OpenRaiser/Intern-Atlas huggingface dataset:
Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks
Venue: arXiv cs.NI 2022
Year: 2022

Emerging six generation (6G) is the integration of heterogeneous wireless networks, which can seamlessly support anywhere and anytime networking. But high Quality-of-Trust should be offered by 6G to meet mobile user expectations. Artificial intelligence (AI) is considered as one of the most important components in 6G. Then AI-based trust management is a promising paradigm to provide trusted and reliable services. In this article, a generative adversarial learning-enabled trust management method is presented for 6G wireless networks. Some typical AI-based trust management schemes are first reviewed, and then a potential heterogeneous and intelligent 6G architecture is introduced. Next, the integration of AI and trust management is developed to optimize the intelligence and security. Finally, the presented AI-based trust management method is applied to secure clustering to achieve reliable and real-time communications. Simulation results have demonstrated its excellent performance in guaranteeing network security and service quality.
Can Gaze Beat Touch? A Fitts' Law Evaluation of Gaze, Touch, and Mouse Inputs
Venue: arXiv cs.HC 2022
Year: 2022

Gaze input has been a promising substitute for mouse input for point and select interactions. Individuals with severe motor and speech disabilities primarily rely on gaze input for communication. Gaze input also serves as a hands-free input modality in the scenarios of situationally-induced impairments and disabilities (SIIDs). Hence, the performance of gaze input has often been compared to mouse input through standardized performance evaluation procedure like the Fitts' Law. With the proliferation of touch-enabled devices such as smartphones, tablet PCs, or any computing device with a touch surface, it is also important to compare the performance of gaze input to touch input. In this study, we conducted ISO 9241-9 Fitts' Law evaluation to compare the performance of multimodal gaze and foot-based input to touch input in a standard desktop environment, while using mouse input as the baseline. From a study involving 12 participants, we found that the gaze input has the lowest throughput (2.55 bits/s), and the highest movement time (1.04 s) of the three inputs. In addition, though touch input involves maximum physical movements, it achieved the highest throughput (6.67 bits/s), the least movement time (0.5 s), and was the most preferred input. While there are similarities in how quickly pointing can be moved from source to target location when using both gaze and touch inputs, target selection consumes maximum time with gaze input. Hence, with a throughput that is over 160% higher than gaze, touch proves to be a superior input modality.
A modified bond-based peridynamics model without limitations on elastic properties
Venue: arXiv cs.CE 2022
Year: 2022

This study proposes a novel Modified Bond-Based PeriDynamic (MBB-PD) model based on the bonds' classification. This classification of bonds is performed on the basis of the equivalent hypothetical local strains and falls into three categories of horizontal normal, vertical normal, and shear bonds. While the classical Bond-Based PD (BB-PD) considers only the stretch of bonds, all components of the bonds' strains are taken into account in the proposed model.A local imaginary element is considered around each bond to estimate the true strains of each bond. The constitutive relations are derived from equating the strain energies of the bonds' deformations to the Classical Continuum Mechanics (CCM) strain energies for a generalized combined loading condition. A novel critical stretch criterion and critical angle criterion are proposed to predict the failure of normal and shear strain bonds, respectively.It is also shown that, unlike the classical BB-PD, the proposed model does not impose any limitations on the value of Poisson's ratio. The model is verified by investigating some intact plane stress and plane strain problems under mechanical and thermal loadings. Moreover, the deformation and damage contours and the corresponding stress-strain responses are presented for different problems with pre-existing defects and validated with the eXtended Finite Element method's (XFEM) analysis.
What can we Learn by Predicting Accuracy?
Venue: arXiv cs.LG 2022
Year: 2022

This paper seeks to answer the following question: \textit{"What can we learn by predicting accuracy?"}. Indeed, classification is one of the most popular tasks in machine learning, and many loss functions have been developed to maximize this non-differentiable objective function. Unlike past work on loss function design, which was guided mainly by intuition and theory before being validated by experimentation, here we propose to approach this problem in the opposite way: we seek to extract knowledge by experimentation. This data-driven approach is similar to that used in physics to discover general laws from data. We used a symbolic regression method to automatically find a mathematical expression highly correlated with a linear classifier's accuracy. The formula discovered on more than 260 datasets of embeddings has a Pearson's correlation of 0.96 and a $r^2$ of 0.93. More interestingly, this formula is highly explainable and confirms insights from various previous papers on loss design. We hope this work will open new perspectives in the search for new heuristics leading to a deeper understanding of machine learning theory.
The Face of Affective Disorders
Venue: arXiv cs.CV 2022
Year: 2022

