OpenRaiser/Intern-Atlas huggingface dataset:
Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder
Venue: arXiv cs.LG 2022
Year: 2022

Adversarial learning is a widely used technique in fair representation learning to remove the biases on sensitive attributes from data representations. It usually requires to incorporate the sensitive attribute labels as prediction targets. However, in many scenarios the sensitive attribute labels of many samples can be unknown, and it is difficult to train a strong discriminator based on the scarce data with observed attribute labels, which may lead to generate unfair representations. In this paper, we propose a semi-supervised fair representation learning approach based on adversarial variational autoencoder, which can reduce the dependency of adversarial fair models on data with labeled sensitive attributes. More specifically, we use a bias-aware model to capture inherent bias information on sensitive attribute by accurately predicting sensitive attributes from input data, and we use a bias-free model to learn debiased fair representations by using adversarial learning to remove bias information from them. The hidden representations learned by the two models are regularized to be orthogonal. In addition, the soft labels predicted by the two models are further integrated into a semi-supervised variational autoencoder to reconstruct the input data, and we apply an additional entropy regularization to encourage the attribute labels inferred from the bias-free model to be high-entropy. In this way, the bias-aware model can better capture attribute information while the bias-free model is less discriminative on sensitive attributes if the input data is well reconstructed. Extensive experiments on two datasets for different tasks validate that our approach can achieve good representation learning fairness under limited data with sensitive attribute labels.
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression
Venue: arXiv cs.CL 2022
Year: 2022

This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases for both subtasks A and B. Using a pre-trained transformer as a backbone, the model targeted the task of multi-task classification and ranking in the context of finding the best fillers for a cloze task related to instructional texts on the website Wikihow. The system employed a combination of two ordinal regression components to tackle this task in a multi-task learning scenario. According to the official leaderboard of the shared task, this system was ranked 5th in the ranking and 7th in the classification subtasks out of 21 participating teams. With additional experiments, the models have since been further optimised.
What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy
Venue: arXiv cs.AI 2022
Year: 2022

Auxiliary tasks have been argued to be useful for representation learning in reinforcement learning. Although many auxiliary tasks have been empirically shown to be effective for accelerating learning on the main task, it is not yet clear what makes useful auxiliary tasks. Some of the most promising results are on the pixel control, reward prediction, and the next state prediction auxiliary tasks; however, the empirical results are mixed, showing substantial improvements in some cases and marginal improvements in others. Careful investigations of how auxiliary tasks help the learning of the main task is necessary. In this paper, we take a step studying the effect of the target policies on the usefulness of the auxiliary tasks formulated as general value functions. General value functions consist of three core elements: 1) policy 2) cumulant 3) continuation function. Our focus on the role of the target policy of the auxiliary tasks is motivated by the fact that the target policy determines the behavior about which the agent wants to make a prediction and the state-action distribution that the agent is trained on, which further affects the main task learning. Our study provides insights about questions such as: Does a greedy policy result in bigger improvement gains compared to other policies? Is it best to set the auxiliary task policy to be the same as the main task policy? Does the choice of the target policy have a substantial effect on the achieved performance gain or simple strategies for setting the policy, such as using a uniformly random policy, work as well? Our empirical results suggest that: 1) Auxiliary tasks with the greedy policy tend to be useful. 2) Most policies, including a uniformly random policy, tend to improve over the baseline. 3) Surprisingly, the main task policy tends to be less useful compared to other policies.
Multi-Agent Path Planning with Asymmetric Interactions In Tight Spaces
Venue: arXiv cs.RO 2022
Year: 2022

By starting with the assumption that motion is fundamentally a decision making problem, we use the world-line concept from Special Relativity as the inspiration for a novel multi-agent path planning method. We have identified a particular set of problems that have so far been overlooked by previous works. We present our solution for the global path planning problem for each agent and ensure smooth local collision avoidance for each pair of agents in the scene. We accomplish this by modeling the trajectories of the agents through 2D space and time as curves in 3D. Global path planning is solved using a modified Djikstra's algorithm to ensure that initial trajectories for agents do not intersect. We then solve for smooth local trajectories using a quasi-Newton interior point solver, providing the trajectory curves with a radius to turn them into rods. Subsequently, resolving collision of the rods ensures that no two agents are in the same spatial position at the same time. This space-time formulation allows us to simulate previously ignored phenomena such as highly asymmetric interactions in very constrained environments. It also provides a solution for scenes with unnaturally symmetric agent alignments without the need for jittering agent positions or velocities.
Traversability, Reconfiguration, and Reachability in the Gadget Framework
Venue: arXiv cs.CC 2022
Year: 2022

