Miracle-dz/UniFAR-FTUs huggingface dataset:
WeirdAnalogyMatic: Experimenting with Analogy for Lyrics Transformation

This paper is on the transformation of text relying mostly on a common analogy vector operation, computed in a distributional model, i.e., static word embeddings. Given a theme, original song lyrics have their words replaced by new ones, related to the new theme as the original words are to the original theme. As this is not enough for producing good lyrics, towards more coherent and singable text, constraints are gradually applied to the replacements. Human opinions conﬁrmed that more balanced lyrics are obtained when there is a one-to-one mapping between original words and their replacement; only content words are replaced; and the replacement has the same part-of-speech and rhythm as the original word.
Casual Creators in the Wild: A Typology of Commercial Generative Creativity Support Tools

Casual creators are a genre of creativity support tool that use generative systems to empower autotelic and enjoyable amateur creativity. They have been posited as a unique means of democratising creativity through the support of user exploration via system gen-erativity, yet little is known about what casual creators are actually available to wider audiences. We conducted a qualitative analysis of currently available casual creators on the App Store. We found three categories of interaction techniques across 89 commercially available apps that qualified as casual creators, which we describe in their exploration potential, feedback speed, and user autonomy
The Case for More Flexible Distributed Tracing

Distributed tracing has been widely adopted as a means to troubleshoot distributed systems in which traditional, device-centric debugging loses traction. However, the debugging capabilities of tracing are underused today because of the immutable nature of tracing instrumentation. Additionally, current tracing solutions do not address in-network compute, and hence cannot be considered end-to-end. We argue for a new tracing workflow in which tracing instrumentation is made more flexible, allowing DevOps to use a query language to specify tracing tasks for computation happening both on x86 servers and network devices.
Functional Aggregate Queries with Additive Inequalities

Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some of the input factors are defined by a collection of additive inequalities between variables. We refer to these queries as FAQ-AI for short. To answer FAQ-AI in the Boolean semiring, we define relaxed tree decompositions and relaxed submodular and fractional hypertree width parameters. We show that an extension of the InsideOut algorithm using Chazelle’s geometric data structure for solving the semigroup range search problem can answer Boolean FAQ-AI in time given by these new width parameters. This new algorithm achieves lower complexity than known solutions for FAQ-AI. It also recovers some known results in database query answering. Our second contribution is a relaxation of the set of polymatroids that gives rise to the counting version of the submodular width, denoted by #subw. This new width is sandwiched between the submodular and the fractional hypertree widths. Any FAQ and FAQ-AI over one semiring can be answered in time proportional to #subw and respectively to the relaxed version of #subw. We present three applications of our FAQ-AI framework to relational machine learning: k-means clustering, training linear support vector machines, and training models using non-polynomial loss. These optimization problems can be solved over a database asymptotically faster than computing the join of the database relations.
: books: : arrow_right: : slight_smile: An Approach for Text-to-Emoji Translation

The task of translating text to images holds some valid creative potential and has been the subject of study in Computational Creativity. In this paper, we present pre-liminary work focused on emoji translation. The work-in-progress system is based on techniques of information retrieval. We compare the performance of our sys-tem with three deep learning approaches using a text-to-emoji task. The preliminary results suggest some advantages of using a knowledge-base as opposed to a purely data-driven approach. This paper aims to situate the research, underline its relevance and attract valuable feedback for future developments.
Controlled Size Chitosan Nanoparticles as an Efficient, Biocompatible Oligonucleotides Delivery System

Polymeric nanoparticles of chitosan crosslinked with glutaraldehyde have been prepared using reverse micellar system. An optically clear solution was obtained on redispersing these nanoparticles in aqueous buffer. The nanoparticles were characterized for their size and surface morphology employing dynamic laser scattering (DLS) and transmission electron microscopy (TEM). The TEM images showed spherical particles with smooth surface and narrow size distribution of about 90 nm, which was also supported by DLS data. Size and morphology of the particles remains the same on redispersing the lyophilized powder of these nanoparticles in aqueous buffer. Further, these nanoparticles were loaded with different synthetic oligonucleotides (ODNs). In vitro pH dependent release of the adsorbed oligonucleotides from these nanoparticles was also studied. At basic pH the release of oligonucleotides was found higher as compared with neutral and acidic medium. Cytotoxicity studies done on HEK 293 cells reveals that oligonucleotide loaded nanoparticles have high cell viability of nearly 76–88% whereas those of lipofectamine was about 35%. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
Mechanized Formal Model of Bitcoin's Blockchain Validation Procedures

