OpenRaiser/Intern-Atlas huggingface dataset:
Towards Activity-Centric Access Control for Smart Collaborative Ecosystems
Venue: arXiv cs.CR 2021
Year: 2021

The ubiquitous presence of smart devices along with advancements in connectivity coupled with the elastic capabilities of cloud and edge systems have nurtured and revolutionized smart ecosystems. Intelligent, integrated cyber-physical systems offer increased productivity, safety, efficiency, speed and support for data driven applications beyond imagination just a decade ago. Since several connected devices work together as a coordinated unit to ensure efficiency and automation, the individual operations they perform are often reliant on each other. Therefore, it is important to control what functions or activities different devices can perform at a particular moment of time, and how they are related to each other. It is also important to consider additional factors such as conditions, obligation or mutability of activities, which are critical in deciding whether or not a device can perform a requested activity. In this paper, we take an initial step to propose and discuss the concept of Activity-Centric Access Control (ACAC) for smart and connected ecosystem. We discuss the notion of activity with respect to the collaborative and distributed yet integrated systems and identify the different entities involved along with the important factors to make an activity control decision. We outline a preliminary approach for defining activity control expressions which can be applied to different smart objects in the system. The main goal of this paper is to present the vision and need for the activity-centric approach for access control in connected smart systems, and foster discussion on the identified future research agenda.
Domain-invariant NBV Planner for Active Cross-domain Self-localization
Venue: arXiv cs.CV 2021
Year: 2021

Pole-like landmark has received increasing attention as a domain-invariant visual cue for visual robot self-localization across domains (e.g., seasons, times of day, weathers). However, self-localization using pole-like landmarks can be ill-posed for a passive observer, as many viewpoints may not provide any pole-like landmark view. To alleviate this problem, we consider an active observer and explore a novel "domain-invariant" next-best-view (NBV) planner that attains consistent performance over different domains (i.e., maintenance-free), without requiring the expensive task of training data collection and retraining. In our approach, a novel multi-encoder deep convolutional neural network enables to detect domain invariant pole-like landmarks, which are then used as the sole input to a model-free deep reinforcement learning -based domain-invariant NBV planner. Further, we develop a practical system for active self-localization using sparse invariant landmarks and dense discriminative landmarks. In experiments, we demonstrate that the proposed method is effective both in efficient landmark detection and in discriminative self-localization.
Deep Deformation Detail Synthesis for Thin Shell Models
Venue: arXiv cs.CV 2021
Year: 2021

In physics-based cloth animation, rich folds and detailed wrinkles are achieved at the cost of expensive computational resources and huge labor tuning. Data-driven techniques make efforts to reduce the computation significantly by a database. One type of methods relies on human poses to synthesize fitted garments which cannot be applied to general cloth. Another type of methods adds details to the coarse meshes without such restrictions. However, existing works usually utilize coordinate-based representations which cannot cope with large-scale deformation, and requires dense vertex correspondences between coarse and fine meshes. Moreover, as such methods only add details, they require coarse meshes to be close to fine meshes, which can be either impossible, or require unrealistic constraints when generating fine meshes. To address these challenges, we develop a temporally and spatially as-consistent-as-possible deformation representation (named TS-ACAP) and a DeformTransformer network to learn the mapping from low-resolution meshes to detailed ones. This TS-ACAP representation is designed to ensure both spatial and temporal consistency for sequential large-scale deformations from cloth animations. With this representation, our DeformTransformer network first utilizes two mesh-based encoders to extract the coarse and fine features, respectively. To transduct the coarse features to the fine ones, we leverage the Transformer network that consists of frame-level attention mechanisms to ensure temporal coherence of the prediction. Experimental results show that our method is able to produce reliable and realistic animations in various datasets at high frame rates: 10 ~ 35 times faster than physics-based simulation, with superior detail synthesis abilities than existing methods.
Accelerating Recursive Partition-Based Causal Structure Learning
Venue: arXiv cs.LG 2021
Year: 2021

