A recently proposed approach to the rigorous engineering of collective adaptive systems is the aggregate computing paradigm, which operationalises the idea of expressing collective adaptive behaviour by a global perspective as a functional composition of dynamic computational fields (i.e., structures mapping a collection of individual devices of a collective to computational values over time). In this paper, we present FScaFi, a core language that captures the essence of exploiting field computations in mainstream functional languages, and which is based on a semantic model for field computations leveraging the novel notion of “computation against a neighbour”. Such a construct models expressions whose evaluation depends on the same evaluation that occurred on a neighbour, thus abstracting communication actions and, crucially, enabling deep and straightforward integration in the Scala programming language, by the ScaFi incarnation. We cover syntax and informal semantics of FScaFi, provide examples of collective adaptive behaviour development in ScaFi, and delineate future work.
I did this work for Fields Institute Machine Learning Graduate Course. It covers the basics of adversarial domain adaptation and the mathematical formulation behind it like the use of domain divergence and how to implement it using a neural network. It also covers the subsequent development of GANs from the idea of adversarial learning including descriptions of CoGAN and CyCADA.
Paper presentation for the final course Advanced Concept in Machine Learning.
The paper is @Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data"
http://jmlr.org/proceedings/papers/v32/chenf14.pdf
Detecting paraphrases using recursive autoencodersFeynman Liang
Presentation on deep learning applied to natural language processing, presented at University of Cambridge Machine Learning Group's Research and Communication Club 2-11-2015 meeting.
I did this work for Fields Institute Machine Learning Graduate Course. It covers the basics of adversarial domain adaptation and the mathematical formulation behind it like the use of domain divergence and how to implement it using a neural network. It also covers the subsequent development of GANs from the idea of adversarial learning including descriptions of CoGAN and CyCADA.
Paper presentation for the final course Advanced Concept in Machine Learning.
The paper is @Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data"
http://jmlr.org/proceedings/papers/v32/chenf14.pdf
Detecting paraphrases using recursive autoencodersFeynman Liang
Presentation on deep learning applied to natural language processing, presented at University of Cambridge Machine Learning Group's Research and Communication Club 2-11-2015 meeting.
This is an introduction of Topic Modeling, including tf-idf, LSA, pLSA, LDA, EM, and some other related materials. I know there are definitely some mistakes, and you can correct them with your wisdom. Thank you~
A Simple Introduction to Neural Information RetrievalBhaskar Mitra
Neural Information Retrieval (or neural IR) is the application of shallow or deep neural networks to IR tasks. In this lecture, we will cover some of the fundamentals of neural representation learning for text retrieval. We will also discuss some of the recent advances in the applications of deep neural architectures to retrieval tasks.
(These slides were presented at a lecture as part of the Information Retrieval and Data Mining course taught at UCL.)
Deep neural methods have recently demonstrated significant performance improvements in several IR tasks. In this lecture, we will present a brief overview of deep models for ranking and retrieval.
This is a follow-up lecture to "Neural Learning to Rank" (https://www.slideshare.net/BhaskarMitra3/neural-learning-to-rank-231759858)
Neural Models for Information RetrievalBhaskar Mitra
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models will also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
We begin this talk with a discussion on text embedding spaces for modelling different types of relationships between items which makes them suitable for different IR tasks. Next, we present how topic-specific representations can be more effective than learning global embeddings. Finally, we conclude with an emphasis on dealing with rare terms and concepts for IR, and how embedding based approaches can be augmented with neural models for lexical matching for better retrieval performance. While our discussions are grounded in IR tasks, the findings and the insights covered during this talk should be generally applicable to other NLP and machine learning tasks.
Neural Models for Information RetrievalBhaskar Mitra
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models may also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
In this talk, I will present my recent work on neural IR models. We begin with a discussion on learning good representations of text for retrieval. I will present visual intuitions about how different embeddings spaces capture different relationships between items, and their usefulness to different types of IR tasks. The second part of this talk is focused on the applications of deep neural architectures to the document ranking task.
Neural Information Retrieval: In search of meaningful progressBhaskar Mitra
The emergence of deep learning based methods for search poses several challenges and opportunities not just for modeling, but also for benchmarking and measuring progress in the field. Some of these challenges are new, while others have evolved from existing challenges in IR benchmarking exacerbated by the scale at which deep learning models operate. Evaluation efforts such as the TREC Deep Learning track and the MS MARCO public leaderboard are intended to encourage research and track our progress, addressing big questions in our field. The goal is not simply to identify which run is "best" but to move the field forward by developing new robust techniques, that work in many different settings, and are adopted in research and practice. This entails a wider conversation in the IR community about what constitutes meaningful progress, how benchmark design can encourage or discourage certain outcomes, and about the validity of our findings. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track--and reflect on the state of the field and the road ahead.
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackBhaskar Mitra
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the “Duet principle”), (ii) query term independence (i.e., the “QTI assumption”) to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.
Towards Automated Engineering for Collective Adaptive Systems: Vision and Res...Roberto Casadei
The opportunities and challenges of recent and
forthcoming distributed computing scenarios have been promot-
ing research on languages and paradigms aimed at modelling the
macro/collective behaviour of systems as well as mechanisms to
endow them with self-* capabilities. One example is the aggregate
computing paradigm, which supports the development of self-
organising systems (e.g., robot swarms, computational ecosys-
tems, and crowd-based services) through various formalisms and
tools developed over a decade. However, very limited work has
been done by a methodological and automation perspective. In
this paper, we explore the issue of organising the development
process of aggregate computing systems. Accordingly, we outline
novel research directions that arise from careful analysis of
the peculiar issues in collective and self-organising systems, the
cornerstones of effective software engineering practices, and
recent scientific trends and insights.
Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-P...Roberto Casadei
The engineering of large-scale cyber-physical systems (CPS) increasingly relies on principles from self-organisation and collective computing, enabling these systems to cooperate and adapt in dynamic environments. CPS engineering also often leverages digital twins that provide synchronised logical counterparts of physical entities. In contrast, sensor networks rely on the different but related concept of virtual device that provides an abstraction of a group of sensors. In this work, we study how such concepts can contribute to the engineering of self-organising CPSs. To that end, we analyse the concepts and devise modelling constructs, distinguishing between identity correspondence and execution relationships. Based on this analysis, we then contribute to the novel concept of “collective digital twin” (CDT) that captures the logical counterpart of a collection of physical devices. A CDT can also be “augmented” with purely virtual devices, which may be exploited to steer the self-organisation process of the CDT and its physical counterpart. We underpin the novel concept with experiments in the context of the pulverisation framework of aggregate computing, showing how augmented CDTs provide a holistic, modular, and cyber-physically integrated system view that can foster the engineering of self-organising CPSs.
