7,745 research outputs found
Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning
This paper presents a solution to the challenge of mitigating carbon
emissions from large-scale high performance computing (HPC) systems and
datacenters that host machine learning (ML) inference services. ML inference is
critical to modern technology products, but it is also a significant
contributor to datacenter compute cycles and carbon emissions. We introduce
Clover, a carbon-friendly ML inference service runtime system that balances
performance, accuracy, and carbon emissions through mixed-quality models and
GPU resource partitioning. Our experimental results demonstrate that Clover is
effective in substantially reducing carbon emissions while maintaining high
accuracy and meeting service level agreement (SLA) targets. Therefore, it is a
promising solution toward achieving carbon neutrality in HPC systems and
datacenters
FairGen: Towards Fair Graph Generation
There have been tremendous efforts over the past decades dedicated to the
generation of realistic graphs in a variety of domains, ranging from social
networks to computer networks, from gene regulatory networks to online
transaction networks. Despite the remarkable success, the vast majority of
these works are unsupervised in nature and are typically trained to minimize
the expected graph reconstruction loss, which would result in the
representation disparity issue in the generated graphs, i.e., the protected
groups (often minorities) contribute less to the objective and thus suffer from
systematically higher errors. In this paper, we aim to tailor graph generation
to downstream mining tasks by leveraging label information and user-preferred
parity constraint. In particular, we start from the investigation of
representation disparity in the context of graph generative models. To mitigate
the disparity, we propose a fairness-aware graph generative model named
FairGen. Our model jointly trains a label-informed graph generation module and
a fair representation learning module by progressively learning the behaviors
of the protected and unprotected groups, from the `easy' concepts to the `hard'
ones. In addition, we propose a generic context sampling strategy for graph
generative models, which is proven to be capable of fairly capturing the
contextual information of each group with a high probability. Experimental
results on seven real-world data sets, including web-based graphs, demonstrate
that FairGen (1) obtains performance on par with state-of-the-art graph
generative models across six network properties, (2) mitigates the
representation disparity issues in the generated graphs, and (3) substantially
boosts the model performance by up to 17% in downstream tasks via data
augmentation
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
Architecture Smells vs. Concurrency Bugs: an Exploratory Study and Negative Results
Technical debt occurs in many different forms across software artifacts. One
such form is connected to software architectures where debt emerges in the form
of structural anti-patterns across architecture elements, namely, architecture
smells. As defined in the literature, ``Architecture smells are recurrent
architectural decisions that negatively impact internal system quality", thus
increasing technical debt. In this paper, we aim at exploring whether there
exist manifestations of architectural technical debt beyond decreased code or
architectural quality, namely, whether there is a relation between architecture
smells (which primarily reflect structural characteristics) and the occurrence
of concurrency bugs (which primarily manifest at runtime). We study 125
releases of 5 large data-intensive software systems to reveal that (1) several
architecture smells may in fact indicate the presence of concurrency problems
likely to manifest at runtime but (2) smells are not correlated with
concurrency in general -- rather, for specific concurrency bugs they must be
combined with an accompanying articulation of specific project characteristics
such as project distribution. As an example, a cyclic dependency could be
present in the code, but the specific execution-flow could be never executed at
runtime
GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions
The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform
high-precision measurements of heavy-hadron decays, which requires the
collection of large data samples and a good understanding and suppression of
multiple background sources. Both factors are challenged by a five-fold
increase in the average number of proton-proton collisions per bunch crossing,
corresponding to a change in the detector operation conditions for the LHCb
Upgrade I phase, recently started. A further ten-fold increase is expected in
the Upgrade II phase, planed for the next decade. The limits in the storage
capacity of the trigger will bring an inverse relation between the amount of
particles selected to be stored per event and the number of events that can be
recorded, and the background levels will raise due to the enlarged
combinatorics. To tackle both challenges, we propose a novel approach, never
attempted before in a hadronic collider: a Deep-learning based Full Event
Interpretation (DFEI), to perform the simultaneous identification, isolation
and hierarchical reconstruction of all the heavy-hadron decay chains per event.
This approach radically contrasts with the standard selection procedure used in
LHCb to identify heavy-hadron decays, that looks individually at sub-sets of
particles compatible with being products of specific decay types, disregarding
the contextual information from the rest of the event. We present the first
prototype for the DFEI algorithm, that leverages the power of Graph Neural
Networks (GNN). This paper describes the design and development of the
algorithm, and its performance in Upgrade I simulated conditions
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
Aerial Network Assistance Systems for Post-Disaster Scenarios : Topology Monitoring and Communication Support in Infrastructure-Independent Networks
Communication anytime and anywhere is necessary for our modern society to function. However, the critical network infrastructure quickly fails in the face of a disaster and leaves the affected population without means of communication. This lack can be overcome by smartphone-based emergency communication systems, based on infrastructure-independent networks like Delay-Tolerant Networks (DTNs). DTNs, however, suffer from short device-to-device link distances and, thus, require multi-hop routing or data ferries between disjunct parts of the network. In disaster scenarios, this fragmentation is particularly severe because of the highly clustered human mobility behavior. Nevertheless, aerial communication support systems can connect local network clusters by utilizing Unmanned Aerial Vehicles (UAVs) as data ferries. To facilitate situation-aware and adaptive communication support, knowledge of the network topology, the identification of missing communication links, and the constant reassessment of dynamic disasters are required. These requirements are usually neglected, despite existing approaches to aerial monitoring systems capable of detecting devices and networks.