Causal structure discovery from observational data is fundamental to the causal understanding of autonomous systems such as medical decision support systems, advertising campaigns and self-driving cars. This is essential to solve well-known causal decision making and prediction problems associated with those real-world applications. Recently, recursive causal discovery algorithms have gained particular attention among the research community due to their ability to provide good results by using Conditional Independent (CI) tests in smaller sub-problems. However, each of such algorithms needs a refinement function to remove undesired causal relations of the discovered graphs. Notably, with the increase of the problem size, the computation cost (i.e., the number of CI-tests) of the refinement function makes an algorithm expensive to deploy in practice. This paper proposes a generic causal structure refinement strategy that can locate the undesired relations with a small number of CI-tests, thus speeding up the algorithm for large and complex problems. We theoretically prove the correctness of our algorithm. We then empirically evaluate its performance against the state-of-the-art algorithms in terms of solution quality and completion time in synthetic and real datasets.
The State-of-Practice in Requirements Elicitation: An Extended Interview Study at 12 Companies
Venue: arXiv cs.SE 2021
Year: 2021

Context. Requirements engineering remains a discipline that is faced with a large number of challenges, including the implementation of a requirements elicitation process in industry. Although several proposals have been suggested by researchers and academics, little is known of the practices that are actually followed in industry. Objective. We investigate the SoTA with respect to requirements elicitation, examining practitioners' practices. We focus on the techniques, the roles involved, and the challenges associated to the process. Method. We conducted an interview-based survey study involving 24 practitioners from 12 different Swedish IT companies. Results. We found that group interaction techniques, including meetings and workshops, are the most popular type of elicitation techniques that are employed by the practitioners, except in the case of small projects. We noted that customers are frequently involved in the elicitation process, except in the case of market-driven organizations. Technical staff (for example, developers and architects) are more frequently involved in the elicitation process compared to the involvement of business- or strategic staff. Finally, we identified a number of challenges with respect to stakeholders. These challenges include difficulties in understanding and prioritizing their needs. Further, it was noted that requirements instability (i.e., caused by changing needs or priorities) was a predominant challenge. These observations need to be interpreted in the context of the study. Conclusion. The relevant observations regarding the survey participants' experiences should be of interest to the industry; experiences that should be analyzed in the practitioners' context. Researchers may find evidence for the use of academic results in practice, thereby inspiring future theoretical work, as well as further empirical studies in the same area.
SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Visual Place Recognition (VPR) is the task of matching current visual imagery from a camera to images stored in a reference map of the environment. While initial VPR systems used simple direct image methods or hand-crafted visual features, recent work has focused on learning more powerful visual features and further improving performance through either some form of sequential matcher / filter or a hierarchical matching process. In both cases the performance of the initial single-image based system is still far from perfect, putting significant pressure on the sequence matching or (in the case of hierarchical systems) pose refinement stages. In this paper we present a novel hybrid system that creates a high performance initial match hypothesis generator using short learnt sequential descriptors, which enable selective control sequential score aggregation using single image learnt descriptors. Sequential descriptors are generated using a temporal convolutional network dubbed SeqNet, encoding short image sequences using 1-D convolutions, which are then matched against the corresponding temporal descriptors from the reference dataset to provide an ordered list of place match hypotheses. We then perform selective sequential score aggregation using shortlisted single image learnt descriptors from a separate pipeline to produce an overall place match hypothesis. Comprehensive experiments on challenging benchmark datasets demonstrate the proposed method outperforming recent state-of-the-art methods using the same amount of sequential information. Source code and supplementary material can be found at https://github.com/oravus/seqNet.
PIERES: A Playground for Network Interrupt Experiments on Real-Time Embedded Systems in the IoT
Venue: arXiv cs.NI 2021
Year: 2021

IoT devices have become an integral part of our lives and the industry. Many of these devices run real-time systems or are used as part of them. As these devices receive network packets over IP networks, the network interface informs the CPU about their arrival using interrupts that might preempt critical processes. Therefore, the question arises whether network interrupts pose a threat to the real-timeness of these devices. However, there are few tools to investigate this issue. We present a playground which enables researchers to conduct experiments in the context of network interrupt simulation. The playground comprises different network interface controller implementations, load generators and timing utilities. It forms a flexible and easy to use foundation for future network interrupt research. We conduct two verification experiments and two real world examples. The latter give insight into the impact of the interrupt handling strategy parameters and the influence of different load types on the execution time with respect to these parameters.
An ecologically valid examination of event-based and time-based prospective memory using immersive virtual reality: the influence of attention, memory, and executive function processes on real-world prospective memory
Venue: arXiv cs.CY 2021
Year: 2021