This is an introduction of Topic Modeling, including tf-idf, LSA, pLSA, LDA, EM, and some other related materials. I know there are definitely some mistakes, and you can correct them with your wisdom. Thank you~
A Simple Introduction to Neural Information RetrievalBhaskar Mitra
Neural Information Retrieval (or neural IR) is the application of shallow or deep neural networks to IR tasks. In this lecture, we will cover some of the fundamentals of neural representation learning for text retrieval. We will also discuss some of the recent advances in the applications of deep neural architectures to retrieval tasks.
(These slides were presented at a lecture as part of the Information Retrieval and Data Mining course taught at UCL.)
Deep neural methods have recently demonstrated significant performance improvements in several IR tasks. In this lecture, we will present a brief overview of deep models for ranking and retrieval.
This is a follow-up lecture to "Neural Learning to Rank" (https://www.slideshare.net/BhaskarMitra3/neural-learning-to-rank-231759858)
Neural Models for Information RetrievalBhaskar Mitra
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models will also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
We begin this talk with a discussion on text embedding spaces for modelling different types of relationships between items which makes them suitable for different IR tasks. Next, we present how topic-specific representations can be more effective than learning global embeddings. Finally, we conclude with an emphasis on dealing with rare terms and concepts for IR, and how embedding based approaches can be augmented with neural models for lexical matching for better retrieval performance. While our discussions are grounded in IR tasks, the findings and the insights covered during this talk should be generally applicable to other NLP and machine learning tasks.
Neural Models for Information RetrievalBhaskar Mitra
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models may also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
In this talk, I will present my recent work on neural IR models. We begin with a discussion on learning good representations of text for retrieval. I will present visual intuitions about how different embeddings spaces capture different relationships between items, and their usefulness to different types of IR tasks. The second part of this talk is focused on the applications of deep neural architectures to the document ranking task.
Neural Information Retrieval: In search of meaningful progressBhaskar Mitra
The emergence of deep learning based methods for search poses several challenges and opportunities not just for modeling, but also for benchmarking and measuring progress in the field. Some of these challenges are new, while others have evolved from existing challenges in IR benchmarking exacerbated by the scale at which deep learning models operate. Evaluation efforts such as the TREC Deep Learning track and the MS MARCO public leaderboard are intended to encourage research and track our progress, addressing big questions in our field. The goal is not simply to identify which run is "best" but to move the field forward by developing new robust techniques, that work in many different settings, and are adopted in research and practice. This entails a wider conversation in the IR community about what constitutes meaningful progress, how benchmark design can encourage or discourage certain outcomes, and about the validity of our findings. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track--and reflect on the state of the field and the road ahead.
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackBhaskar Mitra
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the “Duet principle”), (ii) query term independence (i.e., the “QTI assumption”) to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.
Towards Automated Engineering for Collective Adaptive Systems: Vision and Res...Roberto Casadei
The opportunities and challenges of recent and
forthcoming distributed computing scenarios have been promot-
ing research on languages and paradigms aimed at modelling the
macro/collective behaviour of systems as well as mechanisms to
endow them with self-* capabilities. One example is the aggregate
computing paradigm, which supports the development of self-
organising systems (e.g., robot swarms, computational ecosys-
tems, and crowd-based services) through various formalisms and
tools developed over a decade. However, very limited work has
been done by a methodological and automation perspective. In
this paper, we explore the issue of organising the development
process of aggregate computing systems. Accordingly, we outline
novel research directions that arise from careful analysis of
the peculiar issues in collective and self-organising systems, the
cornerstones of effective software engineering practices, and
recent scientific trends and insights.
Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-P...Roberto Casadei
The engineering of large-scale cyber-physical systems (CPS) increasingly relies on principles from self-organisation and collective computing, enabling these systems to cooperate and adapt in dynamic environments. CPS engineering also often leverages digital twins that provide synchronised logical counterparts of physical entities. In contrast, sensor networks rely on the different but related concept of virtual device that provides an abstraction of a group of sensors. In this work, we study how such concepts can contribute to the engineering of self-organising CPSs. To that end, we analyse the concepts and devise modelling constructs, distinguishing between identity correspondence and execution relationships. Based on this analysis, we then contribute to the novel concept of “collective digital twin” (CDT) that captures the logical counterpart of a collection of physical devices. A CDT can also be “augmented” with purely virtual devices, which may be exploited to steer the self-organisation process of the CDT and its physical counterpart. We underpin the novel concept with experiments in the context of the pulverisation framework of aggregate computing, showing how augmented CDTs provide a holistic, modular, and cyber-physically integrated system view that can foster the engineering of self-organising CPSs.
Self-Organisation Programming: a Functional Reactive Macro Approach (FRASP) [...Roberto Casadei
Engineering self-organising systems – e.g., robot
swarms, collectives of wearables, or distributed infrastructures
– has been investigated and addressed through various kinds
of approaches: devising algorithms by taking inspiration from
nature, relying on design patterns, using learning to synthesise
behaviour from expectations of emergent behaviour, and exposing
key mechanisms and abstractions at the level of a programming
language. Focussing on the latter approach, most of the state-
of-the-art languages for self-organisation leverage a round-based
execution model, where devices repeatedly evaluate their context
and control program fully: this model is simple to reason about
but limited in terms of flexibility and fine-grained management
of sub-activities. By inspiration from the so-called functional
reactive paradigm, in this paper we propose a reactive self-
organisation programming approach that enables to fully decouple
the program logic from the scheduling of its sub-activities.
Specifically, we implement the idea through a functional reactive
implementation of aggregate programming in Scala, based on
the functional reactive library Sodium. The result is a functional
reactive self-organisation programming model, called FRASP,
that maintains the same expressiveness and benefits of aggregate
programming, while enabling significant improvements in terms
of scheduling controllability, flexibility in the sensing/actuation
model, and execution efficiency.
Programming Distributed Collective Processes for Dynamic Ensembles and Collec...Roberto Casadei
Recent trends like the Internet of Things (IoT) suggest a vi-
sion of dense and multi-scale deployments of computing devices in nearly
all kinds of environments. A prominent engineering challenge revolves
around programming the collective adaptive behaviour of such compu-
tational ecosystems. This requires abstractions able to capture concepts
like ensembles (dynamic groups of cooperating devices) and collective
tasks (joint activities carried out by ensembles). In this work, we con-
sider collections of devices interacting with neighbours and that execute
in nearly-synchronised sense–compute–interact rounds, where the com-
putation is given by a single control program. To support programming
whole computational collectives, we propose the abstraction of a dis-
tributed collective process (DCP), which can be used to define at once
the ensemble formation logic and its collective task. We implement the
abstraction in the eXchange Calculus (XC), a core language based on
neighbouring values (maps from neighbours to values) where state man-
agement and interaction is handled through a single primitive, exchange.
