OpenRaiser/Intern-Atlas huggingface dataset:
NOMA-Based Hybrid Satellite-UAV-Terrestrial Networks for Beyond 5G Maritime Internet of Things
Venue: arXiv cs.IT 2021
Year: 2021

Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are usually sparsely distributed on the vast ocean. The nearshore network also shares spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, as well as different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting the max-min optimization, auxiliary function method, and successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.
Progressive Semantic Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

The objective of this work is to segment high-resolution images without overloading GPU memory usage or losing the fine details in the output segmentation map. The memory constraint means that we must either downsample the big image or divide the image into local patches for separate processing. However, the former approach would lose the fine details, while the latter can be ambiguous due to the lack of a global picture. In this work, we present MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels. MagNet has multiple processing stages, where each stage corresponds to a magnification level, and the output of one stage is fed into the next stage for coarse-to-fine information propagation. Each stage analyzes the image at a higher resolution than the previous stage, recovering the previously lost details due to the lossy downsampling step, and the segmentation output is progressively refined through the processing stages. Experiments on three high-resolution datasets of urban views, aerial scenes, and medical images show that MagNet consistently outperforms the state-of-the-art methods by a significant margin.
Enabling Cross-Domain Communication: How to Bridge the Gap between AI and HW Engineers
Venue: arXiv cs.AR 2021
Year: 2021

A key issue in system design is the lack of communication between hardware, software and domain expert. Recent research work shows progress in automatic HW/SW co-design flows of neural accelerators that seems to make this kind of communication obsolete. Most real-world systems, however, are a composition of multiple processing units, communication networks and memories. A HW/SW co-design process of (reconfigurable) neural accelerators, therefore, is an important sub-problem towards a common co-design methodology. The ultimate challenge is to define the constraints for the design space exploration on system level - a task which requires deep knowledge and understanding of hardware architectures, mapping of workloads onto hardware and the application domain, e.g. artificial intelligence. For most projects, these skills are distributed among several people or even different teams which is one of the major reasons why there is no established end-to-end development methodology for digital systems. This position paper discusses possibilities how to establish such a methodology for systems that include (reconfigurable) dedicated accelerators and outlines the central role that languages and tools play in the process.
A Network-based Compute Reuse Architecture for IoT Applications
Venue: arXiv cs.NI 2021
Year: 2021

The tremendous advancements in the Internet of Things (IoT) increasingly involve computationally intensive services. These services often require more computation resources than can entirely be satisfied on local IoT devices. Cloud computing is traditionally used to provide unlimited computation resources at distant servers. However, such remote computation may not address the short-delay constraints that many of today's IoT applications require. Edge computing allows offloading computing close to end users to overcome computation and delay issues. Nonetheless, the edge servers may suffer from computing inefficiencies. Indeed, some IoT applications are invoked multiple times by multiple devices. These invocations are often used with the same or similar input data, which leads to the same computational output (results). Still, the edge server willfully executes all received redundant tasks. In this work, we investigate the use of the computation reuse concept at the edge server. We design a network-based computation reuse architecture for IoT applications. The architecture stores previously executed results and reuses them to satisfy newly arrived similar tasks instead of performing computation from scratch. By doing so, we eliminate redundant computation, optimize resource utilization, and decrease task completion time. We implemented the architecture and evaluated its performance both at the networking and application levels. From the networking perspective, we reach up to an 80\% reduction in task completion time and up to 60\% reduction in resource utilization. From the application perspective, we achieve up to 90\% computation correctness and accuracy.
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
Venue: arXiv cs.CV 2021
Year: 2021

