Constraint Programming-based Job Dispatching for Modern HPC Applications

A High-Performance Computing job dispatcher is a critical software that assigns the finite computing resources to submitted jobs. This resource assignment over time is known as the on-line job dispatching problem in HPC systems. The fact the problem is on-line means that solutions must be computed in real-time, and their required time cannot exceed some threshold to do not affect the normal system functioning. In addition, a job dispatcher must deal with a lot of uncertainty: submission times, the number of requested resources, and duration of jobs. Heuristic-based techniques have been broadly used in HPC systems, at the cost of achieving (sub-)optimal solutions in a short time. However, the scheduling and resource allocation components are separated, thus generates a decoupled decision that may cause a performance loss. Optimization-based techniques are less used for this problem, although they can significantly improve the performance of HPC systems at the expense of higher computation time. Nowadays, HPC systems are being used for modern applications, such as big data analytics and predictive model building, that employ, in general, many short jobs. However, this information is unknown at dispatching time, and job dispatchers need to process large numbers of them quickly while ensuring high Quality-of-Service (QoS) levels. Constraint Programming (CP) has been shown to be an effective approach to tackle job dispatching problems. However, state-of-the-art CP-based job dispatchers are unable to satisfy the challenges of on-line dispatching, such as generate dispatching decisions in a brief period and integrate current and past information of the housing system. Given the previous reasons, we propose CP-based dispatchers that are more suitable for HPC systems running modern applications, generating on-line dispatching decisions in a proper time and are able to make effective use of job duration predictions to improve QoS levels, especially for workloads dominated by short jobs.

Hybrid constraint programming and metaheuristic methods for large scale optimization problems

This work presents hybrid Constraint Programming (CP) and metaheuristic methods for the solution of Large Scale Optimization Problems; it aims at integrating concepts and mechanisms from the metaheuristic methods to a CP-based tree search environment in order to exploit the advantages of both approaches. The modeling and solution of large scale combinatorial optimization problem is a topic which has arisen the interest of many researcherers in the Operations Research field; combinatorial optimization problems are widely spread in everyday life and the need of solving difficult problems is more and more urgent. Metaheuristic techniques have been developed in the last decades to effectively handle the approximate solution of combinatorial optimization problems; we will examine metaheuristics in detail, focusing on the common aspects of different techniques. Each metaheuristic approach possesses its own peculiarities in designing and guiding the solution process; our work aims at recognizing components which can be extracted from metaheuristic methods and re-used in different contexts. In particular we focus on the possibility of porting metaheuristic elements to constraint programming based environments, as constraint programming is able to deal with feasibility issues of optimization problems in a very effective manner. Moreover, CP offers a general paradigm which allows to easily model any type of problem and solve it with a problem-independent framework, differently from local search and metaheuristic methods which are highly problem specific. In this work we describe the implementation of the Local Branching framework, originally developed for Mixed Integer Programming, in a CP-based environment. Constraint programming specific features are used to ease the search process, still mantaining an absolute generality of the approach. We also propose a search strategy called Sliced Neighborhood Search, SNS, that iteratively explores slices of large neighborhoods of an incumbent solution by performing CP-based tree search and encloses concepts from metaheuristic techniques. SNS can be used as a stand alone search strategy, but it can alternatively be embedded in existing strategies as intensification and diversification mechanism. In particular we show its integration within the CP-based local branching. We provide an extensive experimental evaluation of the proposed approaches on instances of the Asymmetric Traveling Salesman Problem and of the Asymmetric Traveling Salesman Problem with Time Windows. The proposed approaches achieve good results on practical size problem, thus demonstrating the benefit of integrating metaheuristic concepts in CP-based frameworks.