Then, we discuss the features of the abstraction, its suitability for differ-
ent kinds of distributed computing applications, and provide a proof-of-
concept implementation of a wave-like process propagation.
Augmented Collective Digital Twins for Self-Organising Cyber-Physical SystemsRoberto Casadei
Context. Self-organising and collective computing
approaches are increasingly applied to large-scale cyber-physical
systems (CPS), enabling them to adapt and cooperate in dynamic
environments. Also, in CPS engineering, digital twins are often
leveraged to provide synchronised logical counterparts of physical
entities, whereas in sensor networks the different-but-related
concept of virtual device is used e.g. to abstract groups of sensors.
Vision. We envision the design concept of “augmented collective
digital twin” that captures digital twins at a collective level
extended with purely virtual devices. We argue that this concept
can foster the engineering of self-organising CPS by providing a
holistic, declarative, and integrated system view.
Method. From a review and proposed taxonomy of logical
devices comprehending both digital twins and virtual devices,
we reinterpret a meta-model for self-organising CPSs and discuss
how it can support augmented collective digital twins. We illus-
trate the approach in a crowd-aware navigation scenario, where
virtual devices are opportunistically integrated into the system
to enhance spatial coverage, improving navigation capabilities.
Conclusion. By integrating physical and virtual devices, the
novel notion of augmented collective digital twin paves the way
to self-improving system functionality and intelligent use of
resources in self-organising CPSs.
Collective Adaptive Systems as Coordination Media: The Case of Tuples in Spac...Roberto Casadei
Coordination is a fundamental problem in the
engineering of collective adaptive systems (CAS). Prominent
approaches in this context promote adaptivity and collective
behaviour by founding coordination on local, decentralised in-
teraction. This is usually enabled through abstractions such as
collective interfaces, neighbour-based interaction, and attribute-
based communication. Application designers, then, use such
coordination mechanisms to enact collective adaptive behaviour
in order to solve specific problems or provide specific services
while coping with dynamic environments. In this paper, we
consider the other way round: we argue that a CAS model can
be used to provide support for high-level coordination models,
simplifying their implementation and transferring to them the
self-* properties it emergently fosters. As a motivating example,
we consider the idea of supporting tuple-based coordination by
Linda primitives such that tuples and operations have a position
and extension in space and time. Then, we adopt an aggregate
perspective, by which space-time is logically represented by a
mobile ad-hoc network of devices, and show that coordination
primitives can be implemented as true collective adaptive pro-
cesses. We describe this model and a prototype implementation
in the ScaFi aggregate programming framework, which is rooted
in the so-called computational field paradigm.
Aggregate computing is a research topic that is addressed by multiple perspectives: computational models, programming languages, distributed adaptive algorithms, middleware architectures, formal analysis, tools.
Coordinating Computation at the Edge: a Decentralized, Self-organizing, Spati...Roberto Casadei
Presentation of a paper accepted at the 4th Internetional Conference on Fog and Mobile Edge Computing (FMEC).
It discusses a decentralised, self-organising, spatial, collective approach to the development of edge-clouds/edge computing ecosystems.
Collective Abstractions and Platforms for Large-Scale Self-Adaptive IoTRoberto Casadei
On the way to the materialisation of the pervasive computing vision, the technological progress swelling from mobile computing and the Internet of Things (IoT) domain is already rich of missed opportunities. Firstly, coordinating large numbers of heterogeneous situated entities to achieve system-level goals in a resilient and self-adaptive way is complex and requires novel approaches to be seamlessly injected into mainstream distributed computing models. Secondly, achieving effective exploitation of computer resources is difficult, due to operational constraints resulting from current paradigms and uncomprehensive software infrastructures which hinder flexibility, adaptation, and smooth coordination of computational tasks execution. Indeed, building dynamic, context-oriented applications in small- or large-scale IoT with traditional abstractions is hard: even harder is to achieve opportunistic, QoS- and QoE-driven application task management across available hardware and networking infras- tructure. In this insight paper, we analyse by the collective adap- tation perspective the key directions of the impelling paradigm shift urged by forthcoming large-scale IoT scenarios. Specifically, we consider how collective abstractions and platforms can syner- gistically assist in such a transformation, by better capturing and enacting a notion of “collective service” as well as the dynamic, opportunistic, and context-driven traits of space-time-situated computations.
Tuple-Based Coordination in Large-Scale Situated SystemsRoberto Casadei
Space and time are key elements for many computer-based systems and often elevated to first-class abstractions. In tuple-based coordination, Linda primitives have been independently extended with space (with tuples and queries spanning spatial regions) or time information (mostly for tuple scoping). However, recent works in collective adaptive systems and aggregate computing show that space and time can naturally be considered as two intertwined facets of a common coordination abstraction for situated distributed systems. Accordingly, we introduce the Spatiotemporal Tuples model, a natural adaptation of Linda model for physically deployed large-scale networks. Unlike prior research, spatiotemporal properties – expressing where and when a tuple should range and has to be deposited/retrieved – naturally turn into specifications of collective adaptive processes, to be carried on in cooperation by the devices filling the computational environment, and sustaining tuple operations in a resilient way, possibly even in mobile and faulty environments. Additionally, the model promotes decentralised implementations where tuples actually reside where they are issued, which is good for supporting peer-to-peer and mobile ad-hoc networks as well as privacy. In this paper, we (i) present and formalise the Spatiotemporal Tuples model, based on the unifying notion of computational space-time structure, (ii) provide an implementation in the ScaFi aggregate computing framework, turning tuple operations into aggregate processes, and finally (iii) provide evaluation through simulation and a rescue case study.
On Context-Orientation in Aggregate ProgrammingRoberto Casadei
Context-awareness plays a central role in self-
adaptive software. By a programming perspective, context is
often used implicitly, and context-aware code is fragmented
in the codebase. In Context-Oriented Programming, instead,
context is considered a first-class citizen and is explicitly used
to modularise context-sensitive functionality and behavioural
variability. In this paper, we reflect on the role of context in
collective adaptive systems, by a discussion from the special
perspective of a macro paradigm, Aggregate Programming,
which supports the specification of collective behaviour by a
global perspective through functional compositions of field com-
putations. In particular, we consider the abstractions exposed in
Context-Oriented and Aggregate Programming, suggest potential
synergies in both directions, and accordingly take the first steps
towards a combined design.
