OpenRaiser/Intern-Atlas huggingface dataset:
Towards Situation Awareness and Attention Guidance in a Multiplayer Environment using Augmented Reality and Carcassonne
Venue: arXiv cs.HC 2022
Year: 2022

Augmented reality (AR) games are a rich environment for researching and testing computational systems that provide subtle user guidance and training. In particular computer systems that aim to augment a user's situation awareness benefit from the range of sensors and computing power available in AR headsets. In this work-in-progress paper, we present a new environment for research into situation awareness and attention guidance (SAAG): an augmented reality version of the board game Carcassonne. We also present our initial work in producing a SAAG pipeline, including the creation of game state encodings, the development and training of a gameplay AI, and the design of situation modelling and gaze tracking systems.
3M: An Effective Multi-view, Multi-granularity, and Multi-aspect Modeling Approach to English Pronunciation Assessment
Venue: arXiv cs.SD 2022
Year: 2022

As an indispensable ingredient of computer-assisted pronunciation training (CAPT), automatic pronunciation assessment (APA) plays a pivotal role in aiding self-directed language learners by providing multi-aspect and timely feedback. However, there are at least two potential obstacles that might hinder its performance for practical use. On one hand, most of the studies focus exclusively on leveraging segmental (phonetic)-level features such as goodness of pronunciation (GOP); this, however, may cause a discrepancy of feature granularity when performing suprasegmental (prosodic)-level pronunciation assessment. On the other hand, automatic pronunciation assessments still suffer from the lack of large-scale labeled speech data of non-native speakers, which inevitably limits the performance of pronunciation assessment. In this paper, we tackle these problems by integrating multiple prosodic and phonological features to provide a multi-view, multi-granularity, and multi-aspect (3M) pronunciation modeling. Specifically, we augment GOP with prosodic and self-supervised learning (SSL) features, and meanwhile develop a vowel/consonant positional embedding for a more phonology-aware automatic pronunciation assessment. A series of experiments conducted on the publicly-available speechocean762 dataset show that our approach can obtain significant improvements on several assessment granularities in comparison with previous work, especially on the assessment of speaking fluency and speech prosody.
Effective Transfer Learning for Low-Resource Natural Language Understanding
Venue: arXiv cs.CL 2022
Year: 2022

Natural language understanding (NLU) is the task of semantic decoding of human languages by machines. NLU models rely heavily on large training data to ensure good performance. However, substantial languages and domains have very few data resources and domain experts. It is necessary to overcome the data scarcity challenge, when very few or even zero training samples are available. In this thesis, we focus on developing cross-lingual and cross-domain methods to tackle the low-resource issues. First, we propose to improve the model's cross-lingual ability by focusing on the task-related keywords, enhancing the model's robustness and regularizing the representations. We find that the representations for low-resource languages can be easily and greatly improved by focusing on just the keywords. Second, we present Order-Reduced Modeling methods for the cross-lingual adaptation, and find that modeling partial word orders instead of the whole sequence can improve the robustness of the model against word order differences between languages and task knowledge transfer to low-resource languages. Third, we propose to leverage different levels of domain-related corpora and additional masking of data in the pre-training for the cross-domain adaptation, and discover that more challenging pre-training can better address the domain discrepancy issue in the task knowledge transfer. Finally, we introduce a coarse-to-fine framework, Coach, and a cross-lingual and cross-domain parsing framework, X2Parser. Coach decomposes the representation learning process into a coarse-grained and a fine-grained feature learning, and X2Parser simplifies the hierarchical task structures into flattened ones. We observe that simplifying task structures makes the representation learning more effective for low-resource languages and domains.
Almost Cost-Free Communication in Federated Best Arm Identification
Venue: arXiv cs.LG 2022
Year: 2022