Portfolio Approaches in Constraint Programming

Recent research has shown that the performance of a single, arbitrarily efficient algorithm can be significantly outperformed by using a portfolio of —possibly on-average slower— algorithms. Within the Constraint Programming (CP) context, a portfolio solver can be seen as a particular constraint solver that exploits the synergy between the constituent solvers of its portfolio for predicting which is (or which are) the best solver(s) to run for solving a new, unseen instance. In this thesis we examine the benefits of portfolio solvers in CP. Despite portfolio approaches have been extensively studied for Boolean Satisfiability (SAT) problems, in the more general CP field these techniques have been only marginally studied and used. We conducted this work through the investigation, the analysis and the construction of several portfolio approaches for solving both satisfaction and optimization problems. We focused in particular on sequential approaches, i.e., single-threaded portfolio solvers always running on the same core. We started from a first empirical evaluation on portfolio approaches for solving Constraint Satisfaction Problems (CSPs), and then we improved on it by introducing new data, solvers, features, algorithms, and tools. Afterwards, we addressed the more general Constraint Optimization Problems (COPs) by implementing and testing a number of models for dealing with COP portfolio solvers. Finally, we have come full circle by developing sunny-cp: a sequential CP portfolio solver that turned out to be competitive also in the MiniZinc Challenge, the reference competition for CP solvers.

Theoretical and implementation aspects in the mechanization of the metatheory of programming languages

Interactive theorem provers are tools designed for the certification of formal proofs developed by means of man-machine collaboration. Formal proofs obtained in this way cover a large variety of logical theories, ranging from the branches of mainstream mathematics, to the field of software verification. The border between these two worlds is marked by results in theoretical computer science and proofs related to the metatheory of programming languages. This last field, which is an obvious application of interactive theorem proving, poses nonetheless a serious challenge to the users of such tools, due both to the particularly structured way in which these proofs are constructed, and to difficulties related to the management of notions typical of programming languages like variable binding. This thesis is composed of two parts, discussing our experience in the development of the Matita interactive theorem prover and its use in the mechanization of the metatheory of programming languages. More specifically, part I covers: - the results of our effort in providing a better framework for the development of tactics for Matita, in order to make their implementation and debugging easier, also resulting in a much clearer code; - a discussion of the implementation of two tactics, providing infrastructure for the unification of constructor forms and the inversion of inductive predicates; we point out interactions between induction and inversion and provide an advancement over the state of the art. In the second part of the thesis, we focus on aspects related to the formalization of programming languages. We describe two works of ours: - a discussion of basic issues we encountered in our formalizations of part 1A of the Poplmark challenge, where we apply the extended inversion principles we implemented for Matita; - a formalization of an algebraic logical framework, posing more complex challenges, including multiple binding and a form of hereditary substitution; this work adopts, for the encoding of binding, an extension of Masahiko Sato's canonical locally named representation we designed during our visit to the Laboratory for Foundations of Computer Science at the University of Edinburgh, under the supervision of Randy Pollack.

Il passato nell’epoca della sua (ri)producibilità digitale. Torino 1943 - 45. Metodologia della ricerca con tecnologie informatiche. Sistema storico-territoriale di informazione multimediale