Building data fusion surrogate models for spacecraft aerodynamic problems wit...Shinwoo Jang
Abstract. This work concerns a construction of surrogate models for a specific aerodynamic data base. This data base is generally available from wind tunnel testing or from CFD aerodynamic simulations and contains aerodynamic coefficients for different flight conditions and configurations (such as Mach number, angle-of-attack, vehicle configuration angle) encountered over different space vehicles mission. The main peculiarity of aerodynamic data base is a specific design of
experiment which is a union of grids of low and high fidelity data with considerably different sizes. Universal algorithms can’t approximate accurately such significantly non-uniform data. In this work a fast and accurate algorithm was developed which takes into account different fidelity of the data and special design of experiments.
A Programming Framework for Collective Adaptive EcosystemsRoberto Casadei
On the thrust of recent technological trends, we can
envision a future where dense ecosystems of digitally empowered devices
continuously adapt and operate in our environments to provide services
both to humans and other systems. To achieve that, we arguably need to
move beyond what an individual device can provide and rather focus on
what collectives of devices can offer as a system. Aggregate Computing
is a recent, promising framework generalising over spatial computing
approaches that supports the development of collective adaptive systems
by global specifications. It builds on the framework of the field
calculus to bridge the local and global perspectives, express
collective computations in a compositional way, and formally analyse
them to derive guarantees.
In this presentation, we describe the key concepts and results, take a
look at the practical support for Aggregate Computing on the JVM
provided by scafi, and consider the main research directions on the topic.
Novel scenarios like IoT and smart cities promote
a vision of computational ecosystems whereby heterogeneous
collectives of humans, devices and computing infrastructure
interact to provide various services. There, autonomous agents
with different capabilities are expected to cooperate towards
global goals in dependable ways. This is challenging, as deployments are within unknown, changing and loosely connected environments characterized by lack of centralized control, where
components may come and go, or disruption may be caused by
failures. Key issues include (i) how to leverage, functionally and
non-functionally, forms of opportunistic computing and locality
that often underlie IoT scenarios; (ii) how to design and operate
large-scale, resilient ecosystems through suitable assumptions,
decentralized control, and adaptive mechanisms; and (iii) how
to capture and enact “global” behaviors and properties, when
the system consists of heterogeneous, autonomous entities. In
this paper, we propose a model for resilient, collaborative edge-
enabled IoT that leverages spatial locality, opportunistic agents,
and coordinator nodes at the edge. The engineering approach
is declarative and configurable, and works by dynamically
dividing the environment into collaboration areas coordinated
by edge devices. We provide an implementation as a collective, self-organizing workflow based on Aggregate Computing,
provide evaluation by means of simulation, and finally discuss
properties and general applicability of the approach.
This presentation shows an overview of the main concepts introduced in the EDBT2015 Summer School, which took place in Palamos. For each area, we summarize the main issues and current approaches. We also describe the challenges and main activities that were undertaken in the summer school
Programming (and Learning) Self-Adaptive & Self-Organising Behaviour with Sca...Roberto Casadei
Large-scale and fully distributed cyber-physical sys-
tems (CPS), such as swarm robotics or IoT systems, pose
significant challenges for programming and design. These chal-
lenges include promoting the desired (emergent) collective and
self-organising behaviour, dealing with failures, enacting decen-
tralised coordination, and deploying efficient executions. Aggre-
gate computing is a promising approach that aims to simplify
the design of such systems by providing a high-level abstraction
for describing collective and self-organising behaviours. In this
tutorial, we introduce a toolchain that supports the development
of aggregate computing applications, based on ScaFi (a Scala-
based language and toolkit for aggregate computing) and Al-
chemist (a simulator for CPS scenarios). We will showcase the
toolchain by means of a series of examples, ranging from simple
collective behaviours to more complex self-adaptive and self-
organising ones. Finally, we provide several pointers to research
opportunities (e.g., related to learning collective behaviours
and adaptive large-scale deployments) and applications (e.g., in
swarm robotics, edge-cloud ecosystems, and more).
Introduction to the 1st DISCOLI workshop on distributed collective intelligenceRoberto Casadei
The 1st DISCOLI workshop on DIStributed COLlective Intelligence is co-located with the 42nd IEEE International Conference on Distributed Computing Systems (ICDCS 2022) that will take place in Bologna, Italy, 10-13 July 2022.
Recent technological and scientific trends are promoting a vision where intelligence is more and more distributed and collective. Indeed, as computing and communication technologies are becoming increasingly pervasive, and complexity of systems is growing in terms of scale, heterogeneity, and interaction, hence the focus tends to shift from the intelligence of individual devices or agents to the collective intelligence (CI) emerging from a dynamic collection of diverse devices. Such intelligence would allow systems to address complex problems through proper coordination (e.g., cooperation or competition), to self-organise to promote functionality under changing environments, and to improve decision-making capabilities.
The workshop aims to provide a forum where researchers and practitioners can share and discuss fundamental concepts, models, and techniques for studying and implementing collectively intelligent distributed systems. Accordingly, it welcomes original research work providing ideas and technical contributions for promoting scientific discussion and practical adoption of CI mechanisms in engineered systems. As such, the workshop also welcomes cross-disciplinary contributions (e.g., extracting computational mechanisms from natural systems exhibiting forms of CI) and contributions from related research areas like coordination (the study of interaction), multi-agent systems (MAS), socio-technical systems, organisational paradigms, Wireless Sensor and Actuator Networks (WSANs), the Internet of Things (IoT), crowd computing, and swarm robotics.
The topics of interest include (but are not limited to) the following:
Algorithms for self-adaptive/self-organizing system behaviour
Algorithms of artificial collective intelligence (e.g., multi-agent reinforcement learning)
Techniques for task-specific collective intelligence
Extraction of collective knowledge in Internet of Things systems
Collaborations of humans and artificial agents in socio-technical systems
Formal models for computational collective intelligence
Design and verification of emergent properties in distributed systems
Coordination models and languages
Programming languages for distributed CI systems
Languages for multi-tier programming or macro-programming
CI for distributed wearable computing systems
Techniques for crowd computing systems and applications
Applications of distributed CI for smart environments (e.g., smart cities, smart buildings)
Tools for programming and simulation of multi-agent systems
6th eCAS workshop on Engineering Collective Adaptive SystemsRoberto Casadei
This is the presentation introducing the 6th eCAS workshop on Engineering Collective Adaptive Systems. It recaps its scope, provides data regarding this edition, provides an overview of the program and related initiatives.
