OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Template Initialization for Part-Aware Person Re-ID
Venue: arXiv cs.CV 2022
Year: 2022

Many of the existing Person Re-identification (Re-ID) approaches depend on feature maps which are either partitioned to localize parts of a person or reduced to create a global representation. While part localization has shown significant success, it uses either naıve position-based partitions or static feature templates. These, however, hypothesize the pre-existence of the parts in a given image or their positions, ignoring the input image-specific information which limits their usability in challenging scenarios such as Re-ID with partial occlusions and partial probe images. In this paper, we introduce a spatial attention-based Dynamic Part Template Initialization module that dynamically generates part-templates using mid-level semantic features at the earlier layers of the backbone. Following a self-attention layer, human part-level features of the backbone are used to extract the templates of diverse human body parts using a simplified cross-attention scheme which will then be used to identify and collate representations of various human parts from semantically rich features, increasing the discriminative ability of the entire model. We further explore adaptive weighting of part descriptors to quantify the absence or occlusion of local attributes and suppress the contribution of the corresponding part descriptors to the matching criteria. Extensive experiments on holistic, occluded, and partial Re-ID task benchmarks demonstrate that our proposed architecture is able to achieve competitive performance. Codes will be included in the supplementary material and will be made publicly available.
A Plural Decentralized Identity Frontier: Abstraction v. Composability Tradeoffs in Web3
Venue: arXiv cs.CR 2022
Year: 2022

In this article, we explore the tension between abstraction and composability in web3 today, specifically within identity solutions, and argue that the current standard DID v1.0 is sufficiently under specified, allowing for many methods and instantiations, including blockchain based certificates. We view experiments today in web3 identity as additive and complementary, and argue that often cited differences are of degree and more in form, less in substance. By way of illustration, we compare decentralized naming services and blockchain based identity certificates such as soulbound tokens (SBTs) to decentralized identifiers (DIDs) and verifiable credentials (VCs). Both paradigms, to the extent they can be meaningfully differentiated, share similar potential as well as challenges. Specifically, we refer to fears about non consensual verification (scarlet letters) and show DID method iterations are not immune by issuing an innocuous public scarlet letter to a DIDs associated public address for anyone to see. Moreover, we argue that because SBTs are unspecified, one could characterize SBTs as an iteration, or extension, of VCs that additionally aspire to achieve composability with web3 smart contracts for correct execution of code, privacy, coercion resistance, and censorship resistance. We offer research paths for how VCs can also achieve these properties. We do not comment on cost, scalability, transferability, or common knowledge as they have been previously reviewed.
CheapET-3: Cost-Efficient Use of Remote DNN Models
Venue: arXiv cs.SE 2022
Year: 2022

On complex problems, state of the art prediction accuracy of Deep Neural Networks (DNN) can be achieved using very large-scale models, consisting of billions of parameters. Such models can only be run on dedicated servers, typically provided by a 3rd party service, which leads to a substantial monetary cost for every prediction. We propose a new software architecture for client-side applications, where a small local DNN is used alongside a remote large-scale model, aiming to make easy predictions locally at negligible monetary cost, while still leveraging the benefits of a large model for challenging inputs. In a proof of concept we reduce prediction cost by up to 50% without negatively impacting system accuracy.
Knowledge-based and Data-driven Reasoning and Learning for Ad Hoc Teamwork
Venue: arXiv cs.AI 2022
Year: 2022

We present an architecture for ad hoc teamwork, which refers to collaboration in a team of agents without prior coordination. State of the art methods for this problem often include a data-driven component that uses a long history of prior observations to model the behaviour of other agents (or agent types) and to determine the ad hoc agent's behaviour. In many practical domains, it is challenging to find large training datasets, and necessary to understand and incrementally extend the existing models to account for changes in team composition or domain attributes. Our architecture combines the principles of knowledge-based and data-driven reasoning and learning. Specifically, we enable an ad hoc agent to perform non-monotonic logical reasoning with prior commonsense domain knowledge and incrementally-updated simple predictive models of other agents' behaviour. We use the benchmark simulated multi-agent collaboration domain Fort Attack to demonstrate that our architecture supports adaptation to unforeseen changes, incremental learning and revision of models of other agents' behaviour from limited samples, transparency in the ad hoc agent's decision making, and better performance than a data-driven baseline.
Active Gaze Control for Foveal Scene Exploration
Venue: arXiv cs.CV 2022
Year: 2022