Se il lavoro dello storico è capire il passato come è stato compreso dalla gente che lo ha vissuto, allora forse non è azzardato pensare che sia anche necessario comunicare i risultati delle ricerche con strumenti propri che appartengono a un'epoca e che influenzano la mentalità di chi in quell'epoca vive. Emergenti tecnologie, specialmente nell’area della multimedialità come la realtà virtuale, permettono agli storici di comunicare l’esperienza del passato in più sensi. In che modo la storia collabora con le tecnologie informatiche soffermandosi sulla possibilità di fare ricostruzioni storiche virtuali, con relativi esempi e recensioni? Quello che maggiormente preoccupa gli storici è se una ricostruzione di un fatto passato vissuto attraverso la sua ricreazione in pixels sia un metodo di conoscenza della storia che possa essere considerato valido. Ovvero l'emozione che la navigazione in una realtà 3D può suscitare, è un mezzo in grado di trasmettere conoscenza? O forse l'idea che abbiamo del passato e del suo studio viene sottilmente cambiato nel momento in cui lo si divulga attraverso la grafica 3D? Da tempo però la disciplina ha cominciato a fare i conti con questa situazione, costretta soprattutto dall'invasività di questo tipo di media, dalla spettacolarizzazione del passato e da una divulgazione del passato parziale e antiscientifica. In un mondo post letterario bisogna cominciare a pensare che la cultura visuale nella quale siamo immersi sta cambiando il nostro rapporto con il passato: non per questo le conoscenze maturate fino ad oggi sono false, ma è necessario riconoscere che esiste più di una verità storica, a volte scritta a volte visuale. Il computer è diventato una piattaforma onnipresente per la rappresentazione e diffusione dell’informazione. I metodi di interazione e rappresentazione stanno evolvendo di continuo. Ed è su questi due binari che è si muove l’offerta delle tecnologie informatiche al servizio della storia. Lo scopo di questa tesi è proprio quello di esplorare, attraverso l’utilizzo e la sperimentazione di diversi strumenti e tecnologie informatiche, come si può raccontare efficacemente il passato attraverso oggetti tridimensionali e gli ambienti virtuali, e come, nel loro essere elementi caratterizzanti di comunicazione, in che modo possono collaborare, in questo caso particolare, con la disciplina storica. La presente ricerca ricostruisce alcune linee di storia delle principali fabbriche attive a Torino durante la seconda guerra mondiale, ricordando stretta relazione che esiste tra strutture ed individui e in questa città in particolare tra fabbrica e movimento operaio, è inevitabile addentrarsi nelle vicende del movimento operaio torinese che nel periodo della lotta di Liberazione in città fu un soggetto politico e sociale di primo rilievo. Nella città, intesa come entità biologica coinvolta nella guerra, la fabbrica (o le fabbriche) diventa il nucleo concettuale attraverso il quale leggere la città: sono le fabbriche gli obiettivi principali dei bombardamenti ed è nelle fabbriche che si combatte una guerra di liberazione tra classe operaia e autorità, di fabbrica e cittadine. La fabbrica diventa il luogo di "usurpazione del potere" di cui parla Weber, il palcoscenico in cui si tengono i diversi episodi della guerra: scioperi, deportazioni, occupazioni .... Il modello della città qui rappresentata non è una semplice visualizzazione ma un sistema informativo dove la realtà modellata è rappresentata da oggetti, che fanno da teatro allo svolgimento di avvenimenti con una precisa collocazione cronologica, al cui interno è possibile effettuare operazioni di selezione di render statici (immagini), di filmati precalcolati (animazioni) e di scenari navigabili interattivamente oltre ad attività di ricerca di fonti bibliografiche e commenti di studiosi segnatamente legati all'evento in oggetto. Obiettivo di questo lavoro è far interagire, attraverso diversi progetti, le discipline storiche e l’informatica, nelle diverse opportunità tecnologiche che questa presenta. Le possibilità di ricostruzione offerte dal 3D vengono così messe a servizio della ricerca, offrendo una visione integrale in grado di avvicinarci alla realtà dell’epoca presa in considerazione e convogliando in un’unica piattaforma espositiva tutti i risultati. Divulgazione Progetto Mappa Informativa Multimediale Torino 1945 Sul piano pratico il progetto prevede una interfaccia navigabile (tecnologia Flash) che rappresenti la pianta della città dell’epoca, attraverso la quale sia possibile avere una visione dei luoghi e dei tempi in cui la Liberazione prese forma, sia a livello concettuale, sia a livello pratico. Questo intreccio di coordinate nello spazio e nel tempo non solo migliora la comprensione dei fenomeni, ma crea un maggiore interesse sull’argomento attraverso l’utilizzo di strumenti divulgativi di grande