Studies on prospective memory (PM) predominantly assess either event- or time-based PM by implementing non-ecological laboratory-based tasks. The results deriving from these paradigms have provided findings that are discrepant with ecologically valid research paradigms that converge on the complexity and cognitive demands of everyday tasks. The Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive virtual reality (VR) neuropsychological battery with enhanced ecological validity, was implemented to assess everyday event- and time-based PM, as well as the influence of other cognitive functions on everyday PM functioning. The results demonstrated the importance of delayed recognition, planning, and visuospatial attention on everyday PM. Delayed recognition and planning ability were found to be central in event- and time-based PM respectively. In order of importance, delayed recognition, visuospatial attention speed, and planning ability were found to be involved in event-based PM functioning. Comparably, planning, visuospatial attention accuracy, delayed recognition, and multitasking/task-shifting ability were found to be involved in time-based PM functioning. These findings further suggest the importance of ecological validity in the study of PM, which may be achieved using immersive VR paradigms.
Testing Hamiltonicity (and other problems) in Minor-Free Graphs
Venue: arXiv cs.DS 2021
Year: 2021

In this paper we provide sub-linear algorithms for several fundamental problems in the setting in which the input graph excludes a fixed minor, i.e., is a minor-free graph. In particular, we provide the following algorithms for minor-free unbounded degree graphs. (1) A tester for Hamiltonicity with two-sided error with $poly(1/ε)$-query complexity, where $ε$ is the proximity parameter. (2) A local algorithm, as defined by Rubinfeld et al. (ICS 2011), for constructing a spanning subgraph with almost minimum weight, specifically, at most a factor $(1+ε)$ of the optimum, with $poly(1/ε)$-query complexity. Both our algorithms use partition oracles, a tool introduced by Hassidim et al. (FOCS 2009), which are oracles that provide access to a partition of the graph such that the number of cut-edges is small and each part of the partition is small. The polynomial dependence in $1/ε$ of our algorithms is achieved by combining the recent $poly(d/ε)$-query partition oracle of Kumar-Seshadhri-Stolman (ECCC 2021) for minor-free graphs with degree bounded by $d$. For bounded degree minor-free graphs we introduce the notion of covering partition oracles which is a relaxed version of partition oracles and design a $poly(d/ε)$-time covering partition oracle. Using our covering partition oracle we provide the same results as above (except that the tester for Hamiltonicity has one-sided error) for minor-free bounded degree graphs, as well as showing that any property which is monotone and additive (e.g. bipartiteness) can be tested in minor-free graphs by making $poly(d/ε)$-queries. The benefit of using the covering partition oracle rather than the partition oracle in our algorithms is its simplicity and an improved polynomial dependence in $1/ε$ in the obtained query complexity.
RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety
Venue: arXiv cs.CV 2021
Year: 2021

Automated monitoring and analysis of passenger movement in safety-critical parts of transport infrastructures represent a relevant visual surveillance task. Recent breakthroughs in visual representation learning and spatial sensing opened up new possibilities for detecting and tracking humans and objects within a 3D spatial context. This paper proposes a flexible analysis scheme and a thorough evaluation of various processing pipelines to detect and track humans on a ground plane, calibrated automatically via stereo depth and pedestrian detection. We consider multiple combinations within a set of RGB- and depth-based detection and tracking modalities. We exploit the modular concepts of Meshroom [2] and demonstrate its use as a generic vision processing pipeline and scalable evaluation framework. Furthermore, we introduce a novel open RGB-D railway platform dataset with annotations to support research activities in automated RGB-D surveillance. We present quantitative results for multiple object detection and tracking for various algorithmic combinations on our dataset. Results indicate that the combined use of depth-based spatial information and learned representations yields substantially enhanced detection and tracking accuracies. As demonstrated, these enhancements are especially pronounced in adverse situations when occlusions and objects not captured by learned representations are present.
Paraphrases do not explain word analogies
Venue: arXiv cs.CL 2021
Year: 2021

Many types of distributional word embeddings (weakly) encode linguistic regularities as directions (the difference between "jump" and "jumped" will be in a similar direction to that of "walk" and "walked," and so on). Several attempts have been made to explain this fact. We respond to Allen and Hospedales' recent (ICML, 2019) theoretical explanation, which claims that word2vec and GloVe will encode linguistic regularities whenever a specific relation of paraphrase holds between the four words involved in the regularity. We demonstrate that the explanation does not go through: the paraphrase relations needed under this explanation do not hold empirically.
QuPeL: Quantized Personalization with Applications to Federated Learning
Venue: arXiv cs.LG 2021
Year: 2021

Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face are heterogeneity in data across clients and collaboration of clients with {\em diverse resources}. In this work, we introduce a \textit{quantized} and \textit{personalized} FL algorithm QuPeL that facilitates collective training with heterogeneous clients while respecting resource diversity. For personalization, we allow clients to learn \textit{compressed personalized models} with different quantization parameters depending on their resources. Towards this, first we propose an algorithm for learning quantized models through a relaxed optimization problem, where quantization values are also optimized over. When each client participating in the (federated) learning process has different requirements of the quantized model (both in value and precision), we formulate a quantized personalization framework by introducing a penalty term for local client objectives against a globally trained model to encourage collaboration. We develop an alternating proximal gradient update for solving this quantized personalization problem, and we analyze its convergence properties. Numerically, we show that optimizing over the quantization levels increases the performance and we validate that QuPeL outperforms both FedAvg and local training of clients in a heterogeneous setting.
Data-driven analysis of central bank digital currency (CBDC) projects drivers
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we use a variety of machine learning methods to quantify the extent to which economic and technological factors are predictive of the progression of Central Bank Digital Currencies (CBDC) within a country, using as our measure of this progression the CBDC project index (CBDCPI). We find that a financial development index is the most important feature for our model, followed by the GDP per capita and an index of the voice and accountability of the country's population. Our results are consistent with previous qualitative research which finds that countries with a high degree of financial development or digital infrastructure have more developed CBDC projects. Further, we obtain robust results when predicting the CBDCPI at different points in time.
Usability and Security of Different Authentication Methods for an Electronic Health Records System
Venue: arXiv cs.CR 2021
Year: 2021

We conducted a survey of 67 graduate students enrolled in the Privacy and Security in Healthcare course at Indiana University Purdue University Indianapolis. This was done to measure user preference and their understanding of usability and security of three different Electronic Health Records authentication methods: single authentication method (username and password), Single sign-on with Central Authentication Service (CAS) authentication method, and a bio-capsule facial authentication method. This research aims to explore the relationship between security and usability, and measure the effect of perceived security on usability in these three aforementioned authentication methods. We developed a formative-formative Partial Least Square Structural Equation Modeling (PLS-SEM) model to measure the relationship between the latent variables of Usability, and Security. The measurement model was developed using five observed variables (measures). - Efficiency and Effectiveness, Satisfaction, Preference, Concerns, and Confidence. The results obtained highlight the importance and impact of these measures on the latent variables and the relationship among the latent variables. From the PLS-SEM analysis, it was found that security has a positive impact on usability for Single sign-on and bio-capsule facial authentication methods. We conclude that the facial authentication method was the most secure and usable among the three authentication methods. Further, descriptive analysis was done to draw out the interesting findings from the survey regarding the observed variables.
Event Camera Based Real-Time Detection and Tracking of Indoor Ground Robots
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents a real-time method to detect and track multiple mobile ground robots using event cameras. The method uses density-based spatial clustering of applications with noise (DBSCAN) to detect the robots and a single k-dimensional ($k - d$) tree to accurately keep track of them as they move in an indoor arena. Robust detections and tracks are maintained in the face of event camera noise and lack of events (due to robots moving slowly or stopping). An off-the-shelf RGB camera-based tracking system was used to provide ground truth. Experiments including up to 4 robots are performed to study the effect of i) varying DBSCAN parameters, ii) the event accumulation time, iii) the number of robots in the arena, iv) the speed of the robots, and v) variation in ambient light conditions on the detection and tracking performance. The experimental results showed 100% detection and tracking fidelity in the face of event camera noise and robots stopping for tests involving up to 3 robots (and upwards of 93% for 4 robots). When the lighting conditions were varied, a graceful degradation in detection and tracking fidelity was observed.
Convergence of Bayesian Nash Equilibrium in Infinite Bayesian Games under Discretization
Venue: arXiv cs.GT 2021
Year: 2021

We prove the existence of Bayesian Nash Equilibrium (BNE) of general-sum Bayesian games with continuous types and finite actions under the conditions that the utility functions and the prior type distributions are continuous concerning the players' types. Moreover, there exists a sequence of discretized Bayesian games whose BNE strategies converge weakly to a BNE strategy of the infinite Bayesian game. Our proof establishes a connection between the equilibria of the infinite Bayesian game and those of finite approximations, which leads to an algorithm to construct $\varepsilon$-BNE of infinite Bayesian games by discretizing players' type spaces.