OpenRaiser/Intern-Atlas huggingface dataset:
Responsible AI Implementation: A Human-centered Framework for Accelerating the Innovation Process
Venue: arXiv cs.AI 2022
Year: 2022

There is still a significant gap between expectations and the successful adoption of AI to innovate and improve businesses. Due to the emergence of deep learning, AI adoption is more complex as it often incorporates big data and the internet of things, affecting data privacy. Existing frameworks have identified the need to focus on human-centered design, combining technical and business/organizational perspectives. However, trust remains a critical issue that needs to be designed from the beginning. The proposed framework expands from the human-centered design approach, emphasizing and maintaining the trust that underpins the process. This paper proposes a theoretical framework for responsible artificial intelligence (AI) implementation. The proposed framework emphasizes a synergistic business technology approach for the agile co-creation process. The aim is to streamline the adoption process of AI to innovate and improve business by involving all stakeholders throughout the project so that the AI technology is designed, developed, and deployed in conjunction with people and not in isolation. The framework presents a fresh viewpoint on responsible AI implementation based on analytical literature review, conceptual framework design, and practitioners' mediating expertise. The framework emphasizes establishing and maintaining trust throughout the human-centered design and agile development of AI. This human-centered approach is aligned with and enabled by the privacy by design principle. The creators of the technology and the end-users are working together to tailor the AI solution specifically for the business requirements and human characteristics. An illustrative case study on adopting AI for assisting planning in a hospital will demonstrate that the proposed framework applies to real-life applications.
BadRes: Reveal the Backdoors through Residual Connection
Venue: arXiv cs.CR 2022
Year: 2022

Generally, residual connections are indispensable network components in building CNNs and Transformers for various downstream tasks in CV and VL, which encourages skip shortcuts between network blocks. However, the layer-by-layer loopback residual connections may also hurt the model's robustness by allowing unsuspecting input. In this paper, we proposed a simple yet strong backdoor attack method - BadRes, where the residual connections play as a turnstile to be deterministic on clean inputs while unpredictable on poisoned ones. We have performed empirical evaluations on four datasets with ViT and BEiT models, and the BadRes achieves 97% attack success rate while receiving zero performance degradation on clean data. Moreover, we analyze BadRes with state-of-the-art defense methods and reveal the fundamental weakness lying in residual connections.
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking
Venue: arXiv cs.LG 2022
Year: 2022

This paper presents COOL-MC, a tool that integrates state-of-the-art reinforcement learning (RL) and model checking. Specifically, the tool builds upon the OpenAI gym and the probabilistic model checker Storm. COOL-MC provides the following features: (1) a simulator to train RL policies in the OpenAI gym for Markov decision processes (MDPs) that are defined as input for Storm, (2) a new model builder for Storm, which uses callback functions to verify (neural network) RL policies, (3) formal abstractions that relate models and policies specified in OpenAI gym or Storm, and (4) algorithms to obtain bounds on the performance of so-called permissive policies. We describe the components and architecture of COOL-MC and demonstrate its features on multiple benchmark environments.
Challenges and Opportunities of Machine Learning for Monitoring and Operational Data Analytics in Quantitative Codesign of Supercomputers
Venue: arXiv cs.DC 2022
Year: 2022

This work examines the challenges and opportunities of Machine Learning (ML) for Monitoring and Operational Data Analytics (MODA) in the context of Quantitative Codesign of Supercomputers (QCS). MODA is employed to gain insights into the behavior of current High Performance Computing (HPC) systems to improve system efficiency, performance, and reliability (e.g. through optimizing cooling infrastructure, job scheduling, and application parameter tuning). In this work, we take the position that QCS in general, and MODA in particular, require close exchange with the ML community to realize the full potential of data-driven analysis for the benefit of existing and future HPC systems. This exchange will facilitate identifying the appropriate ML methods to gain insights into current HPC systems and to go beyond expert-based knowledge and rules of thumb.
On the acceptance by code reviewers of candidate security patches suggested by Automated Program Repair tools
Venue: arXiv cs.SE 2022
Year: 2022