efficacia (e appeal) senza perdere di vista la necessità di valicare le tesi storiche proponendosi come piattaforma didattica. Un tale contesto richiede uno studio approfondito degli eventi storici al fine di ricostruire con chiarezza una mappa della città che sia precisa sia topograficamente sia a livello di navigazione multimediale. La preparazione della cartina deve seguire gli standard del momento, perciò le soluzioni informatiche utilizzate sono quelle fornite da Adobe Illustrator per la realizzazione della topografia, e da Macromedia Flash per la creazione di un’interfaccia di navigazione. La base dei dati descrittivi è ovviamente consultabile essendo contenuta nel supporto media e totalmente annotata nella bibliografia. È il continuo evolvere delle tecnologie d'informazione e la massiccia diffusione dell’uso dei computer che ci porta a un cambiamento sostanziale nello studio e nell’apprendimento storico; le strutture accademiche e gli operatori economici hanno fatto propria la richiesta che giunge dall'utenza (insegnanti, studenti, operatori dei Beni Culturali) di una maggiore diffusione della conoscenza storica attraverso la sua rappresentazione informatizzata. Sul fronte didattico la ricostruzione di una realtà storica attraverso strumenti informatici consente anche ai non-storici di toccare con mano quelle che sono le problematiche della ricerca quali fonti mancanti, buchi della cronologia e valutazione della veridicità dei fatti attraverso prove. Le tecnologie informatiche permettono una visione completa, unitaria ed esauriente del passato, convogliando tutte le informazioni su un'unica piattaforma, permettendo anche a chi non è specializzato di comprendere immediatamente di cosa si parla. Il miglior libro di storia, per sua natura, non può farlo in quanto divide e organizza le notizie in modo diverso. In questo modo agli studenti viene data l'opportunità di apprendere tramite una rappresentazione diversa rispetto a quelle a cui sono abituati. La premessa centrale del progetto è che i risultati nell'apprendimento degli studenti possono essere migliorati se un concetto o un contenuto viene comunicato attraverso più canali di espressione, nel nostro caso attraverso un testo, immagini e un oggetto multimediale. Didattica La Conceria Fiorio è uno dei luoghi-simbolo della Resistenza torinese. Il progetto è una ricostruzione in realtà virtuale della Conceria Fiorio di Torino. La ricostruzione serve a arricchire la cultura storica sia a chi la produce, attraverso una ricerca accurata delle fonti, sia a chi può poi usufruirne, soprattutto i giovani, che, attratti dall’aspetto ludico della ricostruzione, apprendono con più facilità. La costruzione di un manufatto in 3D fornisce agli studenti le basi per riconoscere ed esprimere la giusta relazione fra il modello e l’oggetto storico. Le fasi di lavoro attraverso cui si è giunti alla ricostruzione in 3D della Conceria: . una ricerca storica approfondita, basata sulle fonti, che possono essere documenti degli archivi o scavi archeologici, fonti iconografiche, cartografiche, ecc.; . La modellazione degli edifici sulla base delle ricerche storiche, per fornire la struttura geometrica poligonale che permetta la navigazione tridimensionale; . La realizzazione, attraverso gli strumenti della computer graphic della navigazione in 3D. Unreal Technology è il nome dato al motore grafico utilizzato in numerosi videogiochi commerciali. Una delle caratteristiche fondamentali di tale prodotto è quella di avere uno strumento chiamato Unreal editor con cui è possibile costruire mondi virtuali, e che è quello utilizzato per questo progetto. UnrealEd (Ued) è il software per creare livelli per Unreal e i giochi basati sul motore di Unreal. E’ stata utilizzata la versione gratuita dell’editor. Il risultato finale del progetto è un ambiente virtuale navigabile raffigurante una ricostruzione accurata della Conceria Fiorio ai tempi della Resistenza. L’utente può visitare l’edificio e visualizzare informazioni specifiche su alcuni punti di interesse. La navigazione viene effettuata in prima persona, un processo di “spettacolarizzazione” degli ambienti visitati attraverso un arredamento consono permette all'utente una maggiore immersività rendendo l’ambiente più credibile e immediatamente codificabile. L’architettura Unreal Technology ha permesso di ottenere un buon risultato in un tempo brevissimo, senza che fossero necessari interventi di programmazione. Questo motore è, quindi, particolarmente adatto alla realizzazione rapida di prototipi di una discreta qualità, La presenza di un certo numero di bug lo rende, però, in parte inaffidabile. Utilizzare un editor da videogame per questa ricostruzione auspica la possibilità di un suo impiego nella didattica, quello che le simulazioni in 3D permettono nel caso specifico è di permettere agli studenti di sperimentare il lavoro