OpenRaiser/Intern-Atlas huggingface dataset:
Using Elo Rating as a Metric for Comparative Judgement in Educational Assessment
Venue: arXiv cs.HC 2022
Year: 2022

Marking and feedback are essential features of teaching and learning, across the overwhelming majority of educational settings and contexts. However, it can take a great deal of time and effort for teachers to mark assessments, and to provide useful feedback to the students. Furthermore, it also creates a significant cognitive load on the assessors, especially in ensuring fairness and equity. Therefore, an alternative approach to marking called comparative judgement (CJ) has been proposed in the educational space. Inspired by the law of comparative judgment (LCJ). This pairwise comparison for as many pairs as possible can then be used to rank all submissions. Studies suggest that CJ is highly reliable and accurate while making it quick for the teachers. Alternative studies have questioned this claim suggesting that the process can increase bias in the results as the same submission is shown many times to an assessor for increasing reliability. Additionally, studies have also found that CJ can result in the overall marking process taking longer than a more traditional method of marking as information about many pairs must be collected. In this paper, we investigate Elo, which has been extensively used in rating players in zero-sum games such as chess. We experimented on a large-scale Twitter dataset on the topic of a recent major UK political event ("Brexit", the UK's political exit from the European Union) to ask users which tweet they found funnier between a pair selected from ten tweets. Our analysis of the data reveals that the Elo rating is statistically significantly similar to the CJ ranking with a Kendall's tau score of 0.96 and a p-value of 1.5x10^(-5). We finish with an informed discussion regarding the potential wider application of this approach to a range of educational contexts.
Sensitive vPSA -- Exploring Sensitivity in Visual Parameter Space Analysis
Venue: arXiv cs.GR 2022
Year: 2022

The sensitivity of parameters in computational science problems is difficult to assess, especially for algorithms with multiple input parameters and diverse outputs. This work seeks to explore sensitivity analysis in the visualization domain, introducing novel techniques for respective visual analyses of parameter sensitivity in multi-dimensional algorithms. First, the sensitivity analysis background is revisited, highlighting the definition of sensitivity analysis and approaches analyzing global and local sensitivity as well as the differences of sensitivity analysis to the more common uncertainty analysis. We introduce and explore parameter sensitivity using visualization techniques from overviews to details on demand, covering the analysis of all aspects of sensitivity in a prototypical implementation. The respective visualization techniques outline the algorithmic in- and outputs including indications, on how sensitive an input is with regard to the outputs. The detailed sensitivity information is communicated through constellation plots for the exploration of input and output spaces. A matrix view is discussed for localized information on the sensitivity of specific outputs to specific inputs. A 3D view provides the link of the parameter sensitivity to the spatial domain, in which the results of the multi-dimensional algorithms are embedded. The proposed sensitivity analysis techniques are implemented and evaluated in a prototype called Sensitivity Explorer. We show that Sensitivity Explorer reliably identifies the most influential parameters and provides insights into which of the output characteristics these affect as well as to which extent.
Quantum materials for energy-efficient neuromorphic computing
Venue: arXiv cs.ET 2022
Year: 2022

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.
Compliance Checking with NLI: Privacy Policies vs. Regulations
Venue: arXiv cs.CL 2022
Year: 2022

A privacy policy is a document that states how a company intends to handle and manage their customers' personal data. One of the problems that arises with these privacy policies is that their content might violate data privacy regulations. Because of the enormous number of privacy policies that exist, the only realistic way to check for legal inconsistencies in all of them is through an automated method. In this work, we use Natural Language Inference (NLI) techniques to compare privacy regulations against sections of privacy policies from a selection of large companies. Our NLI model uses pre-trained embeddings, along with BiLSTM in its attention mechanism. We tried two versions of our model: one that was trained on the Stanford Natural Language Inference (SNLI) and the second on the Multi-Genre Natural Language Inference (MNLI) dataset. We found that our test accuracy was higher on our model trained on the SNLI, but when actually doing NLI tasks on real world privacy policies, the model trained on MNLI generalized and performed much better.
MonoTrack: Shuttle trajectory reconstruction from monocular badminton video
Venue: arXiv cs.CV 2022
Year: 2022