Background: Testing and validation of the semantic correctness of patches provided by tools for Automated Program Repairs (APR) has received a lot of attention. Yet, the eventual acceptance or rejection of suggested patches for real world projects by humans patch reviewers has received a limited attention. Objective: To address this issue, we plan to investigate whether (possibly incorrect) security patches suggested by APR tools are recognized by human reviewers. We also want to investigate whether knowing that a patch was produced by an allegedly specialized tool does change the decision of human reviewers. Method: In the first phase, using a balanced design, we propose to human reviewers a combination of patches proposed by APR tools for different vulnerabilities and ask reviewers to adopt or reject the proposed patches. In the second phase, we tell participants that some of the proposed patches were generated by security specialized tools (even if the tool was actually a `normal' APR tool) and measure whether the human reviewers would change their decision to adopt or reject a patch. Limitations: The experiment will be conducted in an academic setting, and to maintain power, it will focus on a limited sample of popular APR tools and popular vulnerability types.
Are Deep Learning-Generated Social Media Profiles Indistinguishable from Real Profiles?
Venue: arXiv cs.SI 2022
Year: 2022

In recent years, deep learning methods have become increasingly capable of generating near photorealistic pictures and humanlike text up to the point that humans can no longer recognize what is real and what is AI-generated. Concerningly, there is evidence that some of these methods have already been adopted to produce fake social media profiles and content. We hypothesize that these advances have made detecting generated fake social media content in the feed extremely difficult, if not impossible, for the average user of social media. This paper presents the results of an experiment where 375 participants attempted to label real and generated profiles and posts in a simulated social media feed. The results support our hypothesis and suggest that even fully-generated fake profiles with posts written by an advanced text generator are difficult for humans to identify.
Towards Interoperability of Open and Permissionless Blockchains: A Cross-Chain Query Language
Venue: arXiv cs.DC 2022
Year: 2022

The rise of open and permissionless blockchains has introduced novel platforms for applications based on distributed data storage. At the application and business levels, long-established query languages such as SQL provide interoperability that can be complemented by blockchain-based data storage today, enabling permissionless and verifiable data storage along with decentralized execution across tens of thousands of nodes. However, when accessing one or more blockchains, interoperability is not provided today, posing challenges such as inhomogeneous data access in addition to different features and trade-offs, e.g. in data and distribution, scalability, and security. Towards interoperability in data access among the increasing number of blockchain platforms, this paper introduces a cross-chain query language for data access across blockchains. Similar to SQL, the language abstracts from implementation based on a data model compatible with the largest open and permissionless blockchains (OPB) today. The language, data model, and processing architecture are demonstrated and evaluated with an implemented prototype, aiming to contribute to the discussion on blockchain interoperability among OPB.
UBARv2: Towards Mitigating Exposure Bias in Task-Oriented Dialogs
Venue: arXiv cs.CL 2022
Year: 2022

This paper studies the exposure bias problem in task-oriented dialog systems, where the model's generated content over multiple turns drives the dialog context away from the ground-truth distribution at training time, introducing error propagation and damaging the robustness of the TOD system. To bridge the gap between training and inference for multi-turn task-oriented dialogs, we propose session-level sampling which explicitly exposes the model to sampled generated content of dialog context during training. Additionally, we employ a dropout-based consistency regularization with the masking strategy R-Mask to further improve the robustness and performance of the model. The proposed UBARv2 achieves state-of-the-art performance on the standardized evaluation benchmark MultiWOZ and extensive experiments show the effectiveness of the proposed methods.
2.5D Mapping, Pathfinding and Path Following For Navigation Of A Differential Drive Robot In Uneven Terrain
Venue: arXiv cs.RO 2022
Year: 2022

Safe navigation in uneven terrains is an important problem in robotic research. In this paper we propose a 2.5D navigation system which consists of elevation map building, path planning and local path following with obstacle avoidance. For local path following we use Model Predictive Path Integral (MPPI) control method. We propose novel cost-functions for MPPI in order to adapt it to elevation maps and motion through unevenness. We evaluate our system on multiple synthetic tests and in a simulated environment with different types of obstacles and rough surfaces.
Un referentiel spatial pour la gestion de la voirie dans son environnement
Venue: arXiv cs.DB 2022
Year: 2022

The densification of urban basements into distribution networks leads to interventions involving different actors. These interventions generally impact the roadway, that is to say the entire road infrastructure, both structurally (vertical component) and superficially (horizontal component). In addition to the financial costs, each intervention can weaken the structure and disturb the various users. Our work aims at optimizing interventions by improving the coordinated management of data related to the road and its environment in a spatial reference frame. For that, we aim at a modeling adapted to the management of the infrastructure integrating the roadway and its constituent elements with the materials and their thicknesses. Our approach is organized in four axes: 1/ A description of the roadway's constituent elements, to propose a coherent vision to the actors. 2/ A study of the existing data for the management of the infrastructure, in order to identify the knowledge and the needs. 3/ A GIS modeling of the infrastructure to help communities to produce their data. 4/ An analysis of spatial interactions between roads and networks, to open discussions on construction choices. This article retraces all the work carried out for the four axes and proposes to open the debate on the design of a spatial reference system for roads adapted to their management.
A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks
Venue: arXiv cs.SI 2022
Year: 2022