Testing: an Introduction and Panorama
- what testing is
- perspectives on testing
- xUnit, TDD, acceptance testing
- pointers to more stuff about testing
Engineering distributed applications and services in emerg-
ing and open computing scenarios like the Internet of Things, cyber-physical systems and pervasive computing, calls for identifying proper abstractions to smoothly capture collective behaviour, adaptivity, and dynamic injection and execution of concurrent distributed activities. Accordingly, we introduce a notion of “aggregate process” as a concurrent
field computation whose execution and interactions are sustained by a dynamic team of devices, and whose spatial region can opportunistically vary over time. We formalise this notion by extending the Field Calculus with a new primitive construct, spawn, used to instantiate a set of field
computations and regulate key aspects of their life-cycle. By virtue of an open-source implementation in the ScaFi framework, we show basic programming examples and benefits via two case studies of mobile ad-hoc networks and drone swarm scenarios, evaluated by simulation.
Brief overview of the Rust system programming language. Provides a concise introduction of its basic features, with an emphasis on its memory safety features (ownership, moves, borrowing) and programming style with generic functions, structures, and traits.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Comparative structure of adrenal gland in vertebrates
FScaFi: A Core Calculus for Collective Adaptive Systems Programming
1. FScaFi: A Core Calculus for Collective Adaptive
Systems Programming
Roberto Casadei1
, Mirko Viroli1
, Giorgio Audrito2
, Ferruccio Damiani2
1
ALMA MATER STUDIORUM–Università di Bologna, Cesena, Italy
2
Università di Torino, Turin, Italy
Rhodes, Greece
Talk @
ISoLA 2021
10th International Symposium on Leveraging Applications of Formal Methods,
Verification and Validation
Rigorous Engineering of Collective Adaptive Systems (REoCAS) Track
3. Collective adaptive systems (CAS) programming
Focus: collectives and their global behaviour
∠ collective: a collection of individuals entities (members) held together by some “shared plan”
[1] R. Casadei, M. Viroli, G. Audrito, D. Pianini, and F. Damiani, “Engineering collective intelligence at the edge with aggregate processes,” Eng. Appl. Artif. Intell.,
2021
[2] T. D. Wolf and T. Holvoet, “Designing self-organising emergent systems based on information flows and feedback-loops,” in Proceedings of the First International
Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007, Boston, MA, USA, July 9-11, 2007, IEEE Computer Society, 2007
[3] G. Audrito, R. Casadei, F. Damiani, and M. Viroli, “Compositional blocks for optimal self-healing gradients,” in 11th IEEE International Conference on Self-Adaptive
and Self-Organizing Systems, SASO 2017, Tucson, AZ, USA, September 18-22, 2017, IEEE Computer Society, 2017
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 1/21
4. Collective adaptive systems (CAS) programming
Focus: collectives and their global behaviour
∠ collective: a collection of individuals entities (members) held together by some “shared plan”
Recurrent abstractions
∠ Ensembles & collective tasks [1]
∠ Self-organising information flows [2]
∠ Self-healing collective structures (e.g., gradients) [3]
[1] R. Casadei, M. Viroli, G. Audrito, D. Pianini, and F. Damiani, “Engineering collective intelligence at the edge with aggregate processes,” Eng. Appl. Artif. Intell.,
2021
[2] T. D. Wolf and T. Holvoet, “Designing self-organising emergent systems based on information flows and feedback-loops,” in Proceedings of the First International
Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007, Boston, MA, USA, July 9-11, 2007, IEEE Computer Society, 2007
[3] G. Audrito, R. Casadei, F. Damiani, and M. Viroli, “Compositional blocks for optimal self-healing gradients,” in 11th IEEE International Conference on Self-Adaptive
and Self-Organizing Systems, SASO 2017, Tucson, AZ, USA, September 18-22, 2017, IEEE Computer Society, 2017
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 1/21
5. Aggregate Computing (AC) paradigm for CAS [4]
“Self-organisation-like” computational/programming model
interaction: continuous communication with neighbours only (→ decentralisation)
behaviour: continuous execution of async rounds of sense – compute – (inter)act
[4] M. Viroli, J. Beal, F. Damiani, G. Audrito, R. Casadei, and D. Pianini, “From distributed coordination to field calculus and aggregate computing,” Journal of Logical
and Algebraic Methods in Programming, 2019
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 2/21
6. Aggregate Computing (AC) paradigm for CAS [4]
“Self-organisation-like” computational/programming model
interaction: continuous communication with neighbours only (→ decentralisation)
behaviour: continuous execution of async rounds of sense – compute – (inter)act
abstraction: computational fields ( → field calculus)
paradigm: functional, macroprogramming
∠ an “aggregate program” as a “shared plan” expressed as composition of “collective behaviours”
neighborhood
device
source destination
gradient distance
gradient
<=
+
dilate
width
37
10
[4] M. Viroli, J. Beal, F. Damiani, G. Audrito, R. Casadei, and D. Pianini, “From distributed coordination to field calculus and aggregate computing,” Journal of Logical
and Algebraic Methods in Programming, 2019
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 2/21
7. Aggregate programming as internal DSL
Problem: embedding aggregate programming into mainstream PLs
Namely, providing an implementation of the higher-order field calculus (HFC)
Previous languages were standalone DSLs (e.g., Protelis)
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 3/21
8. Aggregate programming as internal DSL
Problem: embedding aggregate programming into mainstream PLs
Namely, providing an implementation of the higher-order field calculus (HFC)
Previous languages were standalone DSLs (e.g., Protelis)
Embedding as an internal DSL has benefits
- easier to implement, typically (assuming host PL is suitable)
- easier integration with host PL (and corresponding platform)
- more reuse—cf. knowledge (syntax, typing) and access to general purpose features/toolchains
, less flexibility (cf. syntactic/semantic constraints)