della ricostruzione storica, con tutti i problemi che lo storico deve affrontare nel ricreare il passato. Questo lavoro vuole essere per gli storici una esperienza nella direzione della creazione di un repertorio espressivo più ampio, che includa gli ambienti tridimensionali. Il rischio di impiegare del tempo per imparare come funziona questa tecnologia per generare spazi virtuali rende scettici quanti si impegnano nell'insegnamento, ma le esperienze di progetti sviluppati, soprattutto all’estero, servono a capire che sono un buon investimento. Il fatto che una software house, che crea un videogame di grande successo di pubblico, includa nel suo prodotto, una serie di strumenti che consentano all'utente la creazione di mondi propri in cui giocare, è sintomatico che l'alfabetizzazione informatica degli utenti medi sta crescendo sempre più rapidamente e che l'utilizzo di un editor come Unreal Engine sarà in futuro una attività alla portata di un pubblico sempre più vasto. Questo ci mette nelle condizioni di progettare moduli di insegnamento più immersivi, in cui l'esperienza della ricerca e della ricostruzione del passato si intreccino con lo studio più tradizionale degli avvenimenti di una certa epoca. I mondi virtuali interattivi vengono spesso definiti come la forma culturale chiave del XXI secolo, come il cinema lo è stato per il XX. Lo scopo di questo lavoro è stato quello di suggerire che vi sono grosse opportunità per gli storici impiegando gli oggetti e le ambientazioni in 3D, e che essi devono coglierle. Si consideri il fatto che l’estetica abbia un effetto sull’epistemologia. O almeno sulla forma che i risultati delle ricerche storiche assumono nel momento in cui devono essere diffuse. Un’analisi storica fatta in maniera superficiale o con presupposti errati può comunque essere diffusa e avere credito in numerosi ambienti se diffusa con mezzi accattivanti e moderni. Ecco perchè non conviene seppellire un buon lavoro in qualche biblioteca, in attesa che qualcuno lo scopra. Ecco perchè gli storici non devono ignorare il 3D. La nostra capacità, come studiosi e studenti, di percepire idee ed orientamenti importanti dipende spesso dai metodi che impieghiamo per rappresentare i dati e l’evidenza. Perché gli storici possano ottenere il beneficio che il 3D porta con sè, tuttavia, devono sviluppare un’agenda di ricerca volta ad accertarsi che il 3D sostenga i loro obiettivi di ricercatori e insegnanti. Una ricostruzione storica può essere molto utile dal punto di vista educativo non sono da chi la visita ma, anche da chi la realizza. La fase di ricerca necessaria per la ricostruzione non può fare altro che aumentare il background culturale dello sviluppatore. Conclusioni La cosa più importante è stata la possibilità di fare esperienze nell’uso di mezzi di comunicazione di questo genere per raccontare e far conoscere il passato. Rovesciando il paradigma conoscitivo che avevo appreso negli studi umanistici, ho cercato di desumere quelle che potremo chiamare “leggi universali” dai dati oggettivi emersi da questi esperimenti. Da punto di vista epistemologico l’informatica, con la sua capacità di gestire masse impressionanti di dati, dà agli studiosi la possibilità di formulare delle ipotesi e poi accertarle o smentirle tramite ricostruzioni e simulazioni. Il mio lavoro è andato in questa direzione, cercando conoscere e usare strumenti attuali che nel futuro avranno sempre maggiore presenza nella comunicazione (anche scientifica) e che sono i mezzi di comunicazione d’eccellenza per determinate fasce d’età (adolescenti). Volendo spingere all’estremo i termini possiamo dire che la sfida che oggi la cultura visuale pone ai metodi tradizionali del fare storia è la stessa che Erodoto e Tucidide contrapposero ai narratori di miti e leggende. Prima di Erodoto esisteva il mito, che era un mezzo perfettamente adeguato per raccontare e dare significato al passato di una tribù o di una città. In un mondo post letterario la nostra conoscenza del passato sta sottilmente mutando nel momento in cui lo vediamo rappresentato da pixel o quando le informazioni scaturiscono non da sole, ma grazie all’interattività con il mezzo. La nostra capacità come studiosi e studenti di percepire idee ed orientamenti importanti dipende spesso dai metodi che impieghiamo per rappresentare i dati e l’evidenza. Perché gli storici possano ottenere il beneficio sottinteso al 3D, tuttavia, devono sviluppare un’agenda di ricerca volta ad accertarsi che il 3D sostenga i loro obiettivi di ricercatori e insegnanti. Le esperienze raccolte nelle pagine precedenti ci portano a pensare che in un futuro non troppo lontano uno strumento come il computer sarà l’unico mezzo attraverso cui trasmettere conoscenze, e dal punto di vista didattico la sua interattività consente coinvolgimento negli studenti come nessun altro mezzo di comunicazione moderno.