We present the ﬁrst mechanized formal model of Bitcoin’s transaction and blockchain data structures including the formalization of the blockchain validation procedures. Our formal model, though still a simpliﬁed representation of an actual Bitcoin blockchain, includes regular and coinbase transactions, segregated witnesses, relative and absolute locktime, the Bitcoin Script language expressions together with a denotational semantics, transaction fees and block rewards. We formally specify the details of validity checks performed when adding new blocks to the blockchain. We assume perfect cryptography and use the symbolic approach for modeling hash functions and digital signatures. To demonstrate the utility of the model, we formally state and prove several essential properties of a valid blockchain – transactions are unique, each coin can be spent at most once and the new value is only created through block rewards. The model and the proofs are largely independent of Bitcoin speciﬁc details and easily generalize to any cryptocurrency blockchain based on the Unspent Transaction Output (UTXO) paradigm. We mechanize all the results using the Coq proof assistant.
Automated Music Generation for Visual Art through Emotion

We explore methods of generating music from images using emotion as the connection between the visual and auditory domains. Our primary goal is to express visual art in the auditory domain. The resulting music can enrich visual art or provide a form of translation to facilitate enjoying visual art without reliance on the visual system. We use pre-trained image representations and explore two different types of music modelling methods based on RNN and Transformer architectures to build models capable of generating music given an image as input. To evaluate the performance of these methods, preliminary human and machine evaluation are conducted. The results suggest that both music generators are able to express music with an emotional connection.
Exploring CC in XR: Visualizing Creative Conversation Topics to Facilitate Meaningful Face-to-Face Interaction

With its ability to capture, model, and generate real-and-virtual combined environments, Extended Reality (XR) opens unique opportunities for designing creative agents. In this paper, we present HeyLo, a system for generating meaningful, visual conversation cues in XR. HeyLo autonomously analyzes user tweets to identify common interests and visualizes these interests as conversation cues using emoji. We argue that this system demonstrates creativity using four metrics that characterize novelty, value, and intentionality in the domain of conversation cues: speciﬁcity, inter-and intra-user variance, and relevance. We discuss several potential research questions that we hope to answer in the future using this system and the broader implications of a creative system that is capable of bridging arbitrary interests to innovate its own creative ideas.
Creativity explained by Computational Cognitive Neuroscience

Recently, models in Computational Cognitive Neuro-science (CCN) have gained a renewed interest because they could help analyze current limitations in Artiﬁcial Intelligence (AI) and propose operational ways to address them. These limitations are related to difﬁcul-ties in giving a semantic grounding to manipulated concepts, in coping with high dimensionality and in managing uncertainty. In this paper, we describe the main principles and mechanisms of these models and explain that they can be directly transferred to Computational Creativity (CC), to propose operational mechanisms but also a better understanding of what creativity is.
Creating Six-word Stories via Inferred Linguistic and Semantic Formats

High-quality human-created artifacts are distinguished by cohesion and semantic richness that can be dif-ﬁcult for computational systems to emulate effectively. Certain classes of artifacts feature relationships between their constituent elements (e.g. words in a story) that naturally form a hierarchical structure that underpins the artifact’s meaning. We formalize a tractable method for programmatically extracting such artifacts’ structures framed in hierarchical Bayesian program learning (HBPL) and present HI-EROS, a creative system that uses linguistic and semantic structures—collectively called formats —extracted from human-written six-word stories to guide the creation of novel stories. We describe how HIEROS infers formats from exemplar stories, how those formats inform its selection of words when writing novel stories, and how the system evaluates its stories to search for high-quality output. We present and evaluate stories HIEROS has written and discuss how our format model enhances the system’s creativity.
Emotive Music Composition from Visual Sources of Inspiration

Cross-Domain creative systems can learn how to creatively map and/or transform semantic concepts from remote domains to their own. This project sets out to explore how a computational system relies on what it has learned from analyzing images to produce creative work. Speciﬁcally, the system presented here extracts semantic content and themes from an input image and uses this knowledge to creatively generate emotionally-appropriate musical artifacts to accompany the image. An independent experiment was conducted to provide a performance assessment and direct future work.
Creative Constellation Generation: A System Description

