OpenRaiser/Intern-Atlas huggingface dataset:
Progressive Motion Context Refine Network for Efficient Video Frame Interpolation
Venue: arXiv cs.CV 2022
Year: 2022

Recently, flow-based frame interpolation methods have achieved great success by first modeling optical flow between target and input frames, and then building synthesis network for target frame generation. However, above cascaded architecture can lead to large model size and inference delay, hindering them from mobile and real-time applications. To solve this problem, we propose a novel Progressive Motion Context Refine Network (PMCRNet) to predict motion fields and image context jointly for higher efficiency. Different from others that directly synthesize target frame from deep feature, we explore to simplify frame interpolation task by borrowing existing texture from adjacent input frames, which means that decoder in each pyramid level of our PMCRNet only needs to update easier intermediate optical flow, occlusion merge mask and image residual. Moreover, we introduce a new annealed multi-scale reconstruction loss to better guide the learning process of this efficient PMCRNet. Experiments on multiple benchmarks show that proposed approaches not only achieve favorable quantitative and qualitative results but also reduces current model size and running time significantly.
Continent-wide Efficient and Fair Downlink Resource Allocation in LEO Satellite Constellations
Venue: arXiv cs.NI 2022
Year: 2022

The integration of Low Earth Orbit (LEO) satellite constellations into 5G and Beyond is essential to achieve efficient global connectivity. As LEO satellites are a global infrastructure with predictable dynamics, a pre-planned fair and load-balanced allocation of the radio resources to provide efficient downlink connectivity over large areas is an achievable goal. In this paper, we propose a distributed and a global optimal algorithm for satellite-to-cell resource allocation with multiple beams. These algorithms aim to achieve a fair allocation of time-frequency resources and beams to the cells based on the number of users in connected mode (i.e., registered). Our analyses focus on evaluating the trade-offs between average per-user throughput, fairness, number of cell handovers, and computational complexity in a downlink scenario with fixed cells, where the number of users is extracted from a population map. Our results show that both algorithms achieve a similar average per-user throughput. However, the global optimal algorithm achieves a fairness index over 0.9 in all cases, which is more than twice that of the distributed algorithm. Furthermore, by correctly setting the handover cost parameter, the number of handovers can be effectively reduced by more than 70% with respect to the case where the handover cost is not considered.
Bounding Box Priors for Cell Detection with Point Annotations
Venue: arXiv cs.CV 2022
Year: 2022

The size of an individual cell type, such as a red blood cell, does not vary much among humans. We use this knowledge as a prior for classifying and detecting cells in images with only a few ground truth bounding box annotations, while most of the cells are annotated with points. This setting leads to weakly semi-supervised learning. We propose replacing points with either stochastic (ST) boxes or bounding box predictions during the training process. The proposed "mean-IOU" ST box maximizes the overlap with all the boxes belonging to the sample space with a class-specific approximated prior probability distribution of bounding boxes. Our method trains with both box- and point-labelled images in conjunction, unlike the existing methods, which train first with box- and then point-labelled images. In the most challenging setting, when only 5% images are box-labelled, quantitative experiments on a urine dataset show that our one-stage method outperforms two-stage methods by 5.56 mAP. Furthermore, we suggest an approach that partially answers "how many box-labelled annotations are necessary?" before training a machine learning model.
Requirements Quality vs Process and Stakeholders' Well-being: A Case of a Nordic Bank
Venue: arXiv cs.SE 2022
Year: 2022

Requirements are key artefacts to describe the intended purpose of a software system. The quality of requirements is crucial for deciding what to do next, impacting the development process's effectiveness and efficiency. However, we know very little about the connection between practitioners' perceptions regarding requirements quality and its impact on the process or the feelings of the professionals involved in the development process. Objectives: This study investigates: i) How software development practitioners define requirements quality, ii) how the perceived quality of requirements impact process and stakeholders' well-being, and iii) what are the causes and potential solutions for poor-quality requirements. Method: This study was performed as a descriptive interview study at a sub-organization of a Nordic bank that develops its own web and mobile apps. The data collection comprises interviews with 20 practitioners, including requirements engineers, developers, testers, and newly employed developers, with five interviewees from each group. Results: The results show that different roles have different views on what makes a requirement good quality. Participants highlighted that, in general, they experience negative emotions, more work, and overhead communication when they work with requirements they perceive to be of poor quality. The practitioners also describe positive effects on their performance and positive feelings when they work with requirements that they perceive to be good.
The Effect of the Russian-Ukrainian Conflict from the Perspective of Internet eXchanges
Venue: arXiv cs.NI 2022
Year: 2022