We introduce the \emph{temporal graphlet kernel} for classifying dissemination processes in labeled temporal graphs. Such dissemination processes can be spreading (fake) news, infectious diseases, or computer viruses in dynamic networks. The networks are modeled as labeled temporal graphs, in which the edges exist at specific points in time, and node labels change over time. The classification problem asks to discriminate dissemination processes of different origins or parameters, e.g., infectious diseases with different infection probabilities. Our new kernel represents labeled temporal graphs in the feature space of temporal graphlets, i.e., small subgraphs distinguished by their structure, time-dependent node labels, and chronological order of edges. We introduce variants of our kernel based on classes of graphlets that are efficiently countable. For the case of temporal wedges, we propose a highly efficient approximative kernel with low error in expectation. We show that our kernels are faster to compute and provide better accuracy than state-of-the-art methods.
Public Reaction to Scientific Research via Twitter Sentiment Prediction
Venue: arXiv cs.IR 2022
Year: 2022

Social media users share their ideas, thoughts, and emotions with other users. However, it is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications. Additionally, we investigate what features of the research articles help in such prediction. Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles.
Do Cloud Developers Prefer CLIs or Web Consoles? CLIs Mostly, Though It Varies by Task
Venue: arXiv cs.SE 2022
Year: 2022

Despite the increased importance of Cloud tooling, and many large-scale studies of Cloud users, research has yet to answer what tool modalities (e.g. CLI or web console) developers prefer. In formulating our studies, we quickly found that preference varies heavily based on the programming task at hand. To address this gap, we conducted a two-part research study that quantifies modality preference as a function of programming task. Part one surveys how preference for three tool modalities (CLI, IDE, web console) varies across three classes of task (CRUD, debugging, monitoring). The survey shows, among 60 respondents, developers most prefer the CLI modality, especially for CRUD tasks. Monitoring tasks are the exception for which developers prefer the web console. Part two observes how four participants complete a task using the kubectl CLI and the OpenShift web console. All four participants prefer using the CLI to accomplish the task.
Wealth Dynamics Over Generations: Analysis and Interventions
Venue: arXiv cs.GT 2022
Year: 2022

We present a stylized model with feedback loops for the evolution of a population's wealth over generations. Individuals have both talent and wealth: talent is a random variable distributed identically for everyone, but wealth is a random variable that is dependent on the population one is born into. Individuals then apply to a downstream agent, which we treat as a university throughout the paper (but could also represent an employer) who makes a decision about whether to admit them or not. The university does not directly observe talent or wealth, but rather a signal (representing e.g. a standardized test) that is a convex combination of both. The university knows the distributions from which an individual's type and wealth are drawn, and makes its decisions based on the posterior distribution of the applicant's characteristics conditional on their population and signal. Each population's wealth distribution at the next round then depends on the fraction of that population that was admitted by the university at the previous round. We study wealth dynamics in this model, and give conditions under which the dynamics have a single attracting fixed point (which implies population wealth inequality is transitory), and conditions under which it can have multiple attracting fixed points (which implies that population wealth inequality can be persistent). In the case in which there are multiple attracting fixed points, we study interventions aimed at eliminating or mitigating inequality, including increasing the capacity of the university to admit more people, aligning the signal generated by individuals with the preferences of the university, and making direct monetary transfers to the less wealthy population.
IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture
Venue: arXiv cs.NI 2022
Year: 2022

Aerial base stations (ABSs) allow smart farms to offload processing responsibility of complex tasks from internet of things (IoT) devices to ABSs. IoT devices have limited energy and computing resources, thus it is required to provide an advanced solution for a system that requires the support of ABSs. This paper introduces a novel multi-actor-based risk-sensitive reinforcement learning approach for ABS task scheduling for smart agriculture. The problem is defined as task offloading with a strict condition on completing the IoT tasks before their deadlines. Moreover, the algorithm must also consider the limited energy capacity of the ABSs. The results show that our proposed approach outperforms several heuristics and the classic Q-Learning approach. Furthermore, we provide a mixed integer linear programming solution to determine a lower bound on the performance, and clarify the gap between our risk-sensitive solution and the optimal solution, as well. The comparison proves our extensive simulation results demonstrate that our method is a promising approach for providing a guaranteed task processing services for the IoT tasks in a smart farm, while increasing the hovering time of the ABSs in this farm.
Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts
Venue: arXiv cs.SD 2022
Year: 2022

