Reviewerly/exHarmony huggingface dataset:
A Song of Ice and Fire: Analyzing Textual Autotelic Agents in ScienceWorld

Building open-ended agents that can autonomously discover a diversity of behaviours is one of the long-standing goals of artificial intelligence. This challenge can be studied in the framework of autotelic RL agents, i.e. agents that learn by selecting and pursuing their own goals, self-organizing a learning curriculum. Recent work identified language as a key dimension of autotelic learning, in particular because it enables abstract goal sampling and guidance from social peers for hindsight relabelling. Within this perspective, we study the following open scientific questions: What is the impact of hindsight feedback from a social peer (e.g. selective vs. exhaustive)? How can the agent learn from very rare language goal examples in its experience replay? How can multiple forms of exploration be combined, and take advantage of easier goals as stepping stones to reach harder ones? To address these questions, we use ScienceWorld, a textual environment with rich abstract and combinatorial physics. We show the importance of selectivity from the social peer's feedback; that experience replay needs to over-sample examples of rare goals; and that following self-generated goal sequences where the agent's competence is intermediate leads to significant improvements in final performance.
ActGraph: Prioritization of Test Cases Based on Deep Neural Network Activation Graph

Abstract Widespread applications of deep neural networks (DNNs) benefit fromDNN testing to guarantee their quality. In the DNN testing, numeroustest cases are fed into the model to explore potential vulnerabilities,but they require expensive manual cost to check the label. Therefore,test case prioritization is proposed to solve the problem of labeling cost,e.g., activation-based and mutation-based prioritization methods. How-ever, most of them suffer from limited scenarios (i.e. high confidenceadversarial or false positive cases) and high time complexity. To addressthese challenges, we propose the concept of the activation graph fromthe perspective of the spatial relationship of neurons. We observe thatthe activation graph of cases that triggers the model’s misbehaviorsignificantly differs from that of normal cases. Motivated by it, wedesign a test case prioritization method based on the activation graph,ActGraph, by extracting the high-order node features of the activationgraph for prioritization. ActGraph explains the difference between thetest cases to solve the problem of scenario limitation. Without muta-tion operations, ActGraph is easy to implement, leading to lower timecomplexity. Extensive experiments on three datasets and four mod-els demonstrate that ActGraph has the following key characteristics.(i) Effectiveness and generalizability: ActGraph shows competitiveperformance in all of the natural, adversarial and mixed scenarios,especially in RAUC-100 improvement (∼ ×1.40). (ii) Efficiency: Act-Graph does not use complex mutation operations and runs in less time (∼ ×1/50) than the state-of-the-art method. The code of ActGraph is open-sourced at https:// github.com/ Embed-Debuger/ ActGraph.
A Unified View of Long-Sequence Models towards Modeling Million-Scale Dependencies

Ever since their conception, Transformers have taken over traditional sequence models in many tasks, such as NLP, image classification, and video/audio processing, for their fast training and superior performance. Much of the merit is attributable to positional encoding and multi-head attention. However, Transformers fall short in learning long-range dependencies mainly due to the quadratic complexity scaled with context length, in terms of both time and space. Consequently, over the past five years, a myriad of methods has been proposed to make Transformers more efficient. In this work, we first take a step back, study and compare existing solutions to long-sequence modeling in terms of their pure mathematical formulation. Specifically, we summarize them using a unified template, given their shared nature of token mixing. Through benchmarks, we then demonstrate that long context length does yield better performance, albeit application-dependent, and traditional Transformer models fall short in taking advantage of long-range dependencies. Next, inspired by emerging sparse models of huge capacity, we propose a machine learning system for handling million-scale dependencies. As a proof of concept, we evaluate the performance of one essential component of this system, namely, the distributed multi-head attention. We show that our algorithm can scale up attention computation by almost $40\times$ using four GeForce RTX 4090 GPUs, compared to vanilla multi-head attention mechanism. We believe this study is an instrumental step towards modeling million-scale dependencies.
On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)

Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to evaluate (1) how good LLMs are by themselves in generating and validating simple plans in commonsense planning tasks (of the type that humans are generally quite good at) and (2) how good LLMs are in being a source of heuristic guidance for other agents--either AI planners or human planners--in their planning tasks. To investigate these questions in a systematic rather than anecdotal manner, we start by developing a benchmark suite based on the kinds of domains employed in the International Planning Competition. On this benchmark, we evaluate LLMs in three modes: autonomous, heuristic and human-in-the-loop. Our results show that LLM's ability to autonomously generate executable plans is quite meager, averaging only about 3% success rate. The heuristic and human-in-the-loop modes show slightly more promise. In addition to these results, we also make our benchmark and evaluation tools available to support investigations by research community.
Provable Detection of Propagating Sampling Bias in Prediction Models

With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider a general, but realistic, scenario in which a predictive model is learned from (potentially biased) training data, and model predictions are assessed post-hoc for fairness by some auditing method. We provide a theoretical analysis of how a specific form of data bias, differential sampling bias, propagates from the data stage to the prediction stage. Unlike prior work, we evaluate the downstream impacts of data biases quantitatively rather than qualitatively and prove theoretical guarantees for detection. Under reasonable assumptions, we quantify how the amount of bias in the model predictions varies as a function of the amount of differential sampling bias in the data, and at what point this bias becomes provably detectable by the auditor. Through experiments on two criminal justice datasets -- the well-known COMPAS dataset and historical data from NYPD's stop and frisk policy -- we demonstrate that the theoretical results hold in practice even when our assumptions are relaxed.
Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise

We consider the stochastic optimization problem with smooth but not necessarily convex objectives in the heavy-tailed noise regime, where the stochastic gradient's noise is assumed to have bounded $p$th moment ($p\in(1,2]$). Zhang et al. (2020) is the first to prove the $\Omega(T^{\frac{1-p}{3p-2}})$ lower bound for convergence (in expectation) and provides a simple clipping algorithm that matches this optimal rate. Cutkosky and Mehta (2021) proposes another algorithm, which is shown to achieve the nearly optimal high-probability convergence guarantee $O(\log(T/\delta)T^{\frac{1-p}{3p-2}})$, where $\delta$ is the probability of failure. However, this desirable guarantee is only established under the additional assumption that the stochastic gradient itself is bounded in $p$th moment, which fails to hold even for quadratic objectives and centered Gaussian noise. In this work, we first improve the analysis of the algorithm in Cutkosky and Mehta (2021) to obtain the same nearly optimal high-probability convergence rate $O(\log(T/\delta)T^{\frac{1-p}{3p-2}})$, without the above-mentioned restrictive assumption. Next, and curiously, we show that one can achieve a faster rate than that dictated by the lower bound $\Omega(T^{\frac{1-p}{3p-2}})$ with only a tiny bit of structure, i.e., when the objective function $F(x)$ is assumed to be in the form of $\mathbb{E}_{\Xi\sim\mathcal{D}}[f(x,\Xi)]$, arguably the most widely applicable class of stochastic optimization problems. For this class of problems, we propose the first variance-reduced accelerated algorithm and establish that it guarantees a high-probability convergence rate of $O(\log(T/\delta)T^{\frac{1-p}{2p-1}})$ under a mild condition, which is faster than $\Omega(T^{\frac{1-p}{3p-2}})$. Notably, even when specialized to the finite-variance case, our result yields the (near-)optimal high-probability rate $O(\log(T/\delta)T^{-1/3})$.
Error estimation in current noisy quantum computers

One of the main important features of the noisy intermediate-scale quantum (NISQ) era is the correct evaluation and consideration of errors. In this paper, we analyze the main sources of errors in current (IBM) quantum computers and we present a useful tool (TED-qc) designed to facilitate the total error probability expected for any quantum circuit. We propose this total error probability as the best way to estimate a lower bound for the fidelity in the NISQ era, avoiding the necessity of comparing the quantum calculations with any classical one. In order to contrast the robustness of our tool we compute the total error probability that may occur in three different quantum models: 1) the Ising model, 2) the Quantum-Phase Estimation (QPE), and 3) the Grover's algorithm. For each model, the main quantities of interest are computed and benchmarked against the reference simulator's results as a function of the error probability for a representative and statistically significant sample size. The analysis is satisfactory in more than the $99\%$ of the cases. In addition, we study how error mitigation techniques are able to eliminate the noise induced during the measurement.
Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