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 3/21
10. ScaFi & FScaFi
e We worked on an open-source Scala impl inspired by HFC
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 4/21
11. ScaFi & FScaFi
e We worked on an open-source Scala impl inspired by HFC
ú ScaFi (Scala Fields) DSL
ú a variant calculus: FScaFi (Featherweight ScaFi)
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 4/21
12. ScaFi & FScaFi
e We worked on an open-source Scala impl inspired by HFC
ú ScaFi (Scala Fields) DSL
ú a variant calculus: FScaFi (Featherweight ScaFi)
other tools (simulator, runtime etc.) provided to make ScaFi a comprehensive aggregate
programming toolkit: https://scafi.github.io
SCAFI-CORE
SPALA
(AC PLATFORM)
SCAFI-TESTS
AKKA-CORE AKKA-REMOTING
SCAFI-SIMULATOR
SCAFI-SIMULATOR-GUI
SCAFI-STDLIB-EXT
SCAFI-DISTRIBUTED
SCAFI-COMMONS
(space-time abstractions)
DEMOS
depends on
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 4/21
13. Syntax
P ::= F e program
F ::= def d(x){e} function declaration
e ::= x
54. (x) => {e} function value
trait Constructs {
// Key constructs
def rep[A](init: => A)(fun: (A) => A): A
def foldhood[A](init: => A)(aggr: (A, A) => A)(expr: => A): A
def nbr[A](expr: => A): A
def aggregate[A](b: => A): A // to wrap bodies of ordinary Scala functions
// Abstract types: device identifiers and capability names
type ID, CNAME;
// Access to context
def mid(): ID
def sense[A](name: CNAME): A
def nbrvar[A](name: CNAME): A
}
o Note: (1) no explicit fields in types; (2) nbr calls only within foldhoods (cf. by-name args)
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 5/21
55. Semantics: two main pieces
Device semantics (big-step op-sem)
δ, δ0
; Θ; σ ` e ⇓ θ
“expression e evaluates to value-tree θ on device δ with respect to the neighbour δ0
,
value-tree environment Θ and sensor state σ”
communication based on structural alignment of value-trees
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 6/21
56. Semantics: two main pieces
Device semantics (big-step op-sem)
δ, δ0
; Θ; σ ` e ⇓ θ
“expression e evaluates to value-tree θ on device δ with respect to the neighbour δ0
,
value-tree environment Θ and sensor state σ”
communication based on structural alignment of value-trees
Network semantics (small-step op-sem)
N
act
−
−
→ N
network evolves through
∠ act = δ+ (device computation)
∠ act = δ− (communication)
∠ act = env (environment evolution)
See also: G. Audrito, R. Casadei, F. Damiani, and M. Viroli, “Computation against a
neighbour,” CoRR, 2020. arXiv: 2012.08626 W
R.Casadei Background & Motivation Contribution Examples & More Conclusion References 6/21
57. Device semantics (big-step)
Value-trees and value-tree environments:
θ ::= vhθi value-tree
Θ ::= δ 7→ θ value-tree environment
Rules for expression evaluation: δ, δ
0
; Θ; σ ` e ⇓ θ
[E-VAL]
δ, δ0; Θ; σ ` v ⇓ vhi
[E-B-APP]
δ, δ; π1(Θ); σ ` e ⇓ θ δ, δ0; πi+1(Θ); σ ` ei ⇓ θi for all i ∈ 1, . . . , n
v = LbM
πb(Θ),σ
δ,δ0 (ρ(θ)) (b = ρ(θ) is not relational ) ∨ (δ0 ∈ dom(πb(Θ)) ∪ {δ})
δ, δ0; Θ; σ ` e(e) ⇓ vhθ, θ, vi
[E-D-APP]
δ, δ; π1(Θ); σ ` e ⇓ θ δ, δ0; πi+1(Θ); σ ` ei ⇓ θi for all i ∈ 1, . . . , n
f = ρ(θ) is not a built-in δ, δ0; πf(Θ); σ ` body(f)[args(f) := ρ(θ)] ⇓ θ0
δ, δ0; Θ; σ ` e(e) ⇓ ρ(θ0)hθ, θ, θ0i
[E-REP]
δ, δ; π1(Θ); σ ` e1 ⇓ θ1 v1 = ρ(θ1)
δ, δ; π2(Θ); σ ` e2(v0) ⇓ θ2 v2 = ρ(θ2)
v0 =
ρ(π2(Θ))(δ) if δ ∈ dom(Θ)
v1 otherwise
δ, δ0; Θ; σ ` rep(e1){e2} ⇓ v2hθ1, θ2i
[E-NBR] δ 6= δ0 ∈ dom(Θ) θ = Θ(δ0)
δ, δ0; Θ; σ ` nbr{e} ⇓ θ
[E-NBR-LOC] δ, δ; π1(Θ); σ ` e ⇓ θ
δ, δ; Θ; σ ` nbr{e} ⇓ ρ(θ)hθi
[E-FOLD]
δ, δ; π1(Θ); σ ` e1 ⇓ θ1 δ, δ; π2(Θ); σ ` e2 ⇓ θf f = ρ(θf )
δ1, . . . , δn = dom(Θ) {δ} n ≥ m ≥ 0, δ1, . . . , δm increasing, δ0 = δ
δ, δi ; π3(Θ); σ ` e3 ⇓ θi for all i ∈ 0, ..., m
δ, δj ; π3(Θ); σ ` e3 FAIL for all j ∈ m + 1, ..., n
δ, δ; ∅; σ ` f(ρ(θi ), ρ(θi )) ⇓ θi+1 for all i ∈ 1, ..., m
δ, δ0; Θ; σ ` foldhood(e1, e2, e3) ⇓ ρ(θm+1)hθ1, θf , θ0i
R.Casadei Background Motivation Contribution Examples More Conclusion References 7/21
58. Network semantics (small-step, SOS)
System configurations and action labels:
Ψ ::= δ 7→ Θ value-tree field
α ::= δ 7→ a with a ∈ {false, true} activation predicate
Stat ::= hΨ, αi status
::= hδ, δ
0
i topology
Σ ::= δ 7→ σ sensor state
Env ::= h, Σi environment
N ::= hEnv; Stati network configuration
act ::= δ+
64. Examples
Web playground: https://scafi.github.io/web/ [6]
[6] G. Aguzzi, R. Casadei, N. Maltoni, D. Pianini, and M. Viroli, “Scafi-web: A web-based application for field-based coordination programming,” in Coordination
Models and Languages - 23rd IFIP WG 6.1 International Conference, COORDINATION 2021, Held as Part of the 16th International Federated Conference on Distributed
Computing Techniques, DisCoTec 2021, Valletta, Malta, June 14-18, 2021, Proceedings, F. Damiani and O. Dardha, Eds., ser. Lecture Notes in Computer Science,
Springer, 2021
R.Casadei Background Motivation Contribution Examples More Conclusion References 9/21
65. Examples: gradient [3]
Gradient: resiliently build the field of shortest distances from a source area
[3] G. Audrito, R. Casadei, F. Damiani, and M. Viroli, “Compositional blocks for optimal self-healing gradients,” in 11th IEEE International Conference on Self-Adaptive
and Self-Organizing Systems, SASO 2017, Tucson, AZ, USA, September 18-22, 2017, IEEE Computer Society, 2017
R.Casadei Background Motivation Contribution Examples More Conclusion References 10/21
66. Examples: channel (around obstacles) (1/3)
Channel: resiliently define a domain of devices providing the shortest path from a
source area to a destination area
R.Casadei Background Motivation Contribution Examples More Conclusion References 11/21
69. Examples: SCR pattern [7]
Self-organising Coordination Regions (SCR) pattern: elect leaders → use
gradients to define areas → spread collect info in those areas to support feedback
loops
[7] R. Casadei, D. Pianini, M. Viroli, and A. Natali, “Self-organising coordination regions: A pattern for edge computing,” in Coordination Models and Languages - 21st
IFIP WG 6.1 International Conference, COORDINATION 2019, Held as Part of the 14th International Federated Conference on Distributed Computing Techniques,
DisCoTec 2019, Kongens Lyngby, Denmark, June 17-21, 2019, Proceedings, H. R. Nielson and E. Tuosto, Eds., ser. Lecture Notes in Computer Science, Springer, 2019