We study the statistical properties of facial behaviour altered by the regulation of brain arousal in the clinical domain of psychiatry. The underlying mechanism is linked to the empirical interpretation of the vigilance continuum as behavioral surrogate measurement for certain states of mind. Referring to the classical scalp-based obtrusive measurements, we name the presented method Opto-Electronic Encephalography (OEG) which solely relies on modern camera-based real-time signal processing and computer vision. Based upon a stochastic representation as coherence of the face dynamics, reflecting the hemifacial asymmetry in emotion expressions, we demonstrate an almost flawless distinction between patients and healthy controls as well as between the mental disorders depression and schizophrenia and the symptom severity. In contrast to the standard diagnostic process, which is time-consuming, subjective and does not incorporate neurobiological data such as real-time face dynamics, the objective stochastic modeling of the affective responsiveness only requires a few minutes of video-based facial recordings. We also highlight the potential of the methodology as a causal inference model in transdiagnostic analysis to predict the outcome of pharmacological treatment. All results are obtained on a clinical longitudinal data collection with an amount of 99 patients and 43 controls.
DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction
Venue: arXiv cs.LG 2022
Year: 2022

Leveraging labelled data from multiple domains to enable prediction in another domain without labels is a significant, yet challenging problem. To address this problem, we introduce the framework DAPDAG (\textbf{D}omain \textbf{A}daptation via \textbf{P}erturbed \textbf{DAG} Reconstruction) and propose to learn an auto-encoder that undertakes inference on population statistics given features and reconstructing a directed acyclic graph (DAG) as an auxiliary task. The underlying DAG structure is assumed invariant among observed variables whose conditional distributions are allowed to vary across domains led by a latent environmental variable $E$. The encoder is designed to serve as an inference device on $E$ while the decoder reconstructs each observed variable conditioned on its graphical parents in the DAG and the inferred $E$. We train the encoder and decoder jointly in an end-to-end manner and conduct experiments on synthetic and real datasets with mixed variables. Empirical results demonstrate that reconstructing the DAG benefits the approximate inference. Furthermore, our approach can achieve competitive performance against other benchmarks in prediction tasks, with better adaptation ability, especially in the target domain significantly different from the source domains.
Open innovation using open source tools: a case study at Sony Mobile
Venue: arXiv cs.SE 2022
Year: 2022

Despite growing interest of Open Innovation (OI) in Software Engineering (SE), little is known about what triggers software organizations to adopt it and how this affects SE practices. OI can be realized in numerous of ways, including Open Source Software (OSS) involvement. Outcomes from OI are not restricted to product innovation but also include process innovation, e.g. improved SE practices and methods. This study explores the involvement of a software organization (Sony Mobile) in OSS communities from an OI perspective and what SE practices (requirements engineering and testing) have been adapted in relation to OI. It also highlights the innovative outcomes resulting from OI. An exploratory embedded case study investigates how Sony Mobile use and contribute to Jenkins and Gerrit; the two central OSS tools in their continuous integration tool chain. Quantitative analysis was performed on change log data from source code repositories in order to identify the top contributors and triangulated with the results from five semi-structured interviews to explore the nature of the commits. The findings of the case study include five major themes: i) The process of opening up towards the tool communities correlates in time with a general adoption of OSS in the organization. ii) Assets not seen as competitive advantage nor a source of revenue are made open to OSS communities, and gradually, the organization turns more open. iii) The requirements engineering process towards the community is informal and based on engagement. iv) The need for systematic and automated testing is still in its infancy, but the needs are identified. v) The innovation outcomes included free features and maintenance, and were believed to increase speed and quality in development. Adopting OI was a result of a paradigm shift of moving from Windows to Linux.
Locating modifications in signed data for partial data integrity
Venue: arXiv cs.CR 2022
Year: 2022

We consider the problem of detecting and locating modifications in signed data to ensure partial data integrity. We assume that the data is divided into $n$ blocks (not necessarily of the same size) and that a threshold $d$ is given for the maximum amount of modified blocks that the scheme can support. We propose efficient algorithms for signature and verification steps which provide a reasonably compact signature size, for controlled sizes of $d$ with respect to $n$. For instance, for fixed $d$ the standard signature size gets multiplied by a factor of $O(\log n)$, while allowing the identification of up to $d$ modified blocks. Our scheme is based on nonadaptive combinatorial group testing and cover-free families.
Superdirective Arrays with Finite-Length Dipoles: Modeling and New Perspectives
Venue: arXiv cs.IT 2022
Year: 2022