Consider an agent traversing a graph of "gadgets", each with local state that changes with each traversal by the agent. We characterize the complexity of universal traversal, where the goal is to traverse every gadget at least once, for DAG gadgets, one-state gadgets, and reversible deterministic gadgets. We also study the complexity of reconfiguration, where the goal is to bring the system of gadgets to a specified state, proving many cases PSPACE-complete, and showing in some cases that reconfiguration can be strictly harder than reachability (where the goal is for the agent to reach a specified location), while in other cases, reachability is strictly harder than reconfiguration.
Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we propose an explainable and interpretable diabetic retinopathy (ExplainDR) classification model based on neural-symbolic learning. To gain explainability, a highlevel symbolic representation should be considered in decision making. Specifically, we introduce a human-readable symbolic representation, which follows a taxonomy style of diabetic retinopathy characteristics related to eye health conditions to achieve explainability. We then include humanreadable features obtained from the symbolic representation in the disease prediction. Experimental results on a diabetic retinopathy classification dataset show that our proposed ExplainDR method exhibits promising performance when compared to that from state-of-the-art methods applied to the IDRiD dataset, while also providing interpretability and explainability.
An ICT based Solution for Virtual Garment Fitting for Online Market Place: A Review of Related Literature
Venue: arXiv cs.HC 2022
Year: 2022

In this paper, we describe various technologies that are being used in virtual garment fitting and simulation. There, we have focused about the usage of anthropometry in clothing industry and avatar generation of virtual garment fitting. Most commonly used technologies for avatar generation in virtual environment have been discussed in this paper such as generic body model and laser scanning. Moreover, this paper includes the real-time tracking technologies used in virtual garment fitting like markers and depth cameras in various related researches as well as how the virtual cloth generation and simulation carried out in the related researches. Apart from these, virtual clothing methods such as geometrical, physical and hybrid based models were also discussed in this paper. As ease allowance has a major impact on virtual cloth fitting, it is also considered in this paper related to similar researches. Within this paper, all the above mentioned areas were described thoroughly while stating the existing gap of the virtual garment fitting in online marketplaces.
Double-Hashing Algorithm for Frequency Estimation in Data Streams
Venue: arXiv cs.DS 2022
Year: 2022

Frequency estimation of elements is an important task for summarizing data streams and machine learning applications. The problem is often addressed by using streaming algorithms with sublinear space data structures. These algorithms allow processing of large data while using limited data storage. Commonly used streaming algorithms, such as count-min sketch, have many advantages, but do not take into account properties of a data stream for performance optimization. In the present paper we introduce a novel double-hashing algorithm that provides flexibility to optimize streaming algorithms depending on the properties of a given stream. In the double-hashing approach, first a standard streaming algorithm is employed to obtain an estimate of the element frequencies. This estimate is derived using a fraction of the stream and allows identification of the heavy hitters. Next, it uses a modified hash table where the heavy hitters are mapped into individual buckets and other stream elements are mapped into the remaining buckets. Finally, the element frequencies are estimated based on the constructed hash table over the entire data stream with any streaming algorithm. We demonstrate on both synthetic data and an internet query log dataset that our approach is capable of improving frequency estimation due to removing heavy hitters from the hashing process and, thus, reducing collisions in the hash table. Our approach avoids employing additional machine learning models to identify heavy hitters and, thus, reduces algorithm complexity and streamlines implementation. Moreover, because it is not dependent on specific features of the stream elements for identifying heavy hitters, it is applicable to a large variety of streams. In addition, we propose a procedure on how to dynamically adjust the proposed double-hashing algorithm when frequencies of the elements in a stream are changing over time.
Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control Policies via Hybrid Control
Venue: arXiv cs.LG 2022
Year: 2022

Reinforcement learning (RL) is a promising approach for deriving control policies for complex systems. As we show in two control problems, the derived policies from using the Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) algorithms may lack robustness guarantees. Motivated by these issues, we propose a new hybrid algorithm, which we call Hysteresis-Based RL (HyRL), augmenting an existing RL algorithm with hysteresis switching and two stages of learning. We illustrate its properties in two examples for which PPO and DQN fail.
Human-AI Interaction for User Safety in Social Matching Apps: Involving Marginalized Users in Design
Venue: arXiv cs.HC 2022
Year: 2022