R.Casadei Background Motivation Contribution Examples More Conclusion References 14/21
70. From building blocks to applications
Applications are built by composing functions
∠ each function comprises behaviour + interaction
∠ each function generally steers some emergent collective behaviour
∠ output of a function (namely a field) becomes the input of another
[8] J. Beal, D. Pianini, and M. Viroli, “Aggregate programming for the internet of things,” Computer, no. 9, 2015
[9] R. Casadei, M. Viroli, A. Ricci, and G. Audrito, “Tuple-based coordination in large-scale situated systems,” in Coordination Models and Languages - 23rd
International Conference, COORDINATION 2021, Valletta, Malta, June 14-18, 2021, Proceedings, ser. LNCS, Springer, 2021
[1] R. Casadei, M. Viroli, G. Audrito, D. Pianini, and F. Damiani, “Engineering collective intelligence at the edge with aggregate processes,” Eng. Appl. Artif. Intell.,
2021
[10] R. Casadei, D. Pianini, A. Placuzzi, M. Viroli, and D. Weyns, “Pulverization in cyber-physical systems: Engineering the self-organizing logic separated from
deployment,” Future Internet, no. 11, 2020
[11] G. Audrito, R. Casadei, F. Damiani, V. Stolz, and M. Viroli, “Adaptive distributed monitors of spatial properties for cyber-physical systems,” J. Syst. Softw., 2021
R.Casadei Background Motivation Contribution Examples More Conclusion References 15/21
71. From building blocks to applications
Applications are built by composing functions
∠ each function comprises behaviour + interaction
∠ each function generally steers some emergent collective behaviour
∠ output of a function (namely a field) becomes the input of another
Examples
∠ crowd engineering [8]
∠ situated tuples [9]
∠ collective tasks [1]
∠ pollution-aware household heating control [10]
∠ monitors for runtime verification of spatial properties [11]
[8] J. Beal, D. Pianini, and M. Viroli, “Aggregate programming for the internet of things,” Computer, no. 9, 2015
[9] R. Casadei, M. Viroli, A. Ricci, and G. Audrito, “Tuple-based coordination in large-scale situated systems,” in Coordination Models and Languages - 23rd
International Conference, COORDINATION 2021, Valletta, Malta, June 14-18, 2021, Proceedings, ser. LNCS, Springer, 2021
[1] R. Casadei, M. Viroli, G. Audrito, D. Pianini, and F. Damiani, “Engineering collective intelligence at the edge with aggregate processes,” Eng. Appl. Artif. Intell.,
2021
[10] R. Casadei, D. Pianini, A. Placuzzi, M. Viroli, and D. Weyns, “Pulverization in cyber-physical systems: Engineering the self-organizing logic separated from
deployment,” Future Internet, no. 11, 2020
[11] G. Audrito, R. Casadei, F. Damiani, V. Stolz, and M. Viroli, “Adaptive distributed monitors of spatial properties for cyber-physical systems,” J. Syst. Softw., 2021
R.Casadei Background Motivation Contribution Examples More Conclusion References 15/21
72. FScaFi vs. HFC
From “neighbouring field” (HFC) to “computation against a neighbour” (FScaFi)
FScaFi’ = HFC’
FScaFi HFC
[12] G. Audrito, J. Beal, F. Damiani, and M. Viroli, “Space-time universality of field calculus,” in Coordination Models and Languages - 20th International Conference,
COORDINATION 2018. Proceedings, ser. LNCS, Springer, 2018
[13] M. Viroli, G. Audrito, J. Beal, F. Damiani, and D. Pianini, “Engineering resilient collective adaptive systems by self-stabilisation,” ACM Trans. Model. Comput.
Simul., no. 2, 2018
[5] G. Audrito, R. Casadei, F. Damiani, and M. Viroli, “Computation against a neighbour,” CoRR, 2020. arXiv: 2012.08626
R.Casadei Background Motivation Contribution Examples More Conclusion References 16/21
73. FScaFi vs. HFC
From “neighbouring field” (HFC) to “computation against a neighbour” (FScaFi)
FScaFi’ = HFC’
FScaFi HFC
FScaFi’ retains basic properties and guarantees, notably universality [12] and
self-stabilisation [13]—see [5] W
[12] G. Audrito, J. Beal, F. Damiani, and M. Viroli, “Space-time universality of field calculus,” in Coordination Models and Languages - 20th International Conference,
COORDINATION 2018. Proceedings, ser. LNCS, Springer, 2018
[13] M. Viroli, G. Audrito, J. Beal, F. Damiani, and D. Pianini, “Engineering resilient collective adaptive systems by self-stabilisation,” ACM Trans. Model. Comput.
Simul., no. 2, 2018
[5] G. Audrito, R. Casadei, F. Damiani, and M. Viroli, “Computation against a neighbour,” CoRR, 2020. arXiv: 2012.08626
R.Casadei Background Motivation Contribution Examples More Conclusion References 16/21
75. Conclusion
Contribution: FScaFi calculus for the ScaFi Scala-internal aggregate programming DSL
∠ Enable self-organisation programming for CAS by a macro-perspective
R.Casadei Background Motivation Contribution Examples More Conclusion References 17/21
76. Conclusion
Contribution: FScaFi calculus for the ScaFi Scala-internal aggregate programming DSL
∠ Enable self-organisation programming for CAS by a macro-perspective
One insight: Variants of a core calculus may foster DSL embeddability while retaining
key guarantees
R.Casadei Background Motivation Contribution Examples More Conclusion References 17/21
77. Conclusion
Contribution: FScaFi calculus for the ScaFi Scala-internal aggregate programming DSL
∠ Enable self-organisation programming for CAS by a macro-perspective
One insight: Variants of a core calculus may foster DSL embeddability while retaining
key guarantees
Long-term: ScaFi provides practical tools for building aggregate computing CASs
R.Casadei Background Motivation Contribution Examples More Conclusion References 17/21
78. “Cyber-physical Collectives” Special Issue in Frontiers on
Robotics AI (Scimago Q2, open-access journal)
https://www.frontiersin.org/research-topics/24380/
mobile-cyber-physical-collectives
Guest Editors: R. Casadei, L. Esterle, R. Gamble, P. Harvey, E. Wanner
Abstract deadline: 26 November 2021
Manuscript deadline: 25 February 2022
R.Casadei Background Motivation Contribution Examples More Conclusion References 18/21
79. Bibliography (1/3)
[1] R. Casadei, M. Viroli, G. Audrito, D. Pianini, and F. Damiani, “Engineering collective intelligence at
the edge with aggregate processes,” Eng. Appl. Artif. Intell., vol. 97, p. 104 081, 2021. DOI:
10.1016/j.engappai.2020.104081. [Online]. Available:
https://doi.org/10.1016/j.engappai.2020.104081.