In 2022 the Russian invasion of Ukraine began. It is known that Ukraine faced outages because of the damage to their infrastructure. It is also known that Russia was boycotted by the international community. However the impact on the telecommunications of the two countries remains unknown. In this paper we quantified the degree to which the Internet was affected in both countries by analyzing routing tables from five large Internet Exchange Points (IXPs). IXPs provide a central point of interconnection where internet traffic can be freely exchanged between Autonomous Systems (ASes). This centrality makes IXPs a good vantage point for analyzing changes in the Internet infrastructure. With data collected before and after the start of the conflict we observed considerable damage to the Ukrainian Internet network with numerous outages and minimal damage to the Russian network. An average of 11.12% of Ukrainian ASes were unreachable at each IXP. We identified the biggest outages and the events responsible for them. This paper highlights resilience issues during conflicts to the network and management community, and serves as a basis for future more in-depth research.
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach
Venue: arXiv cs.LG 2022
Year: 2022

The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these systems is fleet rebalancing, i.e., how EVs should be repositioned to better satisfy future demand. This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i.e., the legitimate targets for rebalancing operations can vary over time. We tackle these challenges by first investigating rich sets of data collected from a real-world shared e-mobility system for one year, analyzing the operation model, usage patterns and expansion dynamics of this new mobility mode. With the learned knowledge we design a high-fidelity simulator, which is able to abstract key operation details of EV sharing at fine granularity. Then we model the rebalancing task for shared e-mobility systems under continuous expansion as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We further propose a novel policy optimization approach with action cascading, which is able to cope with the expansion dynamics and solve the formulated MARL. We evaluate the proposed approach extensively, and experimental results show that our approach outperforms the state-of-the-art, offering significant performance gain in both satisfied demand and net revenue.
Emergence of Concepts in DNNs?
Venue: arXiv cs.LG 2022
Year: 2022

The present paper reviews and discusses work from computer science that proposes to identify concepts in internal representations (hidden layers) of DNNs. It is examined, first, how existing methods actually identify concepts that are supposedly represented in DNNs. Second, it is discussed how conceptual spaces -- sets of concepts in internal representations -- are shaped by a tradeoff between predictive accuracy and compression. These issues are critically examined by drawing on philosophy. While there is evidence that DNNs able to represent non-trivial inferential relations between concepts, our ability to identify concepts is severely limited.
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
Venue: arXiv cs.LG 2022
Year: 2022

It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. Fair ML has largely focused on the protection of single attributes in the simpler setting where both attributes and target outcomes are binary. However, the practical application in many a real-world problem entails the simultaneous protection of multiple sensitive attributes, which are often not simply binary, but continuous or categorical. To address this more challenging task, we introduce FairCOCCO, a fairness measure built on cross-covariance operators on reproducing kernel Hilbert Spaces. This leads to two practical tools: first, the FairCOCCO Score, a normalised metric that can quantify fairness in settings with single or multiple sensitive attributes of arbitrary type; and second, a subsequent regularisation term that can be incorporated into arbitrary learning objectives to obtain fair predictors. These contributions address crucial gaps in the algorithmic fairness literature, and we empirically demonstrate consistent improvements against state-of-the-art techniques in balancing predictive power and fairness on real-world datasets.
FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection
Venue: arXiv cs.CV 2022
Year: 2022

Due to its importance in facial behaviour analysis, facial action unit (AU) detection has attracted increasing attention from the research community. Leveraging the online knowledge distillation framework, we propose the ``FANTrans" method for AU detection. Our model consists of a hybrid network of convolution and transformer blocks to learn per-AU features and to model AU co-occurrences. The model uses a pre-trained face alignment network as the feature extractor. After further transformation by a small learnable add-on convolutional subnet, the per-AU features are fed into transformer blocks to enhance their representation. As multiple AUs often appear together, we propose a learnable attention drop mechanism in the transformer block to learn the correlation between the features for different AUs. We also design a classifier that predicts AU presence by considering all AUs' features, to explicitly capture label dependencies. Finally, we make the attempt of adapting online knowledge distillation in the training stage for this task, further improving the model's performance. Experiments on the BP4D and DISFA datasets demonstrating the effectiveness of proposed method.
Unimodal and Multimodal Representation Training for Relation Extraction
Venue: arXiv cs.CL 2022
Year: 2022