In this dissertation, we, therefore, facilitate the coexistence of aerial topology monitoring and communications support mechanisms in an autonomous Aerial Network Assistance System for infrastructure-independent networks as our first contribution. To enable system adaptations to unknown and dynamic disaster situations, our second contribution addresses the collection, processing, and utilization of topology information. For one thing, we introduce cooperative monitoring approaches to include the DTN in the monitoring process. Furthermore, we apply novel approaches for data aggregation and network cluster estimation to facilitate the continuous assessment of topology information and an appropriate system adaptation. Based on this, we introduce an adaptive topology-aware routing approach to reroute UAVs and increase the coverage of disconnected nodes outside clusters.
We generalize our contributions by integrating them into a simulation framework, creating an evaluation platform for autonomous aerial systems as our third contribution. We further increase the expressiveness of our aerial system evaluation, by adding movement models for multicopter aircraft combined with power consumption models based on real-world measurements. Additionally, we improve the disaster simulation by generalizing civilian disaster mobility based on a real-world field test. With a prototypical system implementation, we extensively evaluate our contributions and show the significant benefits of cooperative monitoring and topology-aware routing, respectively. We highlight the importance of continuous and integrated topology monitoring for aerial communications support and demonstrate its necessity for an adaptive and long-term disaster deployment. In conclusion, the contributions of this dissertation enable the usage of autonomous Aerial Network Assistance Systems and their adaptability in dynamic disaster scenarios
Automatic Question Generation to Support Reading Comprehension of Learners - Content Selection, Neural Question Generation, and Educational Evaluation
Simply reading texts passively without actively engaging with their content is suboptimal for text comprehension since learners may miss crucial concepts or misunderstand essential ideas.
In contrast, engaging learners actively by asking questions fosters text comprehension.
However, educational resources frequently lack questions.
Textbooks often contain only a few at the end of a chapter, and informal learning resources such as Wikipedia lack them entirely.
Thus, in this thesis, we study to what extent questions about educational science texts can be automatically generated, tackling two research questions.
The first question concerns selecting learning-relevant passages to guide the generation process.
The second question investigates the generated questions' potential effects and applicability in reading comprehension scenarios.
Our first contribution improves the understanding of neural question generation's quality in education.
We find that the generators' high linguistic quality transfers to educational texts but that they require guidance by educational content selection.
In consequence, we study multiple educational context and answer selection mechanisms.
In our second contribution, we propose novel context selection approaches which target question-worthy sentences in texts.
In contrast to previous works, our context selectors are guided by educational theory.
The proposed methods perform competitive to related work while operating with educationally motivated decision criteria that are easier to understand for educational experts.
The third contribution addresses answer selection methods to guide neural question generation with expected answers.
Our experiments highlight the need for educational corpora for the task. Models trained on noneducational corpora do not transfer well to the educational domain.
Given this discrepancy, we propose a novel corpus construction approach.
It automatically derives educational answer selection corpora from textbooks.
We verify the approach's usefulness by showing that neural models trained on the constructed corpora learn to detect learning-relevant concepts.
In our last contribution, we use the insights from the previous experiments to design, implement, and evaluate an automatic question generator for educational use.
We evaluate the proposed generator intrinsically with an expert annotation study and extrinsically with an empirical reading comprehension study.
The two evaluation scenarios provide a nuanced view of the generated questions' strengths and weaknesses.
Expert annotations attribute an educational value to roughly 60 % of the questions but also reveal various ways in which the questions still fall short of the quality experts desire.
Furthermore, the reader-based evaluation indicates that the proposed educational question generator increases learning outcomes compared to a no-question control group.
In summary, the results of the thesis improve the understanding of the content selection tasks in educational question generation and provide evidence that it can improve reading comprehension.
As such, the proposed approaches are promising tools for authors and learners to promote active reading and thus foster text comprehension
On the Principles of Evaluation for Natural Language Generation
Natural language processing is concerned with the ability of computers to understand natural language texts, which is, arguably, one of the major bottlenecks in the course of chasing the holy grail of general Artificial Intelligence. Given the unprecedented success of deep learning technology, the natural language processing community has been almost entirely in favor of practical applications with state-of-the-art systems emerging and competing for human-parity performance at an ever-increasing pace. For that reason, fair and adequate evaluation and comparison, responsible for ensuring trustworthy, reproducible and unbiased results, have fascinated the scientific community for long, not only in natural language but also in other fields. A popular example is the ISO-9126 evaluation standard for software products, which outlines a wide range of evaluation concerns, such as cost, reliability, scalability, security, and so forth. The European project EAGLES-1996, being the acclaimed extension to ISO-9126, depicted the fundamental principles specifically for evaluating natural language technologies, which underpins succeeding methodologies in the evaluation of natural language.
Natural language processing encompasses an enormous range of applications, each with its own evaluation concerns, criteria and measures. This thesis cannot hope to be comprehensive but particularly addresses the evaluation in natural language generation (NLG), which touches on, arguably, one of the most human-like natural language applications. In this context, research on quantifying day-to-day progress with evaluation metrics lays the foundation of the fast-growing NLG community. However, previous works have failed to address high-quality metrics in multiple scenarios such as evaluating long texts and when human references are not available, and, more prominently, these studies are limited in scope, given the lack of a holistic view sketched for principled NLG evaluation.
In this thesis, we aim for a holistic view of NLG evaluation from three complementary perspectives, driven by the evaluation principles in EAGLES-1996: (i) high-quality evaluation metrics, (ii) rigorous comparison of NLG systems for properly tracking the progress, and (iii) understanding evaluation metrics. To this end, we identify the current state of challenges derived from the inherent characteristics of these perspectives, and then present novel metrics, rigorous comparison approaches, and explainability techniques for metrics to address the identified issues.
We hope that our work on evaluation metrics, system comparison and explainability for metrics inspires more research towards principled NLG evaluation, and contributes to the fair and adequate evaluation and comparison in natural language processing
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