Active perception and foveal vision are the foundations of the human visual system. While foveal vision reduces the amount of information to process during a gaze fixation, active perception will change the gaze direction to the most promising parts of the visual field. We propose a methodology to emulate how humans and robots with foveal cameras would explore a scene, identifying the objects present in their surroundings with in least number of gaze shifts. Our approach is based on three key methods. First, we take an off-the-shelf deep object detector, pre-trained on a large dataset of regular images, and calibrate the classification outputs to the case of foveated images. Second, a body-centered semantic map, encoding the objects classifications and corresponding uncertainties, is sequentially updated with the calibrated detections, considering several data fusion techniques. Third, the next best gaze fixation point is determined based on information-theoretic metrics that aim at minimizing the overall expected uncertainty of the semantic map. When compared to the random selection of next gaze shifts, the proposed method achieves an increase in detection F1-score of 2-3 percentage points for the same number of gaze shifts and reduces to one third the number of required gaze shifts to attain similar performance.
Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation
Venue: arXiv cs.IR 2022
Year: 2022

Deep neural networks can capture the intricate interaction history information between queries and documents, because of their many complicated nonlinear units, allowing them to provide correct search recommendations. However, service providers frequently face more complex obstacles in real-world circumstances, such as deployment cost constraints and fairness requirements. Knowledge distillation, which transfers the knowledge of a well-trained complex model (teacher) to a simple model (student), has been proposed to alleviate the former concern, but the best current distillation methods focus only on how to make the student model imitate the predictions of the teacher model. To better facilitate the application of deep models, we propose a fair information retrieval framework based on knowledge distillation. This framework can improve the exposure-based fairness of models while considerably decreasing model size. Our extensive experiments on three huge datasets show that our proposed framework can reduce the model size to a minimum of 1% of its original size while maintaining its black-box state. It also improves fairness performance by 15%~46% while keeping a high level of recommendation effectiveness.
Repair Is Nearly Generation: Multilingual Program Repair with LLMs
Venue: arXiv cs.SE 2022
Year: 2022

Most programmers make mistakes when writing code. Some of these mistakes are small and require few edits to the original program -- a class of errors recently termed last mile mistakes. These errors break the flow for experienced developers and can stump novice programmers. Existing automated repair techniques targeting this class of errors are language-specific and do not easily carry over to new languages. Transferring symbolic approaches requires substantial engineering and neural approaches require data and retraining. We introduce RING, a multilingual repair engine powered by a large language model trained on code (LLMC) such as Codex. Such a multilingual engine enables a flipped model for programming assistance, one where the programmer writes code and the AI assistance suggests fixes, compared to traditional code suggestion technology. Taking inspiration from the way programmers manually fix bugs, we show that a prompt-based strategy that conceptualizes repair as localization, transformation, and candidate ranking, can successfully repair programs in multiple languages with minimal effort. We present the first results for such a multilingual repair engine by evaluating on 6 different languages and comparing performance to language-specific repair engines. We show that RING can outperform language-specific repair engines for three of these languages.
Constraint-driven multi-task learning
Venue: arXiv cs.LG 2022
Year: 2022

Inductive logic programming is a form of machine learning based on mathematical logic that generates logic programs from given examples and background knowledge. In this project, we extend the Popper ILP system to make use of multi-task learning. We implement the state-of-the-art approach and several new strategies to improve search performance. Furthermore, we introduce constraint preservation, a technique that improves overall performance for all approaches. Constraint preservation allows the system to transfer knowledge between updates on the background knowledge set. Consequently, we reduce the amount of repeated work performed by the system. Additionally, constraint preservation allows us to transition from the current state-of-the-art iterative deepening search approach to a more efficient breadth first search approach. Finally, we experiment with curriculum learning techniques and show their potential benefit to the field.
EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis
Venue: arXiv cs.LG 2022
Year: 2022

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). However, during the COVID-19 pandemic, data collection and analysis could be more challenging. The remote experiment during the pandemic yields several challenges, and we discuss the possible solutions. This paper explores machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. The results show that Random Forest and RBF SVM perform well for EEG classification tasks. Furthermore, we investigate how to conduct such BCI experiments using affordable consumer-grade devices to collect EEG-based BCI data. In addition, we have developed the data collection protocol, EEG4Students, that grants non-experts who are interested in a guideline for such data collection. Our code and data can be found at https://github.com/GuangyaoDou/EEG4Students.
Enforcing Delayed-Impact Fairness Guarantees
Venue: arXiv cs.LG 2022
Year: 2022

Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on peoples' lives or well-being (e.g., applications involving education, employment, and lending), can inadvertently increase social inequality in the long term. This is because prior fairness-aware algorithms only consider static fairness constraints, such as equal opportunity or demographic parity. However, enforcing constraints of this type may result in models that have negative long-term impact on disadvantaged individuals and communities. We introduce ELF (Enforcing Long-term Fairness), the first classification algorithm that provides high-confidence fairness guarantees in terms of long-term, or delayed, impact. We prove that the probability that ELF returns an unfair solution is less than a user-specified tolerance and that (under mild assumptions), given sufficient training data, ELF is able to find and return a fair solution if one exists. We show experimentally that our algorithm can successfully mitigate long-term unfairness.
IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages
Venue: arXiv cs.CL 2022
Year: 2022

A cornerstone in AI research has been the creation and adoption of standardized training and test datasets to earmark the progress of state-of-the-art models. A particularly successful example is the GLUE dataset for training and evaluating Natural Language Understanding (NLU) models for English. The large body of research around self-supervised BERT-based language models revolved around performance improvements on NLU tasks in GLUE. To evaluate language models in other languages, several language-specific GLUE datasets were created. The area of speech language understanding (SLU) has followed a similar trajectory. The success of large self-supervised models such as wav2vec2 enable creation of speech models with relatively easy to access unlabelled data. These models can then be evaluated on SLU tasks, such as the SUPERB benchmark. In this work, we extend this to Indic languages by releasing the IndicSUPERB benchmark. Specifically, we make the following three contributions. (i) We collect Kathbath containing 1,684 hours of labelled speech data across 12 Indian languages from 1,218 contributors located in 203 districts in India. (ii) Using Kathbath, we create benchmarks across 6 speech tasks: Automatic Speech Recognition, Speaker Verification, Speaker Identification (mono/multi), Language Identification, Query By Example, and Keyword Spotting for 12 languages. (iii) On the released benchmarks, we train and evaluate different self-supervised models alongside a commonly used baseline FBANK. We show that language-specific fine-tuned models are more accurate than baseline on most of the tasks, including a large gap of 76\% for the Language Identification task. However, for speaker identification, self-supervised models trained on large datasets demonstrate an advantage. We hope IndicSUPERB contributes to the progress of developing speech language understanding models for Indian languages.
Comprehensive Dataset of Face Manipulations for Development and Evaluation of Forensic Tools
Venue: arXiv cs.CV 2022
Year: 2022

Digital media (e.g., photographs, video) can be easily created, edited, and shared. Tools for editing digital media are capable of doing so while also maintaining a high degree of photo-realism. While many types of edits to digital media are generally benign, others can also be applied for malicious purposes. State-of-the-art face editing tools and software can, for example, artificially make a person appear to be smiling at an inopportune time, or depict authority figures as frail and tired in order to discredit individuals. Given the increasing ease of editing digital media and the potential risks from misuse, a substantial amount of effort has gone into media forensics. To this end, we created a challenge dataset of edited facial images to assist the research community in developing novel approaches to address and classify the authenticity of digital media. Our dataset includes edits applied to controlled, portrait-style frontal face images and full-scene in-the-wild images that may include multiple (i.e., more than one) face per image. The goals of our dataset is to address the following challenge questions: (1) Can we determine the authenticity of a given image (edit detection)? (2) If an image has been edited, can we \textit{localize} the edit region? (3) If an image has been edited, can we deduce (classify) what edit type was performed? The majority of research in image forensics generally attempts to answer item (1), detection. To the best of our knowledge, there are no formal datasets specifically curated to evaluate items (2) and (3), localization and classification, respectively. Our hope is that our prepared evaluation protocol will assist researchers in improving the state-of-the-art in image forensics as they pertain to these challenges.
A Simpler Proof that Pairing Heaps Take O(1) Amortized Time per Insertion
Venue: arXiv cs.DS 2022
Year: 2022

The pairing heap is a simple "self-adjusting" implementation of a heap (priority queue). Inserting an item into a pairing heap or decreasing the key of an item takes O(1) time worst-case, as does melding two heaps. But deleting an item of minimum key can take time linear in the heap size in the worst case. The paper that introduced the pairing heap proved an O(log n) amortized time bound for each heap operation, where n is the number of items in the heap or heaps involved in the operation, by charging all but O(log n) of the time for each deletion to non-deletion operations, O(log n) to each. Later Iacono found a way to reduce the amortized time per insertion to O(1) and that of meld to zero while preserving the O(log n) amortized time bound for the other update operations. We give a simpler proof of Iacono's result with significantly smaller constant factors. Our analysis uses the natural representation of pairing heaps instead of the conversion to a binary tree used in the original analysis and in Iacono's.
Skeleton Prototype Contrastive Learning with Multi-Level Graph Relation Modeling for Unsupervised Person Re-Identification
Venue: arXiv cs.CV 2022
Year: 2022