Constraint based methods for allocation and scheduling of periodic applications

This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design. The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems. In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms. We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity. Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.

Interoperability Challenges in Internet of Things Systems: a Service-Oriented Computing Approach

Internet of Things systems are pervasive systems evolved from cyber-physical to large-scale systems. Due to the number of technologies involved, software development involves several integration challenges. Among them, the ones preventing proper integration are those related to the system heterogeneity, and thus addressing interoperability issues. From a software engineering perspective, developers mostly experience the lack of interoperability in the two phases of software development: programming and deployment. On the one hand, modern software tends to be distributed in several components, each adopting its most-appropriate technology stack, pushing programmers to code in a protocol- and data-agnostic way. On the other hand, each software component should run in the most appropriate execution environment and, as a result, system architects strive to automate the deployment in distributed infrastructures. This dissertation aims to improve the development process by introducing proper tools to handle certain aspects of the system heterogeneity. Our effort focuses on three of these aspects and, for each one of those, we propose a tool addressing the underlying challenge. The first tool aims to handle heterogeneity at the transport and application protocol level, the second to manage different data formats, while the third to obtain optimal deployment. To realize the tools, we adopted a linguistic approach, i.e.\ we provided specific linguistic abstractions that help developers to increase the expressive power of the programming language they use, writing better solutions in more straightforward ways. To validate the approach, we implemented use cases to show that the tools can be used in practice and that they help to achieve the expected level of interoperability. In conclusion, to move a step towards the realization of an integrated Internet of Things ecosystem, we target programmers and architects and propose them to use the presented tools to ease the software development process.

Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

On Reasonable Space and Time Cost Models for the λ-Calculus

Slot and van Emde Boas Invariance Thesis states that a time (respectively, space) cost model is reasonable for a computational model C if there are mutual simulations between Turing machines and C such that the overhead is polynomial in time (respectively, linear in space). The rationale is that under the Invariance Thesis, complexity classes such as LOGSPACE, P, PSPACE, become robust, i.e. machine independent. In this dissertation, we want to find out if it possible to define a reasonable space cost model for the lambda-calculus, the paradigmatic model for functional programming languages. We start by considering an unusual evaluation mechanism for the lambda-calculus, based on Girard's Geometry of Interaction, that was conjectured to be the key ingredient to obtain a space reasonable cost model. By a fine complexity analysis of this schema, based on new variants of non-idempotent intersection types, we disprove this conjecture. Then, we change the target of our analysis. We consider a variant over Krivine's abstract machine, a standard evaluation mechanism for the call-by-name lambda-calculus, optimized for space complexity, and implemented without any pointer. A fine analysis of the execution of (a refined version of) the encoding of Turing machines into the lambda-calculus allows us to conclude that the space consumed by this machine is indeed a reasonable space cost model. In particular, for the first time we are able to measure also sub-linear space complexities. Moreover, we transfer this result to the call-by-value case. Finally, we provide also an intersection type system that characterizes compositionally this new reasonable space measure. This is done through a minimal, yet non trivial, modification of the original de Carvalho type system.

Continual learning for computer vision applications

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic and eventually surpass the intelligence observed in biological systems including, ambitiously, the one observed in humans. The main distinctive strength of humans is their ability to build a deep understanding of the world by learning continuously and drawing from their experiences. This ability, which is found in various degrees in all intelligent biological beings, allows them to adapt and properly react to changes by incrementally expanding and refining their knowledge. Arguably, achieving this ability is one of the main goals of Artificial Intelligence and a cornerstone towards the creation of intelligent artificial agents. Modern Deep Learning approaches allowed researchers and industries to achieve great advancements towards the resolution of many long-standing problems in areas like Computer Vision and Natural Language Processing. However, while this current age of renewed interest in AI allowed for the creation of extremely useful applications, a concerningly limited effort is being directed towards the design of systems able to learn continuously. The biggest problem that hinders an AI system from learning incrementally is the catastrophic forgetting phenomenon. This phenomenon, which was discovered in the 90s, naturally occurs in Deep Learning architectures where classic learning paradigms are applied when learning incrementally from a stream of experiences. This dissertation revolves around the Continual Learning field, a sub-field of Machine Learning research that has recently made a comeback following the renewed interest in Deep Learning approaches. This work will focus on a comprehensive view of continual learning by considering algorithmic, benchmarking, and applicative aspects of this field. This dissertation will also touch on community aspects such as the design and creation of research tools aimed at supporting Continual Learning research, and the theoretical and practical aspects concerning public competitions in this field.

Typed behavioural equivalences in the Pi-Calculus

In this thesis, I study the notion of program equivalences, i.e. proving that two programs can be used interchangeably without altering the overall observable behaviour. This definition is highly dependent on the contexts in which these programs can be used; does the context have exceptions, parallelism, etc... So proofs also need to be adapted according to the expressiveness of those contexts. This thesis presents on the pi-calculus – a concurrent programming language – under various typing constraints. Types allows us to impose different disciplines like forcing a sequential execution, or ensuring linearity, meaning an object can be used once. In each case, the bisimulation, a standard proof technique for the pi-calculus, needs to be adapted accordingly to obtain a suitable equivalence. We then test how using the modified bisimulations can be used to reason about a language with higher-order functions and references, which once translated into the pi-calculus satisfies the typing constraints.

