OpenRaiser/Intern-Atlas huggingface dataset:
Measuring uncertainty in human visual segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human segmentation remains unclear, partially because we lack well-controlled paradigms to measure perceptual segmentation maps and compare models quantitatively. Here we propose a new, integrated approach: given an image, we measure multiple pixel-based same--different judgments and perform model--based reconstruction of the underlying segmentation map. The reconstruction is robust to several experimental manipulations and captures the variability of individual participants. We demonstrate the validity of the approach on human segmentation of natural images and composite textures. We show that image uncertainty affects measured human variability, and it influences how participants weigh different visual features. Because any putative segmentation algorithm can be inserted to perform the reconstruction, our paradigm affords quantitative tests of theories of perception as well as new benchmarks for segmentation algorithms.
FE-TCM: Filter-Enhanced Transformer Click Model for Web Search
Venue: arXiv cs.IR 2023
Year: 2023

Constructing click models and extracting implicit relevance feedback information from the interaction between users and search engines are very important to improve the ranking of search results. Using neural network to model users' click behaviors has become one of the effective methods to construct click models. In this paper, We use Transformer as the backbone network of feature extraction, add filter layer innovatively, and propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search. Firstly, in order to reduce the influence of noise on user behavior data, we use the learnable filters to filter log noise. Secondly, following the examination hypothesis, we model the attraction estimator and examination predictor respectively to output the attractiveness scores and examination probabilities. A novel transformer model is used to learn the deeper representation among different features. Finally, we apply the combination functions to integrate attractiveness scores and examination probabilities into the click prediction. From our experiments on two real-world session datasets, it is proved that FE-TCM outperforms the existing click models for the click prediction.
Autonomous Material Composite Morphing Wing
Venue: arXiv cs.RO 2023
Year: 2023

Aeronautics research has continually sought to achieve the adaptability and morphing performance of avian wings, but in practice, wings of all scales continue to use the same hinged control-surface embodiment. Recent research into compliant and bio-inspired mechanisms for morphing wings and control surfaces has indicated promising results, though often these are mechanically complex, or limited in the number of degrees-of-freedom (DOF) they can control. Seeking to improve on these limitations, we apply a new paradigm denoted Autonomous Material Composites to the design of avian-scale morphing wings. With this methodology, we reduce the need for complex actuation and mechanisms, and allow for three-dimensional placement of stretchable fiber optic strain gauges (Optical Lace) throughout the metamaterial structure. This structure centers around elastomeric conformal lattices, and by applying functionally-graded warping and thickening to this lattice, we allow for local tailoring of the compliance properties to fit the desired morphing. As a result, the wing achieves high-deformation morphing in three DOF: twist, camber, and extension/compression, with these morphed shapes effectively modifying the aerodynamic performance of the wing, as demonstrated in low-Reynolds wind tunnel testing. Our sensors also successfully demonstrate differentiable trends across all degrees of morphing, enabling the future state estimation and control of this wing.
Tight Bounds on 3-Team Manipulations in Randomized Death Match
Venue: arXiv cs.GT 2023
Year: 2023

Consider a round-robin tournament on n teams, where a winner must be (possibly randomly) selected as a function of the results from the ${n \choose 2}$ pairwise matches. A tournament rule is said to be k-SNM-$α$ if no set of k teams can ever manipulate the ${k \choose 2}$ pairwise matches between them to improve 2 the joint probability that one of these k teams wins by more than $α$. Prior work identifies multiple simple tournament rules that are 2-SNM-1/3 (Randomized Single Elimination Bracket [SSW17], Randomized King of the Hill [SWZZ20], Randomized Death Match [DW21]), which is optimal for k = 2 among all Condorcet-consistent rules (that is, rules that select an undefeated team with probability 1). Our main result establishes that Randomized Death Match is 3-SNM-(31/60), which is tight (for Randomized Death Match). This is the first tight analysis of any Condorcet-consistent tournament rule and at least three manipulating teams. Our proof approach is novel in this domain: we explicitly find the most-manipulable tournament, and directly show that no other tournament can be more manipulable. In addition to our main result, we establish that Randomized Death Match disincentivizes Sybil attacks (where a team enters multiple copies of themselves into the tournament, and arbitrarily manipulates the outcomes of matches between their copies). Specifically, for any tournament, and any team u that is not a Condorcet winner, the probability that u or one of its Sybils wins in Randomized Death Match approaches 0 as the number of Sybils approaches $\infty$.
Blockchain Education: Current State, Limitations, Career Scope, Challenges, and Future Directions
Venue: arXiv cs.CY 2023
Year: 2023

