Miracle-dz/UniFAR-FTUs huggingface dataset:
Operational decomposition for large scale multi-objective optimization problems

Most multi-objective evolutionary algorithms (MOEAs) of the state of the art treat the decision variables of a multi-objective optimization problem (MOP) as a whole. However, when dealing with MOPs with a large number of decision variables (more than 100) their efficacy decreases as the number of decision variables of the MOP increases. Problem decomposition, in terms of decision variables, has been found to be extremely efficient and effective for solving large scale optimization problems. In this work, we study the effect of what we call "operational decomposition", which is a novel framework based on coevolutionary concepts to apply MOEAs's crossover operator without adding any extra cost. We investigate the improvements that NSGA-III can achieve when combined with operational decomposition. This new scheme is capable of improving efficiency of a MOEA when dealing with large scale MOPs having from 200 up to 1200 decision variables.
A bi-level hybrid PSO: MIP solver approach to define dynamic tariffs and estimate bounds for an electricity retailer profit

1With the implementation of dynamic tariffs, the electricity retailer may define distinct energy prices along the day. These tariff schemes encourage consumers to adopt different patterns of consumption with potential savings and enable the retailer to manage the interplay between wholesale and retail prices. In this work, the interaction between retailer and consumers is hierarchically modelled as a bi-level (BL) programming problem. However, if the lower level (LL) problem, which deals with the optimal operation of the consumer's appliances, is difficult to solve, it may not be possible to obtain its optimal solution, and therefore the solution to the BL problem is not feasible. Considering a computation budget to solve LL problems, a hybrid particle swarm optimization (PSO) - mixed-integer programming (MLP) approach is proposed to estimate good quality bounds for the upper level (UL) objective function. This work is based on [4].
Facial privacy protection in airborne recreational videography

Cameras mounted on Micro Aerial Vehicles (MAVs) are increasingly used for recreational photography and videography. However, aerial photographs and videographs of public places often contain faces of bystanders thus leading to a perceived or actual violation of privacy. To address this issue, this thesis presents a novel privacy filter that adaptively blurs sensitive image regions and is robust against different privacy attacks. In particular, the thesis aims to impede face recognition from airborne cameras and explores the design space to determine when a face in an airborne image is inherently protected, that is when an individual is not recognisable. When individuals are recognisable by facial recognition algorithms, an adaptive filtering mechanism is proposed to lower the face resolution in order to preserve privacy while ensuring a minimum reduction of the fidelity of the image. Moreover, the filter’s parameters are pseudo-randomly changed to make the applied protection robust against different privacy attacks. In case of videography, the filter is updated with a motion-dependent temporal smoothing to minimise flicker introduced by the pseudo-random switching of the filter’s parameters, without compromising on its robustness against different privacy attacks. To evaluate the efficiency of the proposed filter, the thesis uses a state-of-the-art face recognition algorithm and synthetically generated face data with 3D geometric image transformations that mimic faces captured from an MAV at different heights and pitch angles. For the videography scenario, a small video face data set is first captured and then the proposed filter is evaluated against different privacy attacks and the quality of the resulting video using both objective measures and a subjective test.
Multiple Instance Learning Algorithm for Medical Image Classification

After an overview on most relevant methods for image classification, we focus on a recently proposed Multiple Instance Learning (MIL) approach, suitable for image processing applications and based on a mixed integer nonlinear optimization problem. In particular, the algorithm has been preliminarily applied to a set of color images, with the aim to identify images containing some specific color pattern, and successively to a medical dataset, containing photos of melanoma and common nevi. Since the results appear promising, this technique could be at the basis of computer vision systems that act as a filter mechanism to support physicians in detecting melanomas cancer.
Identity-based Deniable Authenticated Encryption for E-voting Systems

Deniable authentication (DA) is a protocol in which a receiver can generate an authenticator that is probabilistically indistinguishable from a sender. DA can be applied in many scenarios that require user privacy protection. To enhance the security of DA, in this paper, we construct a new deniable authenticated encryption (DAE) scheme that realizes deniable authentication and confidentiality in a logical single step. Compared with existing approaches, our approach provides proof of security and is efficient in terms of performance analysis. Our scheme is in an identity-based environment; thus, it avoids the public key certiﬁcate -based public key infrastructure (PKI). Moreover, we provide an example that shows that our protocol is applicable for e-voting systems.
Parameter-less population pyramid with automatic feedback

