1,804 research outputs found
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Guided rewriting and constraint satisfaction for parallel GPU code generation
Graphics Processing Units (GPUs) are notoriously hard to optimise for manually due to their scheduling and memory hierarchies. What is needed are good automatic code generators and optimisers for such parallel hardware. Functional approaches such as Accelerate, Futhark and LIFT leverage a high-level algorithmic Intermediate Representation (IR) to expose parallelism and abstract the implementation details away from the user. However, producing efficient code for a given accelerator remains challenging. Existing code generators depend on the user input to choose a subset of hard-coded optimizations or automated exploration of implementation search space. The former suffers from the lack of extensibility, while the latter is too costly due to the size of the search space. A hybrid approach is needed, where a space of valid implementations is built automatically and explored with the aid of human expertise.
This thesis presents a solution combining user-guided rewriting and automatically generated constraints to produce high-performance code. The first contribution is an automatic tuning technique to find a balance between performance and memory consumption. Leveraging its functional patterns, the LIFT compiler is empowered to infer tuning constraints and limit the search to valid tuning combinations only.
Next, the thesis reframes parallelisation as a constraint satisfaction problem. Parallelisation constraints are extracted automatically from the input expression, and a solver is used to identify valid rewriting. The constraints truncate the search space to valid parallel mappings only by capturing the scheduling restrictions of the GPU in the context of a given program. A synchronisation barrier insertion technique is proposed to prevent data races and improve the efficiency of the generated parallel mappings.
The final contribution of this thesis is the guided rewriting method, where the user encodes a design space of structural transformations using high-level IR nodes called rewrite points. These strongly typed pragmas express macro rewrites and expose design choices as explorable parameters. The thesis proposes a small set of reusable rewrite points to achieve tiling, cache locality, data reuse and memory optimisation.
A comparison with the vendor-provided handwritten kernel ARM Compute Library and the TVM code generator demonstrates the effectiveness of this thesis' contributions. With convolution as a use case, LIFT-generated direct and GEMM-based convolution implementations are shown to perform on par with the state-of-the-art solutions on a mobile GPU. Overall, this thesis demonstrates that a functional IR yields well to user-guided and automatic rewriting for high-performance code generation
Towards Semantically Enriched Embeddings for Knowledge Graph Completion
Embedding based Knowledge Graph (KG) Completion has gained much attention
over the past few years. Most of the current algorithms consider a KG as a
multidirectional labeled graph and lack the ability to capture the semantics
underlying the schematic information. In a separate development, a vast amount
of information has been captured within the Large Language Models (LLMs) which
has revolutionized the field of Artificial Intelligence. KGs could benefit from
these LLMs and vice versa. This vision paper discusses the existing algorithms
for KG completion based on the variations for generating KG embeddings. It
starts with discussing various KG completion algorithms such as transductive
and inductive link prediction and entity type prediction algorithms. It then
moves on to the algorithms utilizing type information within the KGs, LLMs, and
finally to algorithms capturing the semantics represented in different
description logic axioms. We conclude the paper with a critical reflection on
the current state of work in the community and give recommendations for future
directions
Exploiting Process Algebras and BPM Techniques for Guaranteeing Success of Distributed Activities
The communications and collaborations among activities, pro-
cesses, or systems, in general, are the base of complex sys-
tems defined as distributed systems. Given the increasing
complexity of their structure, interactions, and functionali-
ties, many research areas are interested in providing mod-
elling techniques and verification capabilities to guarantee
their correctness and satisfaction of properties. In particular,
the formal methods community provides robust verification
techniques to prove system properties. However, most ap-
proaches rely on manually designed formal models, making
the analysis process challenging because it requires an expert
in the field. On the other hand, the BPM community pro-
vides a widely used graphical notation (i.e., BPMN) to design
internal behaviour and interactions of complex distributed
systems that can be enhanced with additional features (e.g.,
privacy technologies). Furthermore, BPM uses process min-
ing techniques to automatically discover these models from
events observation. However, verifying properties and ex-
pected behaviour, especially in collaborations, still needs a
solid methodology.
This thesis aims at exploiting the features of the formal meth-
ods and BPM communities to provide approaches that en-
able formal verification over distributed systems. In this con-
text, we propose two approaches. The modelling-based ap-
proach starts from BPMN models and produces process al-
gebra specifications to enable formal verification of system
properties, including privacy-related ones. The process mining-
based approach starts from logs observations to automati-
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cally generate process algebra specifications to enable veri-
fication capabilities
Declarative Specification of Intraprocedural Control-flow and Dataflow Analysis
Static program analysis plays a crucial role in ensuring the quality and security of software applications by detecting and fixing bugs, and potential security vulnerabilities in the code. The use of declarative paradigms in dataflow analysis as part of static program analysis has become increasingly popular in recent years. This is due to its enhanced expressivity and modularity, allowing for a higher-level programming approach, resulting in easy and efficient development.The aim of this thesis is to explore the design and implementation of control-flow and dataflow analyses using the declarative Reference Attribute Grammars formalism. Specifically, we focus on the construction of analyses directly on the source code rather than on an intermediate representation.The main result of this thesis is our language-agnostic framework, called IntraCFG. IntraCFG enables efficient and effective dataflow analysis by allowing the construction of precise and source-level control-flow graphs. The framework superimposes control-flow graphs on top of the abstract syntax tree of the program. The effectiveness of IntraCFG is demonstrated through two case studies, IntraJ and IntraTeal. These case studies showcase the potential and flexibility of IntraCFG in diverse contexts, such as bug detection and education. IntraJ supports the Java programming language, while IntraTeal is a tool designed for teaching program analysis for an educational language, Teal.IntraJ has proven to be faster than and as precise as well-known industrial tools. The combination of precision, performance, and on-demand evaluation in IntraJ leads to low latency in querying the analysis results. This makes IntraJ a suitable tool for use in interactive tools. Preliminary experiments have also been conducted to demonstrate how IntraJ can be used to support interactive bug detection and fixing.Additionally, this thesis presents JFeature, a tool for automatically extracting and summarising the features of a Java corpus, including the use of different Java features (e.g., use of Lambda Expressions) across different Java versions. JFeature provides researchers and developers with a deeper understanding of the characteristics of corpora, enabling them to identify suitable benchmarks for the evaluation of their tools and methodologies
Evaluating Architectural Safeguards for Uncertain AI Black-Box Components
Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability
Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.
Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward
No abstract available
Applying machine learning: a multi-role perspective
Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference
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