We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier. We frame this task as an out-of-distribution (OOD) detection problem. Our novel approach, hierarchical outlier detection (HOD) assigns multiple abstention classes for each training outlier class and jointly performs a coarse classification of inliers vs. outliers, along with fine-grained classification of the individual classes. We demonstrate the effectiveness of the HOD loss in conjunction with modern representation learning approaches (BiT, SimCLR, MICLe) and explore different ensembling strategies for further improving the results. We perform an extensive subgroup analysis over conditions of varying risk levels and different skin types to investigate how the OOD detection performance changes over each subgroup and demonstrate the gains of our framework in comparison to baselines. Finally, we introduce a cost metric to approximate downstream clinical impact. We use this cost metric to compare the proposed method against a baseline system, thereby making a stronger case for the overall system effectiveness in a real-world deployment scenario.
Incentivizing Exploration in Linear Bandits under Information Gap
Venue: arXiv cs.LG 2021
Year: 2021

We study the problem of incentivizing exploration for myopic users in linear bandits, where the users tend to exploit arm with the highest predicted reward instead of exploring. In order to maximize the long-term reward, the system offers compensation to incentivize the users to pull the exploratory arms, with the goal of balancing the trade-off among exploitation, exploration and compensation. We consider a new and practically motivated setting where the context features observed by the user are more informative than those used by the system, e.g., features based on users' private information are not accessible by the system. We propose a new method to incentivize exploration under such information gap, and prove that the method achieves both sublinear regret and sublinear compensation. We theoretical and empirically analyze the added compensation due to the information gap, compared with the case that the system has access to the same context features as the user, i.e., without information gap. We also provide a compensation lower bound of our problem.
Hyperloop System Optimization
Venue: arXiv cs.CE 2021
Year: 2021

Hyperloop system design is a uniquely coupled problem because it involves the simultaneous design of a complex, high-performance vehicle and its accompanying infrastructure. In the clean-sheet design of this new mode of high-speed mass transportation there is an excellent opportunity for the application of rigorous system optimization techniques. This work presents a system optimization tool, HOPS, that has been adopted as a central component of the Virgin Hyperloop design process. We discuss the choice of objective function, the use of a convex optimization technique called geometric programming, and the level of modeling fidelity that has allowed us to capture the system's many intertwined, and often recursive, design relationships. We also highlight the ways in which the tool has been used. Because organizational confidence in a model is as vital as its technical merit, we close with discussion of the measures taken to build stakeholder trust in HOPS.
E-Commerce in Turkey and SAP Integrated E-Commerce System
Venue: arXiv cs.CY 2021
Year: 2021

E-commerce is a kind of shopping by use of the internet. E-commerce, very different from the usual shopping concept, is compatible with today's economic dynamics. E-commerce is becoming an indispensable method with the increase of internet usage. With the use of E-commerce, there are also a number of advantages for companies. On the other hand, SAP is a pioneer and leader in the company resource planning software sector. SAP is very important for large-scale companies. They manage all their processes on SAP and its integration is very important with other related software. In this article, we give brief information on some important aspects of e-commerce and propose a solution for ERP integration of an e-commerce system.
How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic Study Using Neural Metaphor Detection
Venue: arXiv cs.CL 2021
Year: 2021

Metaphors are widely used in political rhetoric as an effective framing device. While the efficacy of specific metaphors such as the war metaphor in political discourse has been documented before, those studies often rely on small number of hand-coded instances of metaphor use. Larger-scale topic-agnostic studies are required to establish the general persuasiveness of metaphors as a device, and to shed light on the broader patterns that guide their persuasiveness. In this paper, we present a large-scale data-driven study of metaphors used in political discourse. We conduct this study on a publicly available dataset of over 85K posts made by 412 US politicians in their Facebook public pages, up until Feb 2017. Our contributions are threefold: we show evidence that metaphor use correlates with ideological leanings in complex ways that depend on concurrent political events such as winning or losing elections; we show that posts with metaphors elicit more engagement from their audience overall even after controlling for various socio-political factors such as gender and political party affiliation; and finally, we demonstrate that metaphoricity is indeed the reason for increased engagement of posts, through a fine-grained linguistic analysis of metaphorical vs. literal usages of 513 words across 70K posts.
A Conceptual Framework for Implicit Evaluation of Conversational Search Interfaces
Venue: arXiv cs.HC 2021
Year: 2021

