataset, the experimental results show that our method significantly outperforms existing state-of-the-art methods by a large margin. Ablation studies also verify the effectiveness of the Instance Relation Transformer encoder and the Multiple Structured Knowledge.
Exploiting long-term temporal dynamics for video captioning
Venue: arXiv cs.CV 2022
Year: 2022

Automatically describing videos with natural language is a fundamental challenge for computer vision and natural language processing. Recently, progress in this problem has been achieved through two steps: 1) employing 2-D and/or 3-D Convolutional Neural Networks (CNNs) (e.g. VGG, ResNet or C3D) to extract spatial and/or temporal features to encode video contents; and 2) applying Recurrent Neural Networks (RNNs) to generate sentences to describe events in videos. Temporal attention-based model has gained much progress by considering the importance of each video frame. However, for a long video, especially for a video which consists of a set of sub-events, we should discover and leverage the importance of each sub-shot instead of each frame. In this paper, we propose a novel approach, namely temporal and spatial LSTM (TS-LSTM), which systematically exploits spatial and temporal dynamics within video sequences. In TS-LSTM, a temporal pooling LSTM (TP-LSTM) is designed to incorporate both spatial and temporal information to extract long-term temporal dynamics within video sub-shots; and a stacked LSTM is introduced to generate a list of words to describe the video. Experimental results obtained in two public video captioning benchmarks indicate that our TS-LSTM outperforms the state-of-the-art methods.
Flipping Plane Spanning Paths
Venue: arXiv cs.CG 2022
Year: 2022

Let $S$ be a planar point set in general position, and let $\mathcal{P}(S)$ be the set of all plane straight-line paths with vertex set $S$. A flip on a path $P \in \mathcal{P}(S)$ is the operation of replacing an edge $e$ of $P$ with another edge $f$ on $S$ to obtain a new valid path from $\mathcal{P}(S)$. It is a long-standing open question whether for every given point set $S$, every path from $\mathcal{P}(S)$ can be transformed into any other path from $\mathcal{P}(S)$ by a sequence of flips. To achieve a better understanding of this question, we show that it is sufficient to prove the statement for plane spanning paths whose first edge is fixed. Furthermore, we provide positive answers for special classes of point sets, namely, for wheel sets and generalized double circles (which include, e.g., double chains and double circles).
Multi-service Threats: Attacking and Protecting Network Printers and VoIP Phones alike
Venue: arXiv cs.CR 2022
Year: 2022

Printing over a network and calling over VoIP technology are routine at present. This article investigates to what extent these services can be attacked using freeware in the real world if they are not configured securely. In finding out that attacks of high impact, termed the Printjack and Phonejack families, could be mounted at least from insiders, the article also observes that secure configurations do not appear to be widely adopted. Users with the necessary skills may put existing security measures in place with printers, but would need novel measures, which the article prototypes, with phones in order for a pair of peers to call each other securely and without trusting anyone else, including sysadmins.
Neural Program Repair: Systems, Challenges and Solutions
Venue: arXiv cs.SE 2022
Year: 2022

Automated Program Repair (APR) aims to automatically fix bugs in the source code. Recently, as advances in Deep Learning (DL) field, there is a rise of Neural Program Repair (NPR) studies, which formulate APR as a translation task from buggy code to correct code and adopt neural networks based on encoder-decoder architecture. Compared with other APR techniques, NPR approaches have a great advantage in applicability because they do not need any specification (i.e., a test suite). Although NPR has been a hot research direction, there isn't any overview on this field yet. In order to help interested readers understand architectures, challenges and corresponding solutions of existing NPR systems, we conduct a literature review on latest studies in this paper. We begin with introducing the background knowledge on this field. Next, to be understandable, we decompose the NPR procedure into a series of modules and explicate various design choices on each module. Furthermore, we identify several challenges and discuss the effect of existing solutions. Finally, we conclude and provide some promising directions for future research.