Trajectory estimation is a fundamental component of racket sport analytics, as the trajectory contains information not only about the winning and losing of each point, but also how it was won or lost. In sports such as badminton, players benefit from knowing the full 3D trajectory, as the height of shuttlecock or ball provides valuable tactical information. Unfortunately, 3D reconstruction is a notoriously hard problem, and standard trajectory estimators can only track 2D pixel coordinates. In this work, we present the first complete end-to-end system for the extraction and segmentation of 3D shuttle trajectories from monocular badminton videos. Our system integrates badminton domain knowledge such as court dimension, shot placement, physical laws of motion, along with vision-based features such as player poses and shuttle tracking. We find that significant engineering efforts and model improvements are needed to make the overall system robust, and as a by-product of our work, improve state-of-the-art results on court recognition, 2D trajectory estimation, and hit recognition.
RIS-aided Cooperative FD-SWIPT-NOMA Outage Performance in Nakagami-m Channels
Venue: arXiv cs.IT 2022
Year: 2022

In this work, we investigate reconfigurable intelligent surfaces (RIS)-assisted cooperative non-orthogonal multiple access (C-NOMA) consisting of two paired users, where the phases of RIS are configured to boost the cell-center device. The cell-center device is designated to act as a full-duplex (FD) relay to assist the cell-edge device. The cell-center device does not use its battery energy to cooperate but harvests energy using simultaneous wireless information power transfer (SWIPT). A more practical non-linear energy harvesting model is considered. Expressions for outage probability (OP) and ergodic rate (ER) are devised, assuming that all users' links undergo Nakagami-$m$ channel fading. We first approximate the harvested power as Gamma random variables via the moments matching technique. This allows us to derive analytical OP/ER expressions that are simple to compute yet accurate for a wide range of RIS passive elements configurations, energy harvesting (EH) coefficients, and residual self-interference (SI) levels, being extensively validated by numerical simulations. The OP expressions reveal how paramount is to mitigate the SI in the FD relay mode since for reasonable values of residual SI coefficient ($ω\geq -20$dB), it is notable its detrimental effect on the system performance. Also, numerical results reveal that increasing the number of RIS elements can benefit the cooperative system more than the non-cooperative one.
High-Quality Pluralistic Image Completion via Code Shared VQGAN
Venue: arXiv cs.CV 2022
Year: 2022

PICNet pioneered the generation of multiple and diverse results for image completion task, but it required a careful balance between $\mathcal{KL}$ loss (diversity) and reconstruction loss (quality), resulting in a limited diversity and quality . Separately, iGPT-based architecture has been employed to infer distributions in a discrete space derived from a pixel-level pre-clustered palette, which however cannot generate high-quality results directly. In this work, we present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed. The core of our design lies in a simple yet effective code sharing mechanism that leads to a very compact yet expressive image representation in a discrete latent domain. The compactness and the richness of the representation further facilitate the subsequent deployment of a transformer to effectively learn how to composite and complete a masked image at the discrete code domain. Based on the global context well-captured by the transformer and the available visual regions, we are able to sample all tokens simultaneously, which is completely different from the prevailing autoregressive approach of iGPT-based works, and leads to more than 100$\times$ faster inference speed. Experiments show that our framework is able to learn semantically-rich discrete codes efficiently and robustly, resulting in much better image reconstruction quality. Our diverse image completion framework significantly outperforms the state-of-the-art both quantitatively and qualitatively on multiple benchmark datasets.
PSDoodle: Fast App Screen Search via Partial Screen Doodle
Venue: arXiv cs.SE 2022
Year: 2022

Searching through existing repositories for a specific mobile app screen design is currently either slow or tedious. Such searches are either limited to basic keyword searches (Google Image Search) or require as input a complete query screen image (SWIRE). A promising alternative is interactive partial sketching, which is more structured than keyword search and faster than complete-screen queries. PSDoodle is the first system to allow interactive search of screens via interactive sketching. PSDoodle is built on top of a combination of the Rico repository of some 58k Android app screens, the Google QuickDraw dataset of icon-level doodles, and DoodleUINet, a curated corpus of some 10k app icon doodles collected from hundreds of individuals. In our evaluation with third-party software developers, PSDoodle provided similar top-10 screen retrieval accuracy as the state of the art from the SWIRE line of work, while cutting the average time required about in half.
Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Venue: arXiv cs.CV 2022
Year: 2022

Available 3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring synthetic or in-studio domains, acquiring such supervision for each new target environment is highly inconvenient. To this end, we cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target. We propose to infer image-to-pose via two explicit mappings viz. image-to-latent and latent-to-pose where the latter is a pre-learned decoder obtained from a prior-enforcing generative adversarial auto-encoder. Next, we introduce relation distillation as a means to align the unpaired cross-modal samples i.e. the unpaired target videos and unpaired 3D pose sequences. To this end, we propose a new set of non-local relations in order to characterize long-range latent pose interactions unlike general contrastive relations where positive couplings are limited to a local neighborhood structure. Further, we provide an objective way to quantify non-localness in order to select the most effective relation set. We evaluate different self-adaptation settings and demonstrate state-of-the-art 3D human pose estimation performance on standard benchmarks.
$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation
Venue: arXiv cs.CL 2022
Year: 2022