Blockchain is a revolutionary technology, and its growth started in various industries (such as IT, education, business, banking, and many others) to capitalize on it. Currently, in higher education institutions (HEIs) adoption of blockchain education needs to be improved in the academic programs and curriculums. In addition, HEIs must make many intense changes in the teaching and learning methods to educate learners about blockchain technology and its applications to meet the current industry workforce demand. Due to a lack of academic programs and courses, students nowadays rely on online resources and pay non-academic organizations a high fee. This paper provides a comprehensive survey of blockchain education's current state of the art by reviewing the different academic programs and industry workforce demand. In addition, blockchain application trends which include market growth and demands are discussed. Moreover, the blockchain career scope for different disciplines of students is examined.
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning
Venue: arXiv cs.CV 2023
Year: 2023

In cross-domain few-shot learning, the core issue is that the model trained on source domains struggles to generalize to the target domain, especially when the domain shift is large. Motivated by the observation that the domain shift between training tasks and target tasks usually can reflect in their style variation, we propose Task Augmented Meta-Learning (TAML) to conduct style transfer-based task augmentation to improve the domain generalization ability. Firstly, Multi-task Interpolation (MTI) is introduced to fuse features from multiple tasks with different styles, which makes more diverse styles available. Furthermore, a novel task-augmentation strategy called Multi-Task Style Transfer (MTST) is proposed to perform style transfer on existing tasks to learn discriminative style-independent features. We also introduce a Feature Modulation module (FM) to add random styles and improve generalization of the model. The proposed TAML increases the diversity of styles of training tasks, and contributes to training a model with better domain generalization ability. The effectiveness is demonstrated via theoretical analysis and thorough experiments on two popular cross-domain few-shot benchmarks.
View-Based Axiomatic Reasoning for PSO (Extended Version)
Venue: arXiv cs.LO 2023
Year: 2023

Weak memory models describe the semantics of concurrent programs on modern multi-core architectures. Reasoning techniques for concurrent programs, like Owicki-Gries-style proof calculi, have to be based on such a semantics, and hence need to be freshly developed for every new memory model. Recently, a more uniform approach to reasoning has been proposed which builds correctness proofs on the basis of a number of core axioms. This allows to prove program correctness independent of memory models, and transfers proofs to specific memory models by showing these to instantiate all axioms required in a proof. The axiomatisation is built on the notion of thread views as first class elements in the semantics. In this paper, we investigate the applicability of this form of axiomatic reasoning to the Partial Store Order (PSO) memory model. As the standard semantics for PSO is not based on views, we first of all provide a view-based semantics for PSO and prove it to coincide with the standard semantics. We then show the new view-based semantics to satisfy all but one axiom. The missing axiom refers to message-passing (MP) abilities of memory models, which PSO does not guarantee. As a consequence, only proofs without usage of the MP axiom are transferable to PSO. We illustrate the reasoning technique by proving correctness of a litmus test employing a fence to ensure message passing.
Fast Inference in Denoising Diffusion Models via MMD Finetuning
Venue: arXiv cs.CV 2023
Year: 2023

Denoising Diffusion Models (DDMs) have become a popular tool for generating high-quality samples from complex data distributions. These models are able to capture sophisticated patterns and structures in the data, and can generate samples that are highly diverse and representative of the underlying distribution. However, one of the main limitations of diffusion models is the complexity of sample generation, since a large number of inference timesteps is required to faithfully capture the data distribution. In this paper, we present MMD-DDM, a novel method for fast sampling of diffusion models. Our approach is based on the idea of using the Maximum Mean Discrepancy (MMD) to finetune the learned distribution with a given budget of timesteps. This allows the finetuned model to significantly improve the speed-quality trade-off, by substantially increasing fidelity in inference regimes with few steps or, equivalently, by reducing the required number of steps to reach a target fidelity, thus paving the way for a more practical adoption of diffusion models in a wide range of applications. We evaluate our approach on unconditional image generation with extensive experiments across the CIFAR-10, CelebA, ImageNet and LSUN-Church datasets. Our findings show that the proposed method is able to produce high-quality samples in a fraction of the time required by widely-used diffusion models, and outperforms state-of-the-art techniques for accelerated sampling. Code is available at: https://github.com/diegovalsesia/MMD-DDM.
An Ergonomic Role Allocation Framework for Dynamic Human-Robot Collaborative Tasks
Venue: arXiv cs.RO 2023
Year: 2023