We study the problem of best arm identification in a federated learning multi-armed bandit setup with a central server and multiple clients. Each client is associated with a multi-armed bandit in which each arm yields {\em i.i.d.}\ rewards following a Gaussian distribution with an unknown mean and known variance. The set of arms is assumed to be the same at all the clients. We define two notions of best arm -- local and global. The local best arm at a client is the arm with the largest mean among the arms local to the client, whereas the global best arm is the arm with the largest average mean across all the clients. We assume that each client can only observe the rewards from its local arms and thereby estimate its local best arm. The clients communicate with a central server on uplinks that entail a cost of $C\ge0$ units per usage per uplink. The global best arm is estimated at the server. The goal is to identify the local best arms and the global best arm with minimal total cost, defined as the sum of the total number of arm selections at all the clients and the total communication cost, subject to an upper bound on the error probability. We propose a novel algorithm {\sc FedElim} that is based on successive elimination and communicates only in exponential time steps and obtain a high probability instance-dependent upper bound on its total cost. The key takeaway from our paper is that for any $C\geq 0$ and error probabilities sufficiently small, the total number of arm selections (resp.\ the total cost) under {\sc FedElim} is at most~$2$ (resp.~$3$) times the maximum total number of arm selections under its variant that communicates in every time step. Additionally, we show that the latter is optimal in expectation up to a constant factor, thereby demonstrating that communication is almost cost-free in {\sc FedElim}. We numerically validate the efficacy of {\sc FedElim}.
Mechanism Learning for Trading Networks
Venue: arXiv cs.GT 2022
Year: 2022

We study the problem of designing mechanisms for trading networks that satisfy four desired properties: dominant-strategy incentive compatibility, efficiency, weak budget balance (WBB), and individual rationality (IR). Although there exist mechanisms that simultaneously satisfy these properties ex post for combinatorial auctions, we prove the impossibility that such mechanisms do not exist for a broad class of trading networks. We thus propose approaches for computing and learning the mechanisms that satisfy the four properties, in a Bayesian setting, where WBB and IR, respectively, are relaxed to ex ante and interim. For computational and sample efficiency, we introduce several techniques, including game theoretical analysis to reduce the input feature space. We empirically demonstrate that the proposed approaches successfully find the mechanisms with the four properties for those trading networks where the impossibility holds ex post.
CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data
Venue: arXiv cs.HC 2022
Year: 2022

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.
Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks
Venue: arXiv cs.CV 2022
Year: 2022

Robust classification is essential in tasks like autonomous vehicle sign recognition, where the downsides of misclassification can be grave. Adversarial attacks threaten the robustness of neural network classifiers, causing them to consistently and confidently misidentify road signs. One such class of attack, shadow-based attacks, causes misidentifications by applying a natural-looking shadow to input images, resulting in road signs that appear natural to a human observer but confusing for these classifiers. Current defenses against such attacks use a simple adversarial training procedure to achieve a rather low 25\% and 40\% robustness on the GTSRB and LISA test sets, respectively. In this paper, we propose a robust, fast, and generalizable method, designed to defend against shadow attacks in the context of road sign recognition, that augments source images with binary adaptive threshold and edge maps. We empirically show its robustness against shadow attacks, and reformulate the problem to show its similarity to $\varepsilon$ perturbation-based attacks. Experimental results show that our edge defense results in 78\% robustness while maintaining 98\% benign test accuracy on the GTSRB test set, with similar results from our threshold defense. Link to our code is in the paper.
Applying Back Propagation Algorithm and Analytic Hierarchy Process to Environment Assessment
Venue: arXiv cs.CY 2022
Year: 2022

This paper designs a new and scientific environmental quality assessment method, and takes Saihan dam as an example to explore the environmental improvement degree to the local and Beijing areas. AHP method is used to assign values to each weight 7 primary indicators and 21 secondary indicators were used to establish an environmental quality assessment model. The conclusion shows that after the establishment of Saihan dam, the local environmental quality has been improved by 7 times, and the environmental quality in Beijing has been improved by 13%. Then the future environmental index is predicted. Finally the Spearson correlation coefficient is analyzed, and it is proved that correlation is 99% when the back-propagation algorithm is used to test and prove that the error is little.
Real and simulated CBM data interacting with an ESCAPE datalake
Venue: arXiv cs.DB 2022
Year: 2022

Integration of the ESCAPE and CBM software environment. The ESCAPE datalake are utilized by the CBM experiment for the storage, distribution and retrieval of real SIS18 and simulated SIS100 particle physics data.
A Ptolemaic Partitioning Mechanism
Venue: arXiv cs.DS 2022
Year: 2022