Recently, parallel text generation has received widespread attention due to its success in generation efficiency. Although many advanced techniques are proposed to improve its generation quality, they still need the help of an autoregressive model for training to overcome the one-to-many multi-modal phenomenon in the dataset, limiting their applications. In this paper, we propose $\textit{latent}$-GLAT, which employs the discrete latent variables to capture word categorical information and invoke an advanced curriculum learning technique, alleviating the multi-modality problem. Experiment results show that our method outperforms strong baselines without the help of an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm.
Detecting Anchors' Opinion in Hinghlish News Delivery
Venue: arXiv cs.CL 2022
Year: 2022

Humans like to express their opinions and crave the opinions of others. Mining and detecting opinions from various sources are beneficial to individuals, organisations, and even governments. One such organisation is news media, where a general norm is not to showcase opinions from their side. Anchors are the face of the digital media, and it is required for them not to be opinionated. However, at times, they diverge from the accepted norm and insert their opinions into otherwise straightforward news reports, either purposefully or unintentionally. This is primarily seen in debates as it requires the anchors to be spontaneous, thus making them vulnerable to add their opinions. The consequence of such mishappening might lead to biased news or even supporting a certain agenda at the worst. To this end, we propose a novel task of anchors' opinion detection in debates. We curate code-mixed news debates and develop the ODIN dataset. A total of 2054 anchors' utterances in the dataset are marked as opinionated or non-opinionated. Lastly, we propose DetONADe, an interactive attention-based framework for classifying anchors' utterances and obtain the best weighted-F1 score of 0.703. A thorough analysis and evaluation show many interesting patterns in the dataset and predictions.
Automating Staged Rollout with Reinforcement Learning
Venue: arXiv cs.SE 2022
Year: 2022

Staged rollout is a strategy of incrementally releasing software updates to portions of the user population in order to accelerate defect discovery without incurring catastrophic outcomes such as system wide outages. Some past studies have examined how to quantify and automate staged rollout, but stop short of simultaneously considering multiple product or process metrics explicitly. This paper demonstrates the potential to automate staged rollout with multi-objective reinforcement learning in order to dynamically balance stakeholder needs such as time to deliver new features and downtime incurred by failures due to latent defects.
How Should Voice Assistants Deal With Users' Emotions?
Venue: arXiv cs.HC 2022
Year: 2022

There is a growing body of research in HCI on detecting the users' emotions. Once it is possible to detect users' emotions reliably, the next question is how an emotion-aware interface should react to the detected emotion. In a first step, we tried to find out how humans deal with the negative emotions of an avatar. The hope behind this approach was to identify human strategies, which we can then mimic in an emotion-aware voice assistant. We present a user study in which participants were confronted with an angry, sad, or frightened avatar. Their task was to make the avatar happy by talking to it. We recorded the voice signal and analyzed it. The results show that users predominantly reacted with neutral emotion. However, we also found gender differences, which opens a range of questions.
Designing a Future Worth Wanting: Applying Virtue Ethics to HCI
Venue: arXiv cs.CY 2022
Year: 2022

Out of the three major approaches to ethics, virtue ethics is uniquely well suited as a moral guide in the digital age, given the pace of sociotechnical change and the complexity of society. Virtue ethics focuses on the traits, situations and actions of moral agents, rather than on rules (as in deontology) or outcomes (consequentialism). Even as interest in ethics has grown within HCI, there has been little engagement with virtue ethics. To address this lacuna and demonstrate further opportunities for ethical design, this paper provides an overview of virtue ethics for application in HCI. It reviews existing HCI work engaging with virtue ethics, provides a primer on virtue ethics to correct widespread misapprehensions within HCI, and presents a deductive literature review illustrating how existing lines of HCI research resonate with the practices of virtue cultivation, paving the way for further work in virtue-oriented design.
All-Pairs Shortest Path Distances with Differential Privacy: Improved Algorithms for Bounded and Unbounded Weights
Venue: arXiv cs.DS 2022
Year: 2022