The discovery of new patterns in the sky and the attri-bution of names to those patterns have thus far been an exclusively human act. We believe that this makes constellation generation a great candidate for a computationally creative system. Artistically speaking, the night sky can be viewed as an inﬁnite canvas and our system leverages that fact to produce novel constellation generations without prior knowledge of existing constellations. Given an image of stars, our system discovers a pattern, identiﬁes an object in the pattern, and assigns it an intentional and creative name based on the object that the pattern resembles. We argue that our system exhibits creativity by emulating the same process by which a human would discover a new constellation. We describe how the system works, analyze the reception that its generations have received, and discuss the implications and future work that could be done to improve the system.
Authenticated Data Structures As Functors

Merkle trees are ubiquitous in blockchains and other distributed ledger technologies (DLTs). They guarantee that the involved systems are referring to the same binary tree, even if each of them knows only the cryptographic hash of the root. Inclusion proofs allow knowledgeable systems to share subtrees with other systems and the latter can verify the subtrees’ authenticity. Often, blockchains and DLTs use data structures more complicated than binary trees; authenticated data structures generalize Merkle trees to such structures. We show how to formally deﬁne and reason about authenticated data structures, their inclusion proofs, and operations thereon as datatypes in Isabelle/HOL. The construction lives in the symbolic model, i.e., we assume that no hash collisions occur. Our approach is modular and allows us to construct complicated trees from reusable building blocks, which we call Merkle functors. Merkle functors include sums, products, and function spaces and are closed under composition and least ﬁxpoints. As a practical application, we model the hierarchical transactions of Canton, a practical interoperability protocol for distributed ledgers, as authenticated data structures. This is a ﬁrst step towards formalizing the Canton protocol and verifying its integrity and security guarantees.
Interferometric Investigation of the Thermoacoustics in a Swirl Stabilized Methane Flame

In this work, an atmospheric model combustion chamber was characterized employing Laser Vibrometry, chemiluminescence and Particle Image Velocimetry. The test object was a variable geometry burner enclosed with a liner, with the flame optically accessible through four fused silica windows. In this burner with adjustable flame conditions the cavity of the atmospheric model combustion chamber was excited at a frequency around 200Hz. Resonant and non-resonant flame conditions were investigated and compared by laser vibrometer interferometry, schlieren visualization and OH*/CH* chemiluminescence. Additionally, the velocity field was recorded with Particle Image Velocimetry, while the aerodynamics of the burner plenum was analyzed with Computational Fluid Dynamics.Copyright © 2015 by ASME
Meta-level Evaluation and Transformational Creativity; An analysis of MEXICA

We present a proposal for meta-level evaluation in creative systems. In this paper, the Mexica creative sys-tem is the subject of our analysis. To achieve meta-level evaluation, we have constructed a program that interacts with Mexica’s inputs and its outputs. The meta-level system evaluates the results and makes decisions based on them. For decision-making it includes heuristics that are deﬁned in terms of Mexica’s results; these are rules particular to the object-level system. We also include other general metrics that apply in any other creative system. For example, Ritchie’s criteria for evaluating creative systems are included in this proposal.
Emojinating Co-Creativity: Integrating Self-Evaluation and Context-Adaptation

Co-creative systems are useful in fostering creativity, often leading to unexpected results. Despite this, the relation between user and system is complex. The level of autonomy given to the system directly inﬂuences its potential for creative behaviour and degree of contribution to the cooperation with the user. In this paper, we present our efforts to instil a more creative behaviour to an existing visual blending system – Emojinating. In order to do so, we integrate two functionalities: self-evaluation and context-adaptation.
ERwEM: Events Represented with Emotive Music Using Topic-Filtered Tweets

We present ERwEM (Events Represented with Emotive Music), a system that generates cross-domain artefacts between text and music by drawing from emotional sen-timents. Our system uses keywords extracted from a news article to ﬁlter live-stream tweets containing these keywords and analyze the tweet collection’s emotion data. It then uses the emotion sentiment (a scale from positive to negative) to inﬂuence the mood of the music it writes using an adapted bi-axial long short term memory (BALSTM) model. From this, our system determines which mode the generated song will be trans-posed to. We use the FACE model to characterize the creative aspects of our system as a collection of generative acts. We provide examples of the artefacts our sys-tem generates as well as the response we received from external sources who reviewed the generated songs.
From Computational Creativity to Creative Problem Solving Agents

Creative problem solving (CPS) is a skill that can greatly improve resourcefulness and adaptability of existing arti-ﬁcial intelligence (AI) systems. In this paper, we discuss how CPS leverages theoretical aspects from Computational Creativity (CC) and planning in AI. We present a deﬁnition of CPS, and discuss how CPS is achieved using aspects of CC and problem solving in AI.
A Blockchain-based Trustworthy Certification Process for Composite Services