Vocal bursts play an important role in communicating affect, making them valuable for improving speech emotion recognition. Here, we present our approach for classifying vocal bursts and predicting their emotional significance in the ACII Affective Vocal Burst Workshop & Challenge 2022 (A-VB). We use a large self-supervised audio model as shared feature extractor and compare multiple architectures built on classifier chains and attention networks, combined with uncertainty loss weighting strategies. Our approach surpasses the challenge baseline by a wide margin on all four tasks.
Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections
Venue: arXiv cs.CV 2022
Year: 2022

Calibration of multi-camera systems, i.e. determining the relative poses between the cameras, is a prerequisite for many tasks in computer vision and robotics. Camera calibration is typically achieved using offline methods that use checkerboard calibration targets. These methods, however, often are cumbersome and lengthy, considering that a new calibration is required each time any camera pose changes. In this work, we propose a novel, marker-free online method for the extrinsic calibration of multiple smart edge sensors, relying solely on 2D human keypoint detections that are computed locally on the sensor boards from RGB camera images. Our method assumes the intrinsic camera parameters to be known and requires priming with a rough initial estimate of the camera poses. The person keypoint detections from multiple views are received at a central backend where they are synchronized, filtered, and assigned to person hypotheses. We use these person hypotheses to repeatedly solve optimization problems in the form of factor graphs. Given suitable observations of one or multiple persons traversing the scene, the estimated camera poses converge towards a coherent extrinsic calibration within a few minutes. We evaluate our approach in real-world settings and show that the calibration with our method achieves lower reprojection errors compared to a reference calibration generated by an offline method using a traditional calibration target.
Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming
Venue: arXiv cs.PL 2022
Year: 2022

Efficient inference is often possible in a streaming context using Rao-Blackwellized particle filters (RBPFs), which exactly solve inference problems when possible and fall back on sampling approximations when necessary. While RBPFs can be implemented by hand to provide efficient inference, the goal of streaming probabilistic programming is to automatically generate such efficient inference implementations given input probabilistic programs. In this work, we propose semi-symbolic inference, a technique for executing probabilistic programs using a runtime inference system that automatically implements Rao-Blackwellized particle filtering. To perform exact and approximate inference together, the semi-symbolic inference system manipulates symbolic distributions to perform exact inference when possible and falls back on approximate sampling when necessary. This approach enables the system to implement the same RBPF a developer would write by hand. To ensure this, we identify closed families of distributions -- such as linear-Gaussian and finite discrete models -- on which the inference system guarantees exact inference. We have implemented the runtime inference system in the ProbZelus streaming probabilistic programming language. Despite an average $1.6\times$ slowdown compared to the state of the art on existing benchmarks, our evaluation shows that speedups of $3\times$-$87\times$ are obtainable on a new set of challenging benchmarks we have designed to exploit closed families.
Decentralized Infrastructure for (Neuro)science
Venue: arXiv cs.GL 2022
Year: 2022

The most pressing problems in science are neither empirical nor theoretical, but infrastructural. Scientific practice is defined by coproductive, mutually reinforcing infrastructural deficits and incentive systems that everywhere constrain and contort our art of curiosity in service of profit and prestige. Our infrastructural problems are not unique to science, but reflective of the broader logic of digital enclosure where platformatized control of information production and extraction fuels some of the largest corporations in the world. I have taken lessons learned from decades of intertwined digital cultures within and beyond academia like wikis, pirates, and librarians in order to draft a path towards more liberatory infrastructures for both science and society. Based on a system of peer-to-peer linked data, I sketch interoperable systems for shared data, tools, and knowledge that map onto three domains of platform capture: storage, computation and communication. The challenge of infrastructure is not solely technical, but also social and cultural, and so I attempt to ground a practical development blueprint in an ethics for organizing and maintaining it. I intend this draft as a rallying call for organization, to be revised with the input of collaborators and through the challenges posed by its implementation. I argue that a more liberatory future for science is neither utopian nor impractical -- the truly impractical choice is to continue to organize science as prestige fiefdoms resting on a pyramid scheme of underpaid labor, playing out the clock as every part of our work is swallowed whole by circling information conglomerates. It was arguably scientists looking for a better way to communicate that created something as radical as the internet in the first place, and I believe we can do it again.