We revisit the problem of privately releasing the all-pairs shortest path distances of a weighted undirected graph up to low additive error, which was first studied by Sealfon [Sea16]. In this paper, we improve significantly on Sealfon's results, both for arbitrary weighted graphs and for bounded-weight graphs on $n$ nodes. Specifically, we provide an approximate-DP algorithm that outputs all-pairs shortest path distances up to maximum additive error $\tilde{O}(\sqrt{n})$, and a pure-DP algorithm that outputs all pairs shortest path distances up to maximum additive error $\tilde{O}(n^{2/3})$ (where we ignore dependencies on $\varepsilon, δ$). This improves over the previous best result of $\tilde{O}(n)$ additive error for both approximate-DP and pure-DP [Sea16], and partially resolves an open question posed by Sealfon [Sea16, Sea20]. We also show that if the graph is promised to have reasonably bounded weights, one can improve the error further to roughly $n^{\sqrt{2}-1+o(1)}$ in the approximate-DP setting and roughly $n^{(\sqrt{17}-3)/2 + o(1)}$ in the pure-DP setting. Previously, it was only known how to obtain $\tilde{O}(n^{1/2})$ additive error in the approximate-DP setting and $\tilde{O}(n^{2/3})$ additive error in the pure-DP setting for bounded-weight graphs [Sea16].
From Monolith to Microservices: Software Architecture for Autonomous UAV Infrastructure Inspection
Venue: arXiv cs.SE 2022
Year: 2022

Linear-infrastructure Mission Control (LiMiC) is an application for autonomous Unmanned Aerial Vehicle (UAV) infrastructure inspection mission planning developed in monolithic software architecture. The application calculates routes along the infrastructure based on the users' inputs, the number of UAVs participating in the mission, and UAVs' locations. LiMiC1.0 is the latest application version migrated from monolith to microservices, continuously integrated, and deployed using DevOps tools to facilitate future features development, enable better traffic management, and improve the route calculation processing time. Processing time was improved by refactoring the route calculation algorithm into services, scaling them in the Kubernetes cluster, and enabling asynchronous communication in between. In this paper, we discuss the differences between the monolith and microservice architecture to justify our decision for migration. We describe the methodology for the application's migration and implementation processes, technologies we use for continuous integration and deployment, and we present microservices improved performance results compared with the monolithic application.
Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis
Venue: arXiv cs.LG 2022
Year: 2022

Generating sub-optimal synthesis transformation sequences ("synthesis recipe") is an important problem in logic synthesis. Manually crafted synthesis recipes have poor quality. State-of-the art machine learning (ML) works to generate synthesis recipes do not scale to large netlists as the models need to be trained from scratch, for which training data is collected using time consuming synthesis runs. We propose a new approach, Bulls-Eye, that fine-tunes a pre-trained model on past synthesis data to accurately predict the quality of a synthesis recipe for an unseen netlist. This approach on achieves 2x-10x run-time improvement and better quality-of-result (QoR) than state-of-the-art machine learning approaches.
Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimation
Venue: arXiv cs.CV 2022
Year: 2022

Deep-learning-based approaches to depth estimation are rapidly advancing, offering superior performance over existing methods. To estimate the depth in real-world scenarios, depth estimation models require the robustness of various noise environments. In this work, a Pyramid Frequency Network(PFN) with Spatial Attention Residual Refinement Module(SARRM) is proposed to deal with the weak robustness of existing deep-learning methods. To reconstruct depth maps with accurate details, the SARRM constructs a residual fusion method with an attention mechanism to refine the blur depth. The frequency division strategy is designed, and the frequency pyramid network is developed to extract features from multiple frequency bands. With the frequency strategy, PFN achieves better visual accuracy than state-of-the-art methods in both indoor and outdoor scenes on Make3D, KITTI depth, and NYUv2 datasets. Additional experiments on the noisy NYUv2 dataset demonstrate that PFN is more reliable than existing deep-learning methods in high-noise scenes.
Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models
Venue: arXiv cs.RO 2022
Year: 2022

In most classical Autonomous Vehicle (AV) stacks, the prediction and planning layers are separated, limiting the planner to react to predictions that are not informed by the planned trajectory of the AV. This work presents a module that tightly couples these layers via a game-theoretic Model Predictive Controller (MPC) that uses a novel interactive multi-agent neural network policy as part of its predictive model. In our setting, the MPC planner considers all the surrounding agents by informing the multi-agent policy with the planned state sequence. Fundamental to the success of our method is the design of a novel multi-agent policy network that can steer a vehicle given the state of the surrounding agents and the map information. The policy network is trained implicitly with ground-truth observation data using backpropagation through time and a differentiable dynamics model to roll out the trajectory forward in time. Finally, we show that our multi-agent policy network learns to drive while interacting with the environment, and, when combined with the game-theoretic MPC planner, can successfully generate interactive behaviors.