For many years, exact metric search relied upon the property of triangle inequality to give a lower bound on uncalculated distances. Two exclusion mechanisms derive from this property, generally known as pivot exclusion and hyperplane exclusion. These mechanisms work in any proper metric space and are the basis of many metric indexing mechanisms. More recently, the Ptolemaic and four-point lower bound properties have been shown to give tighter bounds in some subclasses of metric space. Both triangle inequality and the four-point lower bound directly imply straightforward partitioning mechanisms: that is, a method of dividing a finite space according to a fixed partition, in order that one or more classes of the partition can be eliminated from a search at query time. However, up to now, no partitioning principle has been identified for the Ptolemaic inequality, which has been used only as a filtering mechanism. Here, a novel partitioning mechanism for the Ptolemaic lower bound is presented. It is always better than either pivot or hyperplane partitioning. While the exclusion condition itself is weaker than Hilbert (four-point) exclusion, its calculation is cheaper. Furthermore, it can be combined with Hilbert exclusion to give a new maximum for exclusion power with respect to the number of distances measured per query.
Merging Sorted Lists of Similar Strings
Venue: arXiv cs.DS 2022
Year: 2022

Merging $T$ sorted, non-redundant lists containing $M$ elements into a single sorted, non-redundant result of size $N \ge M/T$ is a classic problem typically solved practically in $O(M \log T)$ time with a priority-queue data structure the most basic of which is the simple *heap*. We revisit this problem in the situation where the list elements are *strings* and the lists contain many *identical or nearly identical elements*. By keeping simple auxiliary information with each heap node, we devise an $O(M \log T+S)$ worst-case method that performs no more character comparisons than the sum of the lengths of all the strings $S$, and another $O(M \log (T/ \bar e)+S)$ method that becomes progressively more efficient as a function of the fraction of equal elements $\bar e = M/N$ between input lists, reaching linear time when the lists are all identical. The methods perform favorably in practice versus an alternate formulation based on a trie.
ArUco Maker based localization and Node graph approach to mapping
Venue: arXiv cs.RO 2022
Year: 2022

This paper explores a method of localization and navigation of indoor mobile robots using a node graph of landmarks that are based on fiducial markers. The use of ArUco markers and their 2D orientation with respect to the camera of the robot and the distance to the markers from the camera is used to calculate the relative position of the robot as well as the relative positions of other markers. The proposed method combines aspects of beacon-based navigation and Simultaneous Localization and Mapping based navigation. The implementation of this method uses a depth camera to obtain the distance to the marker. After calculating the required orientation of the marker, it relies on odometry calculations for tracking the position after localization with respect to the marker. Using the odometry and the relative position of one marker, the robot is then localized with respect to another marker. The relative positions and orientation of the two markers are then calculated. The markers are represented as nodes and the relative distances and orientations are represented as edges connecting the nodes and a node graph can be generated that represents a map for the robot. The method was tested on a wheeled humanoid robot with the objective of having it autonomously navigate to a charging station inside a room. This objective was successfully achieved and the limitations and future improvements are briefly discussed.
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Venue: arXiv cs.CV 2022
Year: 2022

Standard diffusion models involve an image transform -- adding Gaussian noise -- and an image restoration operator that inverts this degradation. We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice. Even when using completely deterministic degradations (e.g., blur, masking, and more), the training and test-time update rules that underlie diffusion models can be easily generalized to create generative models. The success of these fully deterministic models calls into question the community's understanding of diffusion models, which relies on noise in either gradient Langevin dynamics or variational inference, and paves the way for generalized diffusion models that invert arbitrary processes. Our code is available at https://github.com/arpitbansal297/Cold-Diffusion-Models
A level-set formulation to simulate diffusive solid/solid phase transformation in polycrystalline metallic materials -- Application to austenite decomposition in steels
Venue: arXiv cs.CE 2022
Year: 2022

Numerous full-field numerical methods exist concerning the digital description of polycrystalline materials and the modeling of their evolution during thermomechanical treatments. However, these strategies are globally dedicated to the modeling of recrystallization and grain growth for single-phase materials, or to the modeling of phase transformations without considering recrystallization and related phenomena. A generalized numerical framework capable of making predictions in a multi-phase polycrystalline context while respecting the concomitance of the different microstructural mechanisms is thus of prime interest. A novel finite element level-set based full-field numerical formulation is proposed to principally simulate diffusive solid-solid phase transformation at the mesoscopic scale in the context of two-phase metallic alloys. A global kinetic framework, capable of accounting for other concomitant mechanisms such as recrystallization and grain growth is considered in this numerical model. The proposed numerical framework is shown to be promising through a couple of illustrative 1D and 2D test cases in the context of austenite decomposition in steels and compared with ThermoCalc estimations.
Feature Selection for Fault Detection and Prediction based on Event Log Analysis
Venue: arXiv cs.LG 2022
Year: 2022

Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and anomaly detection, wherein the feature extraction step extracts useful features for anomaly detection by counting log events. For a complex system, such as a lithography machine consisting of a large number of subsystems, its log may contain thousands of different events, resulting in abounding extracted features. However, when anomaly detection is performed at the subsystem level, analyzing all features becomes expensive and unnecessary. To mitigate this problem, we develop a feature selection method for log-based anomaly detection and prediction, largely improving the effectiveness and efficiency.
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment
Venue: arXiv cs.CL 2022
Year: 2022

Grammatical Error Correction (GEC) systems perform a sequence-to-sequence task, where an input word sequence containing grammatical errors, is corrected for these errors by the GEC system to output a grammatically correct word sequence. With the advent of deep learning methods, automated GEC systems have become increasingly popular. For example, GEC systems are often used on speech transcriptions of English learners as a form of assessment and feedback - these powerful GEC systems can be used to automatically measure an aspect of a candidate's fluency. The count of \textit{edits} from a candidate's input sentence (or essay) to a GEC system's grammatically corrected output sentence is indicative of a candidate's language ability, where fewer edits suggest better fluency. The count of edits can thus be viewed as a \textit{fluency score} with zero implying perfect fluency. However, although deep learning based GEC systems are extremely powerful and accurate, they are susceptible to adversarial attacks: an adversary can introduce a small, specific change at the input of a system that causes a large, undesired change at the output. When considering the application of GEC systems to automated language assessment, the aim of an adversary could be to cheat by making a small change to a grammatically incorrect input sentence that conceals the errors from a GEC system, such that no edits are found and the candidate is unjustly awarded a perfect fluency score. This work examines a simple universal substitution adversarial attack that non-native speakers of English could realistically employ to deceive GEC systems used for assessment.
Glass-Vault: A Generic Transparent Privacy-preserving Exposure Notification Analytics Platform
Venue: arXiv cs.CR 2022
Year: 2022

The highly transmissible COVID-19 disease is a serious threat to people's health and life. To automate tracing those who have been in close physical contact with newly infected people and/or to analyse tracing-related data, researchers have proposed various ad-hoc programs that require being executed on users' smartphones. Nevertheless, the existing solutions have two primary limitations: (1) lack of generality: for each type of analytic task, a certain kind of data needs to be sent to an analyst; (2) lack of transparency: parties who provide data to an analyst are not necessarily infected individuals; therefore, infected individuals' data can be shared with others (e.g., the analyst) without their fine-grained and direct consent. In this work, we present Glass-Vault, a protocol that addresses both limitations simultaneously. It allows an analyst to run authorised programs over the collected data of infectious users, without learning the input data. Glass-Vault relies on a new variant of generic Functional Encryption that we propose in this work. This new variant, called DD-Steel, offers these two additional properties: dynamic and decentralised. We illustrate the security of both Glass-Vault and DD-Steel in the Universal Composability setting. Glass-Vault is the first UC-secure protocol that allows analysing the data of Exposure Notification users in a privacy-preserving manner. As a sample application, we indicate how it can be used to generate "infection heatmaps".
Study of Novel Sparse Array Design Based on the Maximum Inter-Element Spacing Criterion
Venue: arXiv cs.LG 2022
Year: 2022

A novel sparse array (SA) structure is proposed based on the maximum inter-element spacing (IES) constraint (MISC) criterion. Compared with the traditional MISC array, the proposed SA configurations, termed as improved MISC (IMISC) has significantly increased uniform degrees of freedom (uDOF) and reduced mutual coupling. In particular, the IMISC arrays are composed of six uniform linear arrays (ULAs), which can be determined by an IES set. The IES set is constrained by two parameters, namely the maximum IES and the number of sensors. The uDOF of the IMISC arrays is derived and the weight function of the IMISC arrays is analyzed as well. The proposed IMISC arrays have a great advantage in terms of uDOF against the existing SAs, while their mutual coupling remains at a low level. Simulations are carried out to demonstrate the advantages of the IMISC arrays.