Lack of trustworthiness is a major limit of microservice-based systems, where service composition is mainly driven by functional requirements. In this paper, we propose an approach where composite service certification meets blockchain, to support continuous and trustworthy verification of non-functional requirements. A certification process for composite services is then introduced at the basis of an audit process aiming to support certificates with stable properties. Trustworthiness is built on the blockchain, used as a platform for coordinating collaboration among involved parties such as service orchestrators, certification authorities, and auditors.
Multi-objective Search for Model-based Testing

This paper presents a search-based approach relying on multi-objective reinforcement learning and optimization for test case generation in model-based software testing. Our approach considers test case generation as an exploration versus exploitation dilemma, and we address this dilemma by implementing a particular strategy of multi-objective multi-armed bandits with multiple rewards. After optimizing our strategy using the jMetal multi-objective optimization framework, the resulting parameter setting is then used by an extended version of the Modbat tool for model-based testing. We experimentally evaluate our search-based approach on a collection of examples, such as the ZooKeeper distributed service and PostgreSQL database system, by comparing it to the use of random search for test case generation. Our results show that test cases generated using our search-based approach can obtain more predictable and better state/transition coverage, find failures earlier, and provide improved path coverage.
Reconfigurable Smart In-Memory Computing Platform Supporting Logic and Binarized Neural Networks for Low-Power Edge Devices

Edge computing has been shown to be a promising solution that could relax the burden imposed onto the network infrastructure by the increasing amount of data produced by smart devices. However, reconfigurable ultra-low power computing architectures are needed. RRAM devices together with the material implication logic (IMPLY) are a promising solution for the development of low-power reconfigurable logic-in-memory (LiM) hardware. Nevertheless, traditional approaches suffer from several issues introduced by the circuit topology and device non-idealities. Recently, SIMPLY, a smart LiM architecture based on the IMPLY, has been proposed and shown to solve the common issues of traditional architectures. Here, we use a physics-based RRAM compact model calibrated on three RRAM technologies to further analyze the performance of SIMPLY in typical operating conditions, when the repeated execution of logic operation on the same group of devices is considered. The results show that, compared to the conventional IMPLY architecture, SIMPLY spares more than 40% of the high voltage pulses on average even when complex operations are considered (e.g., the 1-bit half adder). We also show how SIMPLY can implement the set of operations required for the implementation of Binarized Neural Networks (BNN) and benchmark its performance against other memristor-based BNN in-memory accelerator from the literature. The results suggest that our approach is more than two orders of magnitude efficient compared to the state of the art reconfigurable in-memory computing approach and could potentially reach the performance of specialized BNN analog hardware accelerators with appropriate device-circuit co-design strategies.
A Sentiment-based Hotel Review Summarization using LSTM Neural Networks

Hotel customers express reviews for every accommodation service provided and/or for the accommodation as a whole. On the other hand, reviews are particularly interested for the tourism industry in order to extract customers’ opinions and aspects, which will assist them to improve their provided services. The reviews can be thoroughly found in the Web and their quantity can be considered as big data in Natural Language Processing. In this paper, we have designed an architecture and have implemented a system that initially utilizes some preprocessing techniques, as classic Natural Language Processing approaches, namely TF-IDF bag of words and word embeddings, are employed. These approaches can be further used as the input of various classifiers and Long Short Term Memory Neural Networks. The accuracy of these classifiers is evaluated on a dataset consisting of several reviews for the well known evaluation metrics, namely Precision, Recall and Fl-Measure.
A Neural Network approach for Non-parametric Performance Assessment

Data Envelopment Analysis (DEA) is the most popular non-parametric method for the efficiency assessment of homogeneous units. In this paper, we develop a novel approach, which integrates DEA with Artificial Neural Networks (ANNs) to accelerate the evaluation process and reduce the computational burden. We employ ANNs to estimate the efficiency scores of the milestone DEA models. The relative nature of DEA is considered in our approach by assuring that the DMUs used for training the ANNs are first evaluated against the efficient set. The ANNs employed in our approach estimate accurately the DEA efficiency scores. We validate our approach by conducting a series of experiments based on different data generation processes and number of inputs and outputs. Also, these estimated efficiency scores satisfy the properties of the fundamental DEA models. Thus, our approach can be employed for large scale assessments where the traditional DEA methods are rendered impractical.