Knowledge tracing (KT) is a crucial technique to predict students' future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the \emph{homogeneous question} assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students' knowledge state variations at a fine-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at \url{https://pykt.org/}.
Iterative minimization algorithm on a mixture family

Iterative minimization algorithms appear in various areas including machine learning, neural networks, and information theory.The em algorithm is one of the famous iterative minimization algorithms in the area of machine learning, and the Arimoto-Blahut algorithm is a typical iterative algorithm in the area of information theory.However, these two topics had been separately studied for a long time. In this paper, we generalize an algorithm that was recently proposed in the context of the Arimoto-Blahut algorithm.Then, we show various convergence theorems, one of which covers the case when each iterative step is done approximately.Also, we apply this algorithm to the target problem of the em algorithm, and propose its improvement. In addition, we apply it to other various problems in information theory.
A Review of the Role of Causality in Developing Trustworthy AI Systems

State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.
Modeling Complex Event Scenarios via Simple Entity-focused Questions

Event scenarios are often complex and involve multiple event sequences connected through different entity participants. Exploring such complex scenarios requires an ability to branch through different sequences, something that is difficult to achieve with standard event language modeling. To address this, we propose a question-guided generation framework that models events in complex scenarios as answers to questions about participants. At any step in the generation process, the framework uses the previously generated events as context, but generates the next event as an answer to one of three questions: what else a participant did, what else happened to a participant, or what else happened. The participants and the questions themselves can be sampled or be provided as input from a user, allowing for controllable exploration. Our empirical evaluation shows that this question-guided generation provides better coverage of participants, diverse events within a domain, comparable perplexities for modeling event sequences, and more effective control for interactive schema generation.
VISTA: Fast and Efficient Traffic Surveillance by Tile Sampling

&lt;p&gt;With the increasing number of vehicles in modern cities, traffic surveillance via cameras on roads has become an important application. Cities have installed thousands of cameras on roads, which send video feeds to a cloud center to run computer vision algorithms. This requires high bandwidth. Current techniques reduce the bandwidth requirement by either sending a limited number of frames/pixels/regions or relying on re-encoding the important parts of the video. This requires running DNNs to extract important portions in a frame so that they can be again sent at a higher resolution from the camera to the server. This has the disadvantage of imposing significant overhead on the camera side compute, as re-encoding is known to be expensive, and makes the system less real-time. In this work, we propose VISTA, a system that utilizes tile sampling, where a limited number of rectangular areas within the frames, known as tiles, are sent to the server. We then propose an adaptive tile sampling algorithm, that estimates the presence of moving objects by comparing the statistics of the tiles' bitrate (in kbps) and then decide to retain only the necessary tiles, thus eliminating the requirement to use a DNN at the camera side. We evaluate VISTA on different datasets having 56 videos in total to show that on average our technique reduces $17$-$40$\% of the total amount of data sent to the cloud while providing a detection accuracy of over $85\%$. Furthermore, VISTA also runs in real-time even on cheap edge devices like Raspberry Pi and nVidia Jetson Nano. Further, it requires minimal calibration compared to prior works.&lt;/p&gt;
A System for Constituent and Dependency Tree Linearization

In this work, we introduce a framework that unifies existing implementations for the tasks of constituent and dependency parsing as sequence labeling problems. The system provides a way to encode both formalisms as sequences of one label per word, so they can be used with any existing general-purpose sequence labeling architecture. More particu- larly, we implement three linearizations to encode constituent trees and four linearizations for dependency trees. All encoding functions ensure completeness and injectivity. We will also train a sequence labeling neural system to learn such encodings, and compare their ef- fectiveness on standard constituent (PTB and SPMRL treebanks) and dependency parsing (a subset of treebanks from the UD collection) evaluation frameworks.
Positioning system of Android smartphones through the physical properties of sound