By incorporating ergonomics principles into the task allocation processes, human-robot collaboration (HRC) frameworks can favour the prevention of work-related musculoskeletal disorders (WMSDs). In this context, existing offline methodologies do not account for the variability of human actions and states; therefore, planning and dynamically assigning roles in human-robot teams remains an unaddressed challenge.This study aims to create an ergonomic role allocation framework that optimises the HRC, taking into account task features and human state measurements. The presented framework consists of two main modules: the first provides the HRC task model, exploiting AND/OR Graphs (AOG)s, which we adapted to solve the allocation problem; the second module describes the ergonomic risk assessment during task execution through a risk indicator and updates the AOG-related variables to influence future task allocation. The proposed framework can be combined with any time-varying ergonomic risk indicator that evaluates human cognitive and physical burden. In this work, we tested our framework in an assembly scenario, introducing a risk index named Kinematic Wear.The overall framework has been tested with a multi-subject experiment. The task allocation results and subjective evaluations, measured with questionnaires, show that high-risk actions are correctly recognised and not assigned to humans, reducing fatigue and frustration in collaborative tasks.
Towards Rigorous Understanding of Neural Networks via Semantics-preserving Transformations
Venue: arXiv cs.NE 2023
Year: 2023

In this paper we present an algebraic approach to the precise and global verification and explanation of Rectifier Neural Networks, a subclass of Piece-wise Linear Neural Networks (PLNNs), i.e., networks that semantically represent piece-wise affine functions. Key to our approach is the symbolic execution of these networks that allows the construction of semantically equivalent Typed Affine Decision Structures (TADS). Due to their deterministic and sequential nature, TADS can, similarly to decision trees, be considered as white-box models and therefore as precise solutions to the model and outcome explanation problem. TADS are linear algebras which allows one to elegantly compare Rectifier Networks for equivalence or similarity, both with precise diagnostic information in case of failure, and to characterize their classification potential by precisely characterizing the set of inputs that are specifically classified or the set of inputs where two network-based classifiers differ. All phenomena are illustrated along a detailed discussion of a minimal, illustrative example: the continuous XOR function.
Source Code Metrics for Software Defects Prediction
Venue: arXiv cs.SE 2023
Year: 2023

In current research, there are contrasting results about the applicability of software source code metrics as features for defect prediction models. The goal of the paper is to evaluate the adoption of software metrics in models for software defect prediction, identifying the impact of individual source code metrics. With an empirical study on 275 release versions of 39 Java projects mined from GitHub, we compute 12 software metrics and collect software defect information. We train and compare three defect classification models. The results across all projects indicate that Decision Tree (DT) and Random Forest (RF) classifiers show the best results. Among the highest-performing individual metrics are NOC, NPA, DIT, and LCOM5. While other metrics, such as CBO, do not bring significant improvements to the models.
My Zelda Cane: Strategies Used by Blind Players to Play Visual-Centric Digital Games
Venue: arXiv cs.HC 2023
Year: 2023

Mainstream games are typically designed around the visual experience, with behaviors and interactions highly dependent on vision. Despite this, blind people are playing mainstream games while dealing with and overcoming inaccessible content, often together with friends and audiences. In this work, we analyze over 70 hours of YouTube videos, where blind content-creators play visual-centric games. We point out the various strategies employed by players to overcome barriers that permeate mainstream games. We reflect on ways to enable and improve blind players experience with these games, shedding light on the positive and negative consequences of apparently benign design choices. Our observations underline how game elements are appropriated for accessibility, the incidental consequences of audio design, and the trade-offs between accessibility, agency, and engagement.
A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams
Venue: arXiv cs.RO 2023
Year: 2023

The growing deployment of human-robot collaborative processes in several industrial applications, such as handling, welding, and assembly, unfolds the pursuit of systems which are able to manage large heterogeneous teams and, at the same time, monitor the execution of complex tasks. In this paper, we present a novel architecture for dynamic role allocation and collaborative task planning in a mixed human-robot team of arbitrary size. The architecture capitalizes on a centralized reactive and modular task-agnostic planning method based on Behavior Trees (BTs), in charge of actions scheduling, while the allocation problem is formulated through a Mixed-Integer Linear Program (MILP), that assigns dynamically individual roles or collaborations to the agents of the team. Different metrics used as MILP cost allow the architecture to favor various aspects of the collaboration (e.g. makespan, ergonomics, human preferences). Human preference are identified through a negotiation phase, in which, an human agent can accept/refuse to execute the assigned task.In addition, bilateral communication between humans and the system is achieved through an Augmented Reality (AR) custom user interface that provides intuitive functionalities to assist and coordinate workers in different action phases. The computational complexity of the proposed methodology outperforms literature approaches in industrial sized jobs and teams (problems up to 50 actions and 20 agents in the team with collaborations are solved within 1 s). The different allocated roles, as the cost functions change, highlights the flexibility of the architecture to several production requirements. Finally, the subjective evaluation demonstrating the high usability level and the suitability for the targeted scenario.
Shapley Values with Uncertain Value Functions
Venue: arXiv cs.LG 2023
Year: 2023