Multimodal integration of text, layout and visual information has achieved SOTA results in visually rich document understanding (VrDU) tasks, including relation extraction (RE). However, despite its importance, evaluation of the relative predictive capacity of these modalities is less prevalent. Here, we demonstrate the value of shared representations for RE tasks by conducting experiments in which each data type is iteratively excluded during training. In addition, text and layout data are evaluated in isolation. While a bimodal text and layout approach performs best (F1=0.684), we show that text is the most important single predictor of entity relations. Additionally, layout geometry is highly predictive and may even be a feasible unimodal approach. Despite being less effective, we highlight circumstances where visual information can bolster performance. In total, our results demonstrate the efficacy of training joint representations for RE.
An Integrity-Focused Threat Model for Software Development Pipelines
Venue: arXiv cs.CR 2022
Year: 2022

In recent years, there has been a growing concern with software integrity, that is, the assurance that software has not been tampered with on the path between developers and users. This path is represented by a software development pipeline and plays a pivotal role in software supply chain security. While there have been efforts to improve the security of development pipelines, there is a lack of a comprehensive view of the threats affecting them. We develop a systematic threat model for a generic software development pipeline using the STRIDE framework and identify possible mitigations for each threat. The pipeline adopted as a reference comprises five stages (integration, continuous integration, infrastructure-as-code, deployment, and release), and we review vulnerabilities and attacks in all stages reported in the literature. We present a case study applying this threat model to a specific pipeline, showing that the adaptation is straightforward and produces a list of relevant threats.
A geometric approach towards inverse kinematics of soft extensible pneumatic actuators intended for trajectory tracking
Venue: arXiv cs.RO 2022
Year: 2022

Soft robots are interesting examples of hyper-redundancy in robotics, however, the nonlinear continuous dynamics of these robots and the use of hyper-elastic and visco-elastic materials makes modeling of these robots more complicated. This study presents a geometric Inverse Kinematic (IK) model for trajectory tracking of multi-segment extensible soft robots, where, each segment of the soft actuator is geometrically approximated with multiple rigid links connected with rotary and prismatic joints. Using optimization methods, the desired configuration variables of the soft actuator for the desired end-effector positions are obtained. Also, the redundancy of the robot is applied for second task applications, such as tip angle control. The model's performance is investigated through simulations, numerical benchmarks, and experimental validations and results show lower computational costs and higher accuracy compared to most existing methods. The method is easy to apply to multi segment soft robots, both in 2D and 3D. As a case study, a fully 3D-printed soft robot manipulator is tested using a control unit and the model predictions show good agreement with the experimental results.
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds
Venue: arXiv cs.LG 2022
Year: 2022

Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate) likelihoods, two common choices for approximate inference are Expectation Propagation (EP) and Variational Inference (VI), which have complementary strengths and weaknesses. While VI's lower bound to the marginal likelihood is a suitable objective for inferring the approximate posterior, it does not automatically imply it is a good learning objective for hyperparameter optimization. We design a hybrid training procedure where the inference leverages conjugate-computation VI and the learning uses an EP-like marginal likelihood approximation. We empirically demonstrate on binary classification that this provides a good learning objective and generalizes better.
Approximate Max-Flow Min-Multicut Theorem for Graphs of Bounded Treewidth
Venue: arXiv cs.DS 2022
Year: 2022

We prove an approximate max-multiflow min-multicut theorem for bounded treewidth graphs. In particular, we show the following: Given a treewidth-$r$ graph, there exists a (fractional) multicommodity flow of value $f$, and a multicut of capacity $c$ such that $ f \leq c \leq \mathcal{O}(\ln (r+1)) \cdot f$. It is well known that the multiflow-multicut gap on an $r$-vertex (constant degree) expander graph can be $Ω(\ln r)$, and hence our result is tight up to constant factors. Our proof is constructive, and we also obtain a polynomial time $\mathcal{O}(\ln (r+1))$-approximation algorithm for the minimum multicut problem on treewidth-$r$ graphs. Our algorithm proceeds by rounding the optimal fractional solution to the natural linear programming relaxation of the multicut problem. We introduce novel modifications to the well-known region growing algorithm to facilitate the rounding while guaranteeing at most a logarithmic factor loss in the treewidth.
A novel approach to preventing SARS-CoV-2 transmission in classrooms: An OpenFOAM based CFD Study
Venue: arXiv cs.CY 2022
Year: 2022