Knowing the position of a device such as a smartphone accurately enough to control another device is a complex problem. Specific solutions (such as inertial measurement units) exist, but they are relatively expensive while do not provide accurate positioning over extended periods of time. In this project we explored methods based on the physical properties of sound to achieve quality positioning with affordable and relatively easy to set up equipment. Different options have been tested and we have chosen to use the Doppler effect to determine the speed of movement of the device and calculate its position in both 2D and 3D environments. Multiple tests have been performed demonstrating the advantages of the proposed method and pointing out the most important limitations. Finally, a demonstrator of the proposed technology has been developed to use a smartphone as a video game controller.
Comparison of ensemble-combination approaches in an automatic sleep staging inter-database generalization task

Deep learning has demostrated its usefulness in reaching top-level performance. How- ever, inter-database generalization is still a broad of concern due to the aroused differences between local and external datasets’ performances. In this work we explore different deep learning model’s combination strategies applied to a multi-database case of study in the domain of sleep medicine. More specifically, three ensemble combination methods (namely max-voting, output averaging and weighted combination using the Nelder-Mead search) are analyzed in comparison to baseline methods (local models, database assembly approach) in a sleep staging inter-database generalization task.
A Multi-platform Collaborative Architecture for Multi-User eXtended Reality Applications

In the last years, new visualization devices related to technologies like AR, MR and VR have emerged. Such devices have proven to be useful for numerous medical, industrial and manufacturing processes, but the technologies they use tend to be platform dependent. Currently, the Khronos group is making an effort to change this thanks to the OpenXR framework, which provides tools to standardize the development for different XR (Extended Reality) platforms. However, many processes and tasks require multiple users to interact with the same virtual environment simultaneously. To tackle this issue, this paper presents an XR solution that allows for the creation of collaborative applications that can be used at the same time by different platforms, such as computers (i.e., Windows, Mac or Linux PCs) and Mixed Reality smart glasses (e.g., Microsoft HoloLens 2). The proposed architecture is based on Unity and Mirror, a high-level networking tool for the mentioned game engine. The solution allows developers to design just one application that could be compiled and deployed to different platform devices without the need for changing any configuration or for adapting the project to each of the platform requisites. The proposed system also allows for using multiple devices simultaneously, providing a new way for collaborative interaction with the application, showing all the visual components synchronized in the same position and in the same state, thus facilitating communications and awareness of the environment for the developed XR experiences.
Task-oriented autonomous representation of visual inputs to facilitate robot goal achievement

State Representation Learning (SRL) is a field in Robotics and Artificial Intelligence that studies how to encode the observations of an environment in a way that facilitates performing specific tasks. A common approach is using autoencoders and learning to reproduce the same state from a low-dimensional representation [1, 2, 3]. Although very task-independent, this method learns to encode features that may not be relevant to the task in which the encoding will be used. An alternative is to use some elements related to the goal to achieve and/or some knowledge about the environment and the problem [1] to produce an appropriate low-dimensional encoding that captures only the relevant knowledge. In this paper, we propose an approach to autonomously obtain latent spaces of the appropriate (low) dimension that permit an efficient representation of the sensorial inputs using information about the environment and the goal. To measure the performance of this methodology, we show the results of a series of simulations of robots performing a task consisting in catching a ball in different environments. In these cases, we have found that the models required for the prediction of the final position of the ball, taking as input the learned encoding, are much simpler than those that would be required using the sensing information directly.
Testing and Analysis of Self-Sovereign Decentralized Digital Identity Technology

The emergence of the Internet has generated new functionalities in our daily lives: data sharing, people communication or even the creation of a new “digital life” within the virtual world. Nowadays, each user visits many websites and web applications, in which the owner companies stores certain information from the user to be able to use the services offered. As a result, the user’s digital identity is scattered in many websites databases. During the last few years, some solutions have been developed, proposing a new digital identity solution that is known as Self-Sovereign Identity. This solution allows the decentralization of the user personal data and the self-sovereign identity management using blockchain technology. This paper shows a proof of concept of a simplified self-sovereign identity system that allows the user to manage their personal data with different external and compatible web services.