We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory. Such uncertain value functions can arise in the context of explainable machine learning as a result of non-deterministic algorithms. We show that random effects can in fact be absorbed into a Shapley value with a noiseless but shifted value function. Hence, Shapley values with uncertain value functions can be used in analogy to regular Shapley values. However, their reliable evaluation typically requires more computational effort.
Legal Obligation and Ethical Best Practice: Towards Meaningful Verbal Consent for Voice Assistants
Venue: arXiv cs.HC 2023
Year: 2023

To improve user experience, Alexa now allows users to consent to data sharing via voice rather than directing them to the companion smartphone app. While verbal consent mechanisms for voice assistants (VAs) can increase usability, they can also undermine principles core to informed consent. We conducted a Delphi study with experts from academia, industry, and the public sector on requirements for verbal consent in VAs. Candidate requirements were drawn from the literature, regulations, and research ethics guidelines that participants rated based on their relevance to the consent process, actionability by platforms, and usability by end-users, discussing their reasoning as the study progressed. We highlight key areas of (dis)agreement between experts, deriving recommendations for regulators, skill developers, and VA platforms towards crafting meaningful verbal consent mechanisms. Key themes include approaching permissions according to the user's ability to opt-out, minimising consent decisions, and ensuring platforms follow established consent principles.
Convergence beyond the over-parameterized regime using Rayleigh quotients
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we present a new strategy to prove the convergence of deep learning architectures to a zero training (or even testing) loss by gradient flow. Our analysis is centered on the notion of Rayleigh quotients in order to prove Kurdyka-Łojasiewicz inequalities for a broader set of neural network architectures and loss functions. We show that Rayleigh quotients provide a unified view for several convergence analysis techniques in the literature. Our strategy produces a proof of convergence for various examples of parametric learning. In particular, our analysis does not require the number of parameters to tend to infinity, nor the number of samples to be finite, thus extending to test loss minimization and beyond the over-parameterized regime.
A Benchmark Generator for Combinatorial Testing
Venue: arXiv cs.SE 2023
Year: 2023

Combinatorial Testing (CT) tools are essential to test properly a wide range of systems (train systems, Graphical User Interfaces (GUIs), autonomous driving systems, etc). While there is an active research community working on developing CT tools, paradoxically little attention has been paid to making available enough resources to test the CT tools themselves. In particular, the set of available benchmarks to asses their correctness, effectiveness and efficiency is rather limited. In this paper, we introduce a new generator of CT benchmarks that essentially borrows the structure contained in the plethora of available Combinatorial Problems from other research communities in order to create meaningful benchmarks. We additionally perform an extensive evaluation of CT tools with these new benchmarks. Thanks to this study we provide some insights on under which circumstances a particular CT tool should be used.
DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction
Venue: arXiv cs.IR 2023
Year: 2023

Learning feature interactions is the key to success for the large-scale CTR prediction in Ads ranking and recommender systems. In industry, deep neural network-based models are widely adopted for modeling such problems. Researchers proposed various neural network architectures for searching and modeling the feature interactions in an end-to-end fashion. However, most methods only learn static feature interactions and have not fully leveraged deep CTR models' representation capacity. In this paper, we propose a new model: DynInt. By extending Polynomial-Interaction-Network (PIN), which learns higher-order interactions recursively to be dynamic and data-dependent, DynInt further derived two modes for modeling dynamic higher-order interactions: dynamic activation and dynamic parameter. In dynamic activation mode, we adaptively adjust the strength of learned interactions by instance-aware activation gating networks. In dynamic parameter mode, we re-parameterize the parameters by different formulations and dynamically generate the parameters by instance-aware parameter generation networks. Through instance-aware gating mechanism and dynamic parameter generation, we enable the PIN to model dynamic interaction for potential industry applications. We implement the proposed model and evaluate the model performance on real-world datasets. Extensive experiment results demonstrate the efficiency and effectiveness of DynInt over state-of-the-art models.