Teaching informatics to novices: big ideas and the necessity of optimal guidance

This thesis reports on the two main areas of our research: introductory programming as the traditional way of accessing informatics and cultural teaching informatics through unconventional pathways. The research on introductory programming aims to overcome challenges in traditional programming education, thus increasing participation in informatics. Improving access to informatics enables individuals to pursue more and better professional opportunities and contribute to informatics advancements. We aimed to balance active, student-centered activities and provide optimal support to novices at their level. Inspired by Productive Failure and exploring the concept of notional machine, our work focused on developing Necessity Learning Design, a design to help novices tackle new programming concepts. Using this design, we implemented a learning sequence to introduce arrays and evaluated it in a real high-school context. The subsequent chapters discuss our experiences teaching CS1 in a remote-only scenario during the COVID-19 pandemic and our collaborative effort with primary school teachers to develop a learning module for teaching iteration using a visual programming environment. The research on teaching informatics principles through unconventional pathways, such as cryptography, aims to introduce informatics to a broader audience, particularly younger individuals that are less technical and professional-oriented. It emphasizes the importance of understanding informatics's cultural and scientific aspects to focus on the informatics societal value and its principles for active citizenship. After reflecting on computational thinking and inspired by the big ideas of science and informatics, we describe our hands-on approach to teaching cryptography in high school, which leverages its key scientific elements to emphasize its social aspects. Additionally, we present an activity for teaching public-key cryptography using graphs to explore fundamental concepts and methods in informatics and mathematics and their interdisciplinarity. In broadening the understanding of informatics, these research initiatives also aim to foster motivation and prime for more professional learning of informatics.

Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan

The study of ancient, undeciphered scripts presents unique challenges, that depend both on the nature of the problem and on the peculiarities of each writing system. In this thesis, I present two computational approaches that are tailored to two different tasks and writing systems. The first of these methods is aimed at the decipherment of the Linear A afraction signs, in order to discover their numerical values. This is achieved with a combination of constraint programming, ad-hoc metrics and paleographic considerations. The second main contribution of this thesis regards the creation of an unsupervised deep learning model which uses drawings of signs from ancient writing system to learn to distinguish different graphemes in the vector space. This system, which is based on techniques used in the field of computer vision, is adapted to the study of ancient writing systems by incorporating information about sequences in the model, mirroring what is often done in natural language processing. In order to develop this model, the Cypriot Greek Syllabary is used as a target, since this is a deciphered writing system. Finally, this unsupervised model is adapted to the undeciphered Cypro-Minoan and it is used to answer open questions about this script. In particular, by reconstructing multiple allographs that are not agreed upon by paleographers, it supports the idea that Cypro-Minoan is a single script and not a collection of three script like it was proposed in the literature. These results on two different tasks shows that computational methods can be applied to undeciphered scripts, despite the relatively low amount of available data, paving the way for further advancement in paleography using these methods.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Towards a logical foundation of randomized computation

This dissertation investigates the relations between logic and TCS in the probabilistic setting. It is motivated by two main considerations. On the one hand, since their appearance in the 1960s-1970s, probabilistic models have become increasingly pervasive in several fast-growing areas of CS. On the other, the study and development of (deterministic) computational models has considerably benefitted from the mutual interchanges between logic and CS. Nevertheless, probabilistic computation was only marginally touched by such fruitful interactions. The goal of this thesis is precisely to (start) bring(ing) this gap, by developing logical systems corresponding to specific aspects of randomized computation and, therefore, by generalizing standard achievements to the probabilistic realm. To do so, our key ingredient is the introduction of new, measure-sensitive quantifiers associated with quantitative interpretations. The dissertation is tripartite. In the first part, we focus on the relation between logic and counting complexity classes. We show that, due to our classical counting propositional logic, it is possible to generalize to counting classes, the standard results by Cook and Meyer and Stockmeyer linking propositional logic and the polynomial hierarchy. Indeed, we show that the validity problem for counting-quantified formulae captures the corresponding level in Wagner's hierarchy. In the second part, we consider programming language theory. Type systems for randomized \lambda-calculi, also guaranteeing various forms of termination properties, were introduced in the last decades, but these are not "logically oriented" and no Curry-Howard correspondence is known for them. Following intuitions coming from counting logics, we define the first probabilistic version of the correspondence. Finally, we consider the relationship between arithmetic and computation. We present a quantitative extension of the language of arithmetic able to formalize basic results from probability theory. This language is also our starting point to define randomized bounded theories and, so, to generalize canonical results by Buss.