Parameter-less Population Pyramid (P3) is a parameter-less optimization method that employs the Dependency Structure Matrix (DSM) to discover dependencies between genes. The results published so far show that P3 effectiveness is low when bimodal trap problems are in use. Therefore, Parameter-less Population Pyramid with Feedback (fP3) was proposed. fP3 extends P3 by the feedback operation which improves P3's effectiveness when bimodal trap problems are used. However, fP3 is no longer a parameter-less method. In this paper, we propose an adaptive strategy that sets the feedback probability in each method's iteration. Therefore, newly proposed Parameter-less Population Pyramid with Automatic Feedback (afP3) is parameter-less and preserves fP3 advantages.
Skill emergence and transfer in multi-agent environments

A central problem in training artificial agents to perform complex skills is specifying appropriate cost functions whose optimization will lead to the desired behavior. Specifying detailed cost functions is laborious and often inefficient. The training of agents in competitive and cooperative multi-agent environments provides an avenue to circumvent these limitations: By competition and cooperation agents provide to each other a natural curriculum that can lead to the emergence of complicated skills, even if the rewards of the multi-agent game are simple [1]. Here we explore the emergence of complex strategies and skills in a simple hide and seek game simulated in a 3-D physics environment. We show that training using deep reinforcement learning (RL) leads to the emergence of several rounds of strategies, counter-strategies, and skills composed of several sequential behaviors. Our results suggest that training multiple agents in a sufficiently complex environment using large scale RL can lead to open-ended development of behavior. We furthermore show that emergent skills can be extracted and re-used in distinct environments. This skill transfer is both a useful evaluation metric for multi-agent emergence and suggests that multi-agent pre-training might be a useful strategy to generate targeted skills.
Notes on Programming Standard ML of New Jersey (version 110.0.6)

Preface The impetus behind these notes was the desire to provide a cohesive description of Standard ML of New Jersey, an interactive compiler and environment for Standard ML. The goal is to end up with a complete user guide to the system, inclduing the libraries, the tools and the extensions, as well as a tutorial on how to write " real " applications, centered around the use of the module system and the compilation manager. Other reasons include the desire to provide hands-on examples of how to use some maybe poorly understood features or features with an interface different from what one may be used to. Examples of such features include sockets, the input and output subsystem, the readers approach to text scanning and the use of continuations to handle non-local control-flow. All in all, this would be a repository for snippets of SML and SML/NJ programming lore. These notes are not a tutorial introduction to Standard ML. There exists excellent introductory material, available both commercially or freely over the Internet. These notes complement this literature by focusing on the Standard ML of New Jersey environment. The first part of these notes does given an overview of the language, but a quick one and without highlighting some of the subtleties of the language. Better writers than I have written better introductory material and I urge you to read those first. References are given in the chapter notes of Chapter 1. I go in somewhat more details when describing the Basis Library, since some of the underlying ideas are fundamental to the overall programming philosophy. Unfortunately , that chapter is long, boring and reads more like a reference manual than a tutorial. Throughness and precision at odds with readability. With luck, enough sample code and tutorial material is interspersed to lighten the mood. In the course of the notes, I take the opportunity to describe more advanced topics typically not covered in introductory material, such as sockets programming, signals handling, continuations, concurrency. Some of these subjects are discussed in advanced programming language courses, which not every one has taken or plan to take. Some of these subjects are hardly discussed and one needs to rummage technical papers or source code. These notes are quite obviously a work in progress. As such, any comments or iii iv PREFACE suggestions will be more than welcome. This version includes chapters 1 through 7. Planned …
Transitive Signature Schemes for Undirected Graphs from Lattices