In this position paper we intend to advocate for participatory design methods and mobile social matching apps as ripe contexts for exploring novel human-AI interactions that benefit marginalized groups. Mobile social matching apps like Tinder and Bumble use AI to introduce users to each other for rapid face-to-face meetings. These user discoveries and subsequent interactions pose disproportionate risk of sexual violence and other harms to marginalized user demographics, specifically women and the LGBTQIA+ community. We want to extend the role of AI in these apps to keep users safe while they interact with strangers across online and offline modalities. To do this, we are using participatory design methods to empower women and LGBTQIA+ individuals to envision future human-AI interactions that prioritize their safety during social matching app-use. In one study, stakeholders identifying as LGBTQIA+ or women are redesigning dating apps to mediate exchange of sexual consent and therefore mitigate sexual violence. In the other study, women are designing multi-purpose, opportunistic social matching apps that foreground women's safety.
Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review
Venue: arXiv cs.HC 2022
Year: 2022

With the rising prevalence of autism diagnoses, it is essential for research to understand how to leverage technology to support the diverse nature of autistic traits. While traditional interventions focused on technology for medical cure and rehabilitation, recent research aims to understand how technology can accommodate each unique situation in an efficient and engaging way. Extended reality (XR) technology has been shown to be effective in improving attention in autistic users given that it is more engaging and motivating than other traditional mediums. Here, we conducted a systematic review of 59 research articles that explored the role of attention in XR interventions for autistic users. We systematically analyzed demographics, study design and findings, including autism screening and attention measurement methods. Furthermore, given methodological inconsistencies in the literature, we systematically synthesize methods and protocols including screening tools, physiological and behavioral cues of autism and XR tasks. While there is substantial evidence for the effectiveness of using XR in attention-based interventions for autism to support autistic traits, we have identified three principal research gaps that provide promising research directions to examine how autistic populations interact with XR. First, our findings highlight the disproportionate geographic locations of autism studies and underrepresentation of autistic adults, evidence of gender disparity, and presence of individuals diagnosed with co-occurring conditions across studies. Second, many studies used an assortment of standardized and novel tasks and self-report assessments with limited tested reliability. Lastly, the research lacks evidence of performance maintenance and transferability.
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning
Venue: arXiv cs.CV 2022
Year: 2022

Person tracking using computer vision techniques has wide ranging applications such as autonomous driving, home security and sports analytics. However, the growing threat of adversarial attacks raises serious concerns regarding the security and reliability of such techniques. In this work, we study the impact of multi-task learning (MTL) on the adversarial robustness of the widely used SiamRPN tracker, in the context of person tracking. Specifically, we investigate the effect of jointly learning with semantically analogous tasks of person tracking and human keypoint detection. We conduct extensive experiments with more powerful adversarial attacks that can be physically realizable, demonstrating the practical value of our approach. Our empirical study with simulated as well as real-world datasets reveals that training with MTL consistently makes it harder to attack the SiamRPN tracker, compared to typically training only on the single task of person tracking.
A Study of Real-World Data Races in Golang
Venue: arXiv cs.DC 2022
Year: 2022

The concurrent programming literature is rich with tools and techniques for data race detection. Less, however, has been known about real-world, industry-scale deployment, experience, and insights about data races. Golang (Go for short) is a modern programming language that makes concurrency a first-class citizen. Go offers both message passing and shared memory for communicating among concurrent threads. Go is gaining popularity in modern microservice-based systems. Data races in Go stand in the face of its emerging popularity. In this paper, using our industrial codebase as an example, we demonstrate that Go developers embrace concurrency and show how the abundance of concurrency alongside language idioms and nuances make Go programs highly susceptible to data races. Google's Go distribution ships with a built-in dynamic data race detector based on ThreadSanitizer. However, dynamic race detectors pose scalability and flakiness challenges; we discuss various software engineering trade-offs to make this detector work effectively at scale. We have deployed this detector in Uber's 46 million lines of Go codebase hosting 2100 distinct microservices, found over 2000 data races, and fixed over 1000 data races, spanning 790 distinct code patches submitted by 210 unique developers over a six-month period. Based on a detailed investigation of these data race patterns in Go, we make seven high-level observations relating to the complex interplay between the Go language paradigm and data races.
Energy Efficient HARQ for Ultrareliability via Novel Outage Probability Bound and Geometric Programming
Venue: arXiv cs.IT 2022
Year: 2022