Conversational search (CS) has recently become a significant focus of the information retrieval (IR) research community. Multiple studies have been conducted which explore the concept of conversational search. Understanding and advancing research in CS requires careful and detailed evaluation. Existing CS studies have been limited to evaluation based on simple user feedback on task completion. We propose a CS evaluation framework which includes multiple dimensions: search experience, knowledge gain, software usability, cognitive load and user experience, based on studies of conversational systems and IR. We introduce these evaluation criteria and propose their use in a framework for the evaluation of CS systems.
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future
Venue: arXiv cs.AI 2021
Year: 2021

Climate change poses a major challenge to humanity, especially in its impact on agriculture, a challenge that a responsible AI should meet. In this paper, we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by supporting grassland management, through accurate crop growth prediction. As climate changes, PBI-CBRs historical cases become less useful in predicting future grass growth. Hence, we extend PBI-CBR using data augmentation, to specifically handle disruptive climate events, using a counterfactual method (from XAI). Study 1 shows that historical, extreme climate-events (climate outlier cases) tend to be used by PBI-CBR to predict grass growth during climate disrupted periods. Study 2 shows that synthetic outliers, generated as counterfactuals on a outlier-boundary, improve the predictive accuracy of PBICBR, during the drought of 2018. This study also shows that an instance-based counterfactual method does better than a benchmark, constraint-guided method.
Re-designing cities with conditional adversarial networks
Venue: arXiv cs.CV 2021
Year: 2021

This paper introduces a conditional generative adversarial network to redesign a street-level image of urban scenes by generating 1) an urban intervention policy, 2) an attention map that localises where intervention is needed, 3) a high-resolution street-level image (1024 X 1024 or 1536 X1536) after implementing the intervention. We also introduce a new dataset that comprises aligned street-level images of before and after urban interventions from real-life scenarios that make this research possible. The introduced method has been trained on different ranges of urban interventions applied to realistic images. The trained model shows strong performance in re-modelling cities, outperforming existing methods that apply image-to-image translation in other domains that is computed in a single GPU. This research opens the door for machine intelligence to play a role in re-thinking and re-designing the different attributes of cities based on adversarial learning, going beyond the mainstream of facial landmarks manipulation or image synthesis from semantic segmentation.
Network in Disaggregated Datacenters
Venue: arXiv cs.DC 2021
Year: 2021

Nowadays, datacenters lean on a computer-centric approach based on monolithic servers which include all necessary hardware resources (mainly CPU, RAM, network and disks) to run applications. Such an architecture comes with two main limitations: (1) difficulty to achieve full resource utilization and (2) coarse granularity for hardware maintenance. Recently, many works investigated a resource-centric approach called disaggregated architecture where the datacenter is composed of self-content resource boards interconnected using fast interconnection technologies, each resource board including instances of one resource type. The resource-centric architecture allows each resource to be managed (maintenance, allocation) independently. LegoOS is the first work which studied the implications of disaggregation on the operating system, proposing to disaggregate the operating system itself. They demonstrated the suitability of this approach, considering mainly CPU and RAM resources. However, they didn't study the implication of disaggregation on network resources. We reproduced a LegoOS infrastructure and extended it to support disaggregated networking. We show that networking can be disaggregated following the same principles, and that classical networking optimizations such as DMA, DDIO or loopback can be reproduced in such an environment. Our evaluations show the viability of the approach and the potential of future disaggregated infrastructures.
Co-Creating Educational Project Management Board Games to Enhance Student Engagement
Venue: arXiv cs.CY 2021
Year: 2021