Dense arrays can facilitate the integration of multiple antennas into finite volumes. In addition to the compact size, sub-wavelength spacing enables superdirectivity for endfire operation, a phenomenon that has been mainly studied for isotropic and infinitesimal radiators. In this work, we focus on linear dipoles of arbitrary yet finite length. Specifically, we first introduce an array model that accounts for the sinusoidal current distribution (SCD) on very thin dipoles. Based on the SCD, the loss resistance of each dipole antenna is precisely determined. Capitalizing on the derived model, we next investigate the maximum achievable rate under a fixed power constraint. The optimal design entails conjugate power matching along with maximizing the array gain. Our theoretical analysis is corroborated by the method of moments under the thin-wire approximation, as well as by full-wave simulations. Numerical results showcase that a super-gain is attainable with high radiation efficiency when the dipole antennas are not too short and thin.
Stratified Certification for k-Induction
Venue: arXiv cs.LO 2022
Year: 2022

Our recently proposed certification framework for bit-level k-induction-based model checking has been shown to be quite effective in increasing the trust of verification results even though it partially involved quantifier reasoning. In this paper we show how to simplify the approach by assuming reset functions to be stratified. This way it can be lifted to word-level and in principle to other theories where quantifier reasoning is difficult. Our new method requires six simple SAT checks and one polynomial-time check, allowing certification to remain in co-NP while the previous approach required five SAT checks and one QBF check. Experimental results show a substantial performance gain for our new approach. Finally, we present and evaluate our new tool Certifaiger-wl which is able to certify k-induction-based word-level model checking.
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Venue: arXiv cs.CL 2022
Year: 2022

Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML knowledge, which poses a significant barrier for many potential users. To lift this barrier, we introduce Label Sleuth, a free open source system for labeling and creating text classifiers. This system is unique for (a) being a no-code system, making NLP accessible to non-experts, (b) guiding users through the entire labeling process until they obtain a custom classifier, making the process efficient -- from cold start to classifier in a few hours, and (c) being open for configuration and extension by developers. By open sourcing Label Sleuth we hope to build a community of users and developers that will broaden the utilization of NLP models.
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents an open and comprehensive framework to systematically evaluate state-of-the-art contributions to self-supervised monocular depth estimation. This includes pretraining, backbone, architectural design choices and loss functions. Many papers in this field claim novelty in either architecture design or loss formulation. However, simply updating the backbone of historical systems results in relative improvements of 25%, allowing them to outperform the majority of existing systems. A systematic evaluation of papers in this field was not straightforward. The need to compare like-with-like in previous papers means that longstanding errors in the evaluation protocol are ubiquitous in the field. It is likely that many papers were not only optimized for particular datasets, but also for errors in the data and evaluation criteria. To aid future research in this area, we release a modular codebase (https://github.com/jspenmar/monodepth_benchmark), allowing for easy evaluation of alternate design decisions against corrected data and evaluation criteria. We re-implement, validate and re-evaluate 16 state-of-the-art contributions and introduce a new dataset (SYNS-Patches) containing dense outdoor depth maps in a variety of both natural and urban scenes. This allows for the computation of informative metrics in complex regions such as depth boundaries.
Towards Psychologically-Grounded Dynamic Preference Models
Venue: arXiv cs.IR 2022
Year: 2022

Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior and psychological condition. We argue that modeling the influence of recommendations on people's preferences must be grounded in psychologically plausible models. We contribute a methodology for developing grounded dynamic preference models. We demonstrate this method with models that capture three classic effects from the psychology literature: Mere-Exposure, Operant Conditioning, and Hedonic Adaptation. We conduct simulation-based studies to show that the psychological models manifest distinct behaviors that can inform system design. Our study has two direct implications for dynamic user modeling in recommendation systems. First, the methodology we outline is broadly applicable for psychologically grounding dynamic preference models. It allows us to critique recent contributions based on their limited discussion of psychological foundation and their implausible predictions. Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design. In an example, we show that engagement and diversity metrics may be unable to capture desirable recommendation system performance.
Safe Supervisory Control of Soft Robot Actuators
Venue: arXiv cs.RO 2022
Year: 2022