Hybrid automatic repeat 1 request (HARQ) is a key enabler for ultrareliable communications. This paper optimizes transmit power for the initial transmission and the subsequent retransmissions of HARQ with either incremental redundancy or Chase combining, aiming to minimize the expected energy consumption given the target outage probability and the target latency. The main challenge is due to the fact that the outage probability is a complicated function of the power variables which are nested in successive convolutions. The existing works mostly use a classic upper bound to approximate the outage probability by assuming unbounded transmit power, then convert the original problem to a geometric programming (GP) problem. In contrast, we propose a novel and much tighter upper bound by taking the practical power limit into consideration. The new bound and the resulting new GP method are further extended to a broader group of channel models with various fading, multiple antennas, and multiple receivers. As shown in simulations, the GP method based on the new bound significantly outperforms the existing strategies that either fix transmit power or optimize power by the classic bounding technique.
Leveraging Phone Mask Training for Phonetic-Reduction-Robust E2E Uyghur Speech Recognition
Venue: arXiv cs.SD 2022
Year: 2022

In Uyghur speech, consonant and vowel reduction are often encountered, especially in spontaneous speech with high speech rate, which will cause a degradation of speech recognition performance. To solve this problem, we propose an effective phone mask training method for Conformer-based Uyghur end-to-end (E2E) speech recognition. The idea is to randomly mask off a certain percentage features of phones during model training, which simulates the above verbal phenomena and facilitates E2E model to learn more contextual information. According to experiments, the above issues can be greatly alleviated. In addition, deep investigations are carried out into different units in masking, which shows the effectiveness of our proposed masking unit. We also further study the masking method and optimize filling strategy of phone mask. Finally, compared with Conformer-based E2E baseline without mask training, our model demonstrates about 5.51% relative Word Error Rate (WER) reduction on reading speech and 12.92% on spontaneous speech, respectively. The above approach has also been verified on test-set of open-source data THUYG-20, which shows 20% relative improvements.
Efficient comparison of sentence embeddings
Venue: arXiv cs.CL 2022
Year: 2022

The domain of natural language processing (NLP), which has greatly evolved over the last years, has highly benefited from the recent developments in word and sentence embeddings. Such embeddings enable the transformation of complex NLP tasks, like semantic similarity or Question and Answering (Q&A), into much simpler to perform vector comparisons. However, such a problem transformation raises new challenges like the efficient comparison of embeddings and their manipulation. In this work, we will discuss about various word and sentence embeddings algorithms, we will select a sentence embedding algorithm, BERT, as our algorithm of choice and we will evaluate the performance of two vector comparison approaches, FAISS and Elasticsearch, in the specific problem of sentence embeddings. According to the results, FAISS outperforms Elasticsearch when used in a centralized environment with only one node, especially when big datasets are included.
Find Another Me Across the World -- Large-scale Semantic Trajectory Analysis Using Spark
Venue: arXiv cs.DC 2022
Year: 2022

In today's society, location-based services are widely used which collect a huge amount of human trajectories. Analyzing semantic meanings of these trajectories can benefit numerous real-world applications, such as product advertisement, friend recommendation, and social behavior analysis. However, existing works on semantic trajectories are mostly centralized approaches that are not able to keep up with the rapidly growing trajectory collections. In this paper, we propose a novel large-scale semantic trajectory analysis algorithm in Apache Spark. We design a new hash function along with efficient distributed algorithms that can quickly compute semantic trajectory similarities and identify communities of people with similar behavior across the world. The experimental results show that our approach is more than 30 times faster than centralized approaches without sacrificing any accuracy like other parallel approaches.
SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications
Venue: arXiv cs.DL 2022
Year: 2022

Classifying scientific publications according to Field-of-Science (FoS) taxonomies is of crucial importance, allowing funders, publishers, scholars, companies and other stakeholders to organize scientific literature more effectively. Most existing works address classification either at venue level or solely based on the textual content of a research publication. We present SciNoBo, a novel classification system of publications to predefined FoS taxonomies, leveraging the structural properties of a publication and its citations and references organised in a multilayer network. In contrast to other works, our system supports assignments of publications to multiple fields by considering their multidisciplinarity potential. By unifying publications and venues under a common multilayer network structure made up of citing and publishing relationships, classifications at the venue-level can be augmented with publication-level classifications. We evaluate SciNoBo on a publications' dataset extracted from Microsoft Academic Graph and we perform a comparative analysis against a state-of-the-art neural-network baseline. The results reveal that our proposed system is capable of producing high-quality classifications of publications.