Management education scholarship has long outlined the need to enhance student engagement and participation in business schools, using more innovative teaching practices. This is increasingly motivating scholars to strive for more collaborative pedagogic dynamics between teachers and students. At the same time, research into co-creation of Game Based Learning material such as board games has largely focused on the value added to games when educators involve students in the design process. However there has been scant research examining the qualities of co-creational game design exercises as teaching experiences themselves, thus overlooking the opportunity to conceptualise such activities as an innovative teaching tool that can help educators facilitate student engagement and participation. To address this research gap, this paper presents a case study where Project Management students participated in two co-creation workshops designing educational Project Management games. Data were collected conducting focus groups at the end of the two workshops. Throughout the paper we have sought to present some positive outcomes of such processes as well as some critical points that emerged through the data that were collected. Mentionable outcomes include a series of positive characteristics of co-creative Game Based Learning activities like enhanced engagement as well as a list of challenges when facilitating such activities. The main findings of this research have been organised in two frameworks, one outlining five positive characteristics of co-creative Game Based Learning activities: engagement with knowledge, knowledge assessment, creativity, communication and the second outlining challenges in facilitating such activities: lack of focus, lack of structure and the need for more practice-oriented games.
Embedding Sustainability in Complex Projects: A Pedagogic Practice Simulation Approach
Venue: arXiv cs.CY 2021
Year: 2021

Sustainability is focussed on avoiding the long-term depletion of natural resources. Under the terms of a government plan to tackle climate change, a driver for improved sustainability is the cut of greenhouse gas emissions in the UK to almost zero by 2050. With this type of change, new themes are continuously being developed which drive complex projects, such as the development of new power generation methods, which encompass challenging lead times and demanding requirements. Consideration of the implementation of strategies and key concepts, which may engender sustainability within complex projects therefore presents an opportunity for further critical debate, review, and application through a project management lens. Sustainability incorporation in project management has been documented in academic literature, with this emerging field providing new challenges. For example, project management education can provide a holistic base for the inculcation of sustainability factors to a range of industries, including complex projects. Likewise, practitioner interest and approaches to sustainability in project management are being driven by the recently Chartered Association for Project Management (APM). Whilst this body makes a significant contribution to the UK economy across many sectors, it also addresses ongoing sustainability challenges. Therefore, by drawing on research and practitioner developments, the authors argue that by connecting with the next generation through practice simulation approaches, and embedding sustainability issues within project management tools and methods, improved focus on sustainability in complex project management may be achieved.
A survey on extremism analysis using Natural Language Processing
Venue: arXiv cs.CY 2021
Year: 2021

Extremism research has grown as an open problem for several countries during recent years, especially due to the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. Natural Language Processing (NLP) represents a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by this groups, with the final objective of detecting and preventing its spread. This survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a description and comparison of the frequently used NLP techniques, how they were applied, the insights they provided, the most frequently used NLP software tools and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested.
Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
Venue: arXiv cs.CV 2021
Year: 2021

We present a relocalization pipeline, which combines an absolute pose regression (APR) network with a novel view synthesis based direct matching module, offering superior accuracy while maintaining low inference time. Our contribution is twofold: i) we design a direct matching module that supplies a photometric supervision signal to refine the pose regression network via differentiable rendering; ii) we modify the rotation representation from the classical quaternion to SO(3) in pose regression, removing the need for balancing rotation and translation loss terms. As a result, our network Direct-PoseNet achieves state-of-the-art performance among all other single-image APR methods on the 7-Scenes benchmark and the LLFF dataset.
Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks
Venue: arXiv cs.LG 2021
Year: 2021

Reinforcement learning (RL) is always the preferred embodiment to construct the control strategy of complex tasks, like asymmetric assembly tasks. However, the convergence speed of reinforcement learning severely restricts its practical application. In this paper, the convergence is first accelerated by combining RL and compliance control. Then a completely innovative progressive extension of action dimension (PEAD) mechanism is proposed to optimize the convergence of RL algorithms. The PEAD method is verified in DDPG and PPO. The results demonstrate the PEAD method will enhance the data-efficiency and time-efficiency of RL algorithms as well as increase the stable reward, which provides more potential for the application of RL.