[2] T. D. Wolf and T. Holvoet, “Designing self-organising emergent systems based on information flows
and feedback-loops,” in Proceedings of the First International Conference on Self-Adaptive and
Self-Organizing Systems, SASO 2007, Boston, MA, USA, July 9-11, 2007, IEEE Computer Society,
2007, pp. 295–298. DOI: 10.1109/SASO.2007.16. [Online]. Available:
https://doi.org/10.1109/SASO.2007.16.
[3] G. Audrito, R. Casadei, F. Damiani, and M. Viroli, “Compositional blocks for optimal self-healing
gradients,” in 11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems,
SASO 2017, Tucson, AZ, USA, September 18-22, 2017, IEEE Computer Society, 2017,
pp. 91–100. DOI: 10.1109/SASO.2017.18. [Online]. Available:
http://doi.ieeecomputersociety.org/10.1109/SASO.2017.18.
[4] M. Viroli, J. Beal, F. Damiani, G. Audrito, R. Casadei, and D. Pianini, “From distributed coordination
to field calculus and aggregate computing,” Journal of Logical and Algebraic Methods in
Programming, vol. 109, p. 100 486, 2019, ISSN: 2352-2208. DOI:
10.1016/j.jlamp.2019.100486.
[5] G. Audrito, R. Casadei, F. Damiani, and M. Viroli, “Computation against a neighbour,” CoRR,
vol. abs/2012.08626, 2020. arXiv: 2012.08626. [Online]. Available:
https://arxiv.org/abs/2012.08626.
R.Casadei Background Motivation Contribution Examples More Conclusion References 19/21
80. Bibliography (2/3)
[6] G. Aguzzi, R. Casadei, N. Maltoni, D. Pianini, and M. Viroli, “Scafi-web: A web-based application for
field-based coordination programming,” in Coordination Models and Languages - 23rd IFIP WG 6.1
International Conference, COORDINATION 2021, Held as Part of the 16th International Federated
Conference on Distributed Computing Techniques, DisCoTec 2021, Valletta, Malta, June 14-18,
2021, Proceedings, F. Damiani and O. Dardha, Eds., ser. Lecture Notes in Computer Science,
vol. 12717, Springer, 2021, pp. 285–299. DOI: 10.1007/978-3-030-78142-2_18. [Online].
Available: https://doi.org/10.1007/978-3-030-78142-2_18.
[7] R. Casadei, D. Pianini, M. Viroli, and A. Natali, “Self-organising coordination regions: A pattern for
edge computing,” in Coordination Models and Languages - 21st IFIP WG 6.1 International
Conference, COORDINATION 2019, Held as Part of the 14th International Federated Conference
on Distributed Computing Techniques, DisCoTec 2019, Kongens Lyngby, Denmark, June 17-21,
2019, Proceedings, H. R. Nielson and E. Tuosto, Eds., ser. Lecture Notes in Computer Science,
vol. 11533, Springer, 2019, pp. 182–199. DOI: 10.1007/978-3-030-22397-7_11. [Online].
Available: https://doi.org/10.1007/978-3-030-22397-7_11.
[8] J. Beal, D. Pianini, and M. Viroli, “Aggregate programming for the internet of things,” Computer,
vol. 48, no. 9, pp. 22–30, 2015. DOI: 10.1109/MC.2015.261. [Online]. Available:
https://doi.org/10.1109/MC.2015.261.
[9] R. Casadei, M. Viroli, A. Ricci, and G. Audrito, “Tuple-based coordination in large-scale situated
systems,” in Coordination Models and Languages - 23rd International Conference,
COORDINATION 2021, Valletta, Malta, June 14-18, 2021, Proceedings, ser. LNCS, vol. 12717,
Springer, 2021, pp. 149–167. DOI: 10.1007/978-3-030-78142-2_10. [Online]. Available:
https://doi.org/10.1007/978-3-030-78142-2_10.
R.Casadei Background Motivation Contribution Examples More Conclusion References 20/21
81. Bibliography (3/3)
[10] R. Casadei, D. Pianini, A. Placuzzi, M. Viroli, and D. Weyns, “Pulverization in cyber-physical
systems: Engineering the self-organizing logic separated from deployment,” Future Internet, vol. 12,
no. 11, p. 203, 2020. DOI: 10.3390/fi12110203. [Online]. Available:
https://doi.org/10.3390/fi12110203.
[11] G. Audrito, R. Casadei, F. Damiani, V. Stolz, and M. Viroli, “Adaptive distributed monitors of spatial
properties for cyber-physical systems,” J. Syst. Softw., vol. 175, p. 110 908, 2021. DOI:
10.1016/j.jss.2021.110908. [Online]. Available:
https://doi.org/10.1016/j.jss.2021.110908.
[12] G. Audrito, J. Beal, F. Damiani, and M. Viroli, “Space-time universality of field calculus,” in
Coordination Models and Languages - 20th International Conference, COORDINATION 2018.
Proceedings, ser. LNCS, vol. 10852, Springer, 2018, pp. 1–20. DOI:
10.1007/978-3-319-92408-3_1. [Online]. Available:
https://doi.org/10.1007/978-3-319-92408-3_1.
[13] M. Viroli, G. Audrito, J. Beal, F. Damiani, and D. Pianini, “Engineering resilient collective adaptive
systems by self-stabilisation,” ACM Trans. Model. Comput. Simul., vol. 28, no. 2, 16:1–16:28, 2018.
DOI: 10.1145/3177774. [Online]. Available: https://doi.org/10.1145/3177774.
R.Casadei Background Motivation Contribution Examples More Conclusion References 21/21