Person re-identification (re-ID) via 3D skeletons is an important emerging topic with many merits. Existing solutions rarely explore valuable body-component relations in skeletal structure or motion, and they typically lack the ability to learn general representations with unlabeled skeleton data for person re-ID. This paper proposes a generic unsupervised Skeleton Prototype Contrastive learning paradigm with Multi-level Graph Relation learning (SPC-MGR) to learn effective representations from unlabeled skeletons to perform person re-ID. Specifically, we first construct unified multi-level skeleton graphs to fully model body structure within skeletons. Then we propose a multi-head structural relation layer to comprehensively capture relations of physically-connected body-component nodes in graphs. A full-level collaborative relation layer is exploited to infer collaboration between motion-related body parts at various levels, so as to capture rich body features and recognizable walking patterns. Lastly, we propose a skeleton prototype contrastive learning scheme that clusters feature-correlative instances of unlabeled graph representations and contrasts their inherent similarity with representative skeleton features ("skeleton prototypes") to learn discriminative skeleton representations for person re-ID. Empirical evaluations show that SPC-MGR significantly outperforms several state-of-the-art skeleton-based methods, and it also achieves highly competitive person re-ID performance for more general scenarios.
Parallel Path Progression DAG Scheduling
Venue: arXiv cs.DC 2022
Year: 2022

To satisfy the increasing performance needs of modern cyber-physical systems, multiprocessor architectures are increasingly utilized. To efficiently exploit their potential parallelism in hard real-time systems, appropriate task models and scheduling algorithms that allow providing timing guarantees are required. Such scheduling algorithms and the corresponding worst-case response time analyses usually suffer from resource over-provisioning due to pessimistic analyses based on worst-case assumptions. Hence, scheduling algorithms and analysis with high resource efficiency are required. A prominent parallel task model is the directed-acyclic-graph (DAG) task model, where precedence constrained subjobs express parallelism. This paper studies the real-time scheduling problem of sporadic arbitrary-deadline DAG tasks. We propose a path parallel progression scheduling property with only two distinct subtask priorities, which allows to track the execution of a collection of paths simultaneously. This novel approach significantly improves the state-of-the-art response time analyses for parallel DAG tasks for highly parallel DAG structures. Two hierarchical scheduling algorithms are designed based on this property, extending the parallel path progression properties and improving the response time analysis for sporadic arbitrary-deadline DAG task sets.
Elly: A Real-Time Failure Recovery and Data Collection System for Robotic Manipulation
Venue: arXiv cs.RO 2022
Year: 2022

Even the most robust autonomous behaviors can fail. The goal of this research is to both recover and collect data from failures, during autonomous task execution, so they can be prevented in the future. We propose haptic intervention for real-time failure recovery and data collection. Elly is a system that allows for seamless transitions between autonomous robot behaviors and human intervention while collecting sensory information from the human's recovery strategy. The system and our design choices were experimentally validated on a single arm task -- installing a lightbulb in a socket -- and a bimanual task -- screwing a cap on a bottle -- using two 7-DOF manipulators equipped 4-finger grippers. In these examples, Elly achieved over 80% task completion during a total of 40 runs.
On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations
Venue: arXiv cs.CR 2022
Year: 2022

In this work, we consider a federated learning model in a wireless system with multiple base stations and inter-cell interference. We apply a differential private scheme to transmit information from users to their corresponding base station during the learning phase. We show the convergence behavior of the learning process by deriving an upper bound on its optimality gap. Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage. To find the locally optimal solutions of this problem, we first propose an algorithm that schedules the resource blocks and users. We then extend this scheme to reduce the total privacy leakage by optimizing the differential privacy artificial noise. We apply the solutions of these two procedures as parameters of a federated learning system. In this setting, we assume that each user is equipped with a classifier. Moreover, the communication cells are assumed to have mostly fewer resource blocks than numbers of users. The simulation results show that our proposed scheduler improves the average accuracy of the predictions compared with a random scheduler. Furthermore, its extended version with noise optimizer significantly reduces the amount of privacy leakage.