Although soft robots show safer interactions with their environment than traditional robots, soft mechanisms and actuators still have significant potential for damage or degradation particularly during unmodeled contact. This article introduces a feedback strategy for safe soft actuator operation during control of a soft robot. To do so, a supervisory controller monitors actuator state and dynamically saturates control inputs to avoid conditions that could lead to physical damage. We prove that, under certain conditions, the supervisory controller is stable and verifiably safe. We then demonstrate completely onboard operation of the supervisory controller using a soft thermally-actuated robot limb with embedded shape memory alloy (SMA) actuators and sensing. Tests performed with the supervisor verify its theoretical properties and show stabilization of the robot limb's pose in free space. Finally, experiments show that our approach prevents overheating during contact (including environmental constraints and human contact) or when infeasible motions are commanded. This supervisory controller, and its ability to be executed with completely onboard sensing, has the potential to make soft robot actuators reliable enough for practical use.
Research Contribution of major Centrally Funded Institution Systems of India
Venue: arXiv cs.DL 2022
Year: 2022

India is now among the major knowledge producers of the world, ranking among the top 5 countries in total research output, as per some recent reports. The institutional setup for Research & Development (R&D) in India comprises a diverse set of Institutions, including Universities, government departments, research laboratories, and private sector institutions etc. It may be noted that more than 45% share of India's Gross Expenditure on Research and Development (GERD) comes from the central government. In this context, this article attempts to explore the quantum of research contribution of centrally funded institutions and institution systems of India. The volume, proportionate share and growth patterns of research publications from the major centrally funded institutions, organised in 16 groups, is analysed. These institutions taken together account for 67.54% of Indian research output during 2001 to 2020. The research output of the centrally funded institutions in India has increased steadily since 2001 with a good value for CAGR. The paper presents noteworthy insights about scientific research production of India that may be useful to policymakers, researchers and science practitioners in India. It presents a case for increased activity by the state governments and private sector to further the cause of sustainable and inclusive research and development in the country.
Principles of Query Visualization
Venue: arXiv cs.DB 2022
Year: 2022

Query Visualization (QV) is the problem of transforming a given query into a graphical representation that helps humans understand its meaning. This task is notably different from designing a Visual Query Language (VQL) that helps a user compose a query. This article discusses the principles of relational query visualization and its potential for simplifying user interactions with relational data.
Curvature-informed multi-task learning for graph networks
Venue: arXiv cs.LG 2022
Year: 2022

Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws of physics. However, when state-of-the-art graph neural networks attempt to predict multiple properties simultaneously (the multi-task learning (MTL) setting), they frequently underperform a suite of single property predictors. This suggests graph networks may not be fully leveraging these underlying similarities. Here we investigate a potential explanation for this phenomenon: the curvature of each property's loss surface significantly varies, leading to inefficient learning. This difference in curvature can be assessed by looking at spectral properties of the Hessians of each property's loss function, which is done in a matrix-free manner via randomized numerical linear algebra. We evaluate our hypothesis on two benchmark datasets (Materials Project (MP) and QM8) and consider how these findings can inform the training of novel multi-task learning models.
Differentiable Subdivision Surface Fitting
Venue: arXiv cs.GR 2022
Year: 2022

In this paper, we present a powerful differentiable surface fitting technique to derive a compact surface representation for a given dense point cloud or mesh, with application in the domains of graphics and CAD/CAM. We have chosen the Loop subdivision surface, which in the limit yields the smooth surface underlying the point cloud, and can handle complex surface topology better than other popular compact representations, such as NURBS. The principal idea is to fit the Loop subdivision surface not directly to the point cloud, but to the IMLS (implicit moving least squares) surface defined over the point cloud. As both Loop subdivision and IMLS have analytical expressions, we are able to formulate the problem as an unconstrained minimization problem of a completely differentiable function that can be solved with standard numerical solvers. Differentiability enables us to integrate the subdivision surface into any deep learning method for point clouds or meshes. We demonstrate the versatility and potential of this approach by using it in conjunction with a differentiable renderer to robustly reconstruct compact surface representations of spatial-temporal sequences of dense meshes.
Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing
Venue: arXiv cs.NI 2022
Year: 2022

Recently, open radio access network (O-RAN) has become a promising technology to provide an open environment for network vendors and operators. Coordinating the x-applications (xAPPs) is critical to increase flexibility and guarantee high overall network performance in O-RAN. Meanwhile, federated reinforcement learning has been proposed as a promising technique to enhance the collaboration among distributed reinforcement learning agents and improve learning efficiency. In this paper, we propose a federated deep reinforcement learning algorithm to coordinate multiple independent xAPPs in O-RAN for network slicing. We design two xAPPs, namely a power control xAPP and a slice-based resource allocation xAPP, and we use a federated learning model to coordinate two xAPP agents to enhance learning efficiency and improve network performance. Compared with conventional deep reinforcement learning, our proposed algorithm can achieve 11% higher throughput for enhanced mobile broadband (eMBB) slices and 33% lower delay for ultra-reliable low-latency communication (URLLC) slices.