The education sector has suffered a catastrophic setback due to ongoing COVID-pandemic, with classrooms being closed indefinitely. The current study aims to solve the existing dilemma by examining COVID transmission inside a classroom and providing long-term sustainable solutions. In this work, a standard 5m x 3m x 5m classroom is considered where 24 students are seated, accompanied by a teacher. A computational fluid dynamics simulation based on OpenFOAM is performed using a Eulerian-Lagrangian framework. Based on the stochastic dose response framework, we have evaluated the infection risk in the classroom for two distinct cases: (i) certain students are infected (ii) the teacher is infected. If the teacher is infected, the probability of infection could reach 100% for certain students. When certain students are infected, the maximum infection risk for a susceptible person reaches 30%. The commonly used cloth mask proves to be ineffective in providing protection against infection transmission reducing the maximum infection probability by approximately 26% only. Another commonly used solution in the form of shields installed on desks have also failed to provide adequate protection against infection reducing the infection risk only by 50%. Furthermore, the shields serves as a source of fomite mode of infection. Screens suspended from the ceiling, which entrap droplets, have been proposed as a novel solution that reduces the infection risk by 90% and 95% compared to the no screen scenario besides being completely devoid of fomite infection mode. As a result of the screens, the class-time can be extended by 55 minutes.
CryptoHalal: An Intelligent Decision-System for Identifying Halal and Haram Cryptocurrencies
Venue: arXiv cs.CY 2022
Year: 2022

In this research, we discussed a rising issue for Muslims in today world that involves a financial and technical innovation, namely: cryptocurrencies. We found out through a questionnaire that many Muslims are having a hard time finding the jurisprudence rulings on certain cryptocurrencies. Therefore, the objective of this research is to investigate and identify features that play a part in determining the jurisprudence rulings on cryptocurrencies. We have collected a dataset containing 106 cryptocurrencies classified into 56 Halal and 50 Haram cryptocurrencies, and used 20 handcrafted features. Moreover, based on these identified features, we designed an intelligent system that contains a Machine Learning model for classifying cryptocurrencies into Halal and Haram.
Sensor Visibility Estimation: Metrics and Methods for Systematic Performance Evaluation and Improvement
Venue: arXiv cs.CV 2022
Year: 2022

Sensor visibility is crucial for safety-critical applications in automotive, robotics, smart infrastructure and others: In addition to object detection and occupancy mapping, visibility describes where a sensor can potentially measure or is blind. This knowledge can enhance functional safety and perception algorithms or optimize sensor topologies. Despite its significance, to the best of our knowledge, neither a common definition of visibility nor performance metrics exist yet. We close this gap and provide a definition of visibility, derived from a use case review. We introduce metrics and a framework to assess the performance of visibility estimators. Our metrics are verified with labeled real-world and simulation data from infrastructure radars and cameras: The framework easily identifies false visible or false invisible estimations which are safety-critical. Applying our metrics, we enhance the radar and camera visibility estimators by modeling the 3D elevation of sensor and objects. This refinement outperforms the conventional planar 2D approach in trustfulness and thus safety.
Health Guardian Platform: A technology stack to accelerate discovery in Digital Health research
Venue: arXiv cs.CY 2022
Year: 2022

This paper highlights the design philosophy and architecture of the Health Guardian, a platform developed by the IBM Digital Health team to accelerate discoveries of new digital biomarkers and development of digital health technologies. The Health Guardian allows for rapid translation of artificial intelligence (AI) research into cloud-based microservices that can be tested with data from clinical cohorts to understand disease and enable early prevention. The platform can be connected to mobile applications, wearables, or Internet of things (IoT) devices to collect health-related data into a secure database. When the analytics are created, the researchers can containerize and deploy their code on the cloud using pre-defined templates, and validate the models using the data collected from one or more sensing devices. The Health Guardian platform currently supports time-series, text, audio, and video inputs with 70+ analytic capabilities and is used for non-commercial scientific research. We provide an example of the Alzheimer's disease (AD) assessment microservice which uses AI methods to extract linguistic features from audio recordings to evaluate an individual's mini-mental state, the likelihood of having AD, and to predict the onset of AD before turning the age of 85. Today, IBM research teams across the globe use the Health Guardian internally as a test bed for early-stage research ideas, and externally with collaborators to support and enhance AI model development and clinical study efforts.
Autonomous Multirotor Landing on Landing Pads and Lava Flows
Venue: arXiv cs.RO 2022
Year: 2022

Landing is a challenging part of autonomous drone flight and a great research opportunity. This PhD proposes to improve on fiducial autonomous landing algorithms by making them more flexible. Further, it leverages its location, Iceland, to develop a method for landing on lava flows in cooperation with analog Mars exploration missions taking place in Iceland now - and potentially for future Mars landings.