In a transitive signature scheme, a signer wants to authenticate edges in a dynamically growing and transitively closed graph. Using transitive signature schemes it is possible to authenticate an edge ( , ) i k , if the signer has already authenticated two edges ( , ) i j and ( , ) j k . That is, it is possible to make a signature on ( , ) i k using two signatures on ( , ) i j and ( , ) j k . We propose the first transitive signature schemes for undirected graphs from lattices. Our first scheme is provably secure in the random oracle model and our second scheme is provably secure in the standard model.
Computing rational border curves of melanoma and other skin lesions from medical images with bat algorithm

Border detection of melanoma and other skin lesions from images is an important step in the medical image processing pipeline. Although this task is typically carried out manually by the dermatologists, some recent papers have applied evolutionary computation techniques to automate this process. However, these works are only focused on the polynomial case, ignoring the more powerful (but also more difficult) case of rational curves. In this paper, we address this problem with rational Bézier curves by applying the bat algorithm, a popular bio-inspired swarm intelligence technique for optimization. Experimental results on two examples of medical images of melanomas show that this method is promising, as it outperforms the polynomial approach and can be applied to medical images without further pre/post-processing.
AI + Art = Human

Over the past few years, specialised online and offline press blossomed with articles about art made "with" Artificial Intelligence (AI) but the narrative is rapidly changing. In fact, in October 2018, the auction house Christie's sold an art piece allegedly made "by" an AI. We draw from philosophy of art and science arguing that AI as a technical object is always intertwined with human nature despite its level of autonomy. However, the use of creative autonomous agents has cultural and social implications in the way we experience art as creators as well as audience. Therefore, we highlight the importance of an interdisciplinary dialogue by promoting a culture of transparency of the technology used, awareness of the meaning of technology in our society and the value of creativity in our lives.
On the non-convergence of differential evolution: some generalized adversarial conditions and a remedy

In this paper, we analyze the convergence behavior of Differential Evolution (DE) and theoretically prove that under certain adversarial conditions, the generic DE algorithm may not at all converge to the global optimum even on apparently simpler fitness landscapes. We characterize these function classes and initialization conditions theoretically and provide mathematical supports to the non-convergence behavior of DE. To overcome these adversarial conditions, we propose a slightly modified variant of DE called Differential Evolution with Noisy Mutation (DENM), which incorporates a noise term in the mutation step. We analytically show that DENM can converge to the global optima within a finite budget of function evaluations.
Evolved Cellular Automata for Edge Detection in Binary Images

One significant and complex image processing task that may be used as a step in various complex processes is the edge detection. The mechanism of detecting the edges is called edge detector. The scope of this paper is to propose an edge detector based on evolved Cellular Automata (CA), for binary images. It was proved in many cases that CA may be successfully applied in various image processing tasks because they have a number of advantages over the traditional methods of computations: simplicity of implementation, the complexity of behaviour, parallelisation, extensibility, scalability, robustness. In the scope of this paper, two Evolutionary Algorithms are used to evolve the CA’ rule to detect edge points in binary images.
Benchmarking MO-CMA-ES and COMO-CMA-ES on the bi-objective bbob-biobj testbed

In this paper, we propose a comparative benchmark of MO-CMA-ES, COMO-CMA-ES (recently introduced in [12]) and NSGA-II, using the COCO framework for performance assessment and the Bi-objective test suite bbob-biobj. For a fixed number of points p, COMO-CMA-ES approximates an optimal p-distribution of the Hypervolume Indicator. While not designed to perform on archive-based assessment, i.e. with respect to all points evaluated so far by the algorithm, COMO-CMA-ES behaves well on the COCO platform. The experiments are done in a true Black-Blox spirit by using a minimal setting relative to the information shared by the 55 problems of the bbob-biobj Testbed.
Binary 100-digit challenge using IEEE-754 coded numerical optimization scenarios (100b-digit) and V-shape binary distance-based success history differential evolution (DISHv)

This paper proposes a new discrete optimization benchmark 100b-Digit, a binary discretized version for the 100-Digit Challenge. The continuous version 100-Digit Challenge utilizing continuous input parameters for a fitness function was suggested for the competitions at 2019 GECCO and 2019 CEC, while this paper proposes an extension, a discrete version of the 100-Digit Challenge, discretizing using an IEEE-754 double precision floating-point representation for the search space variables. Furthermore, the paper also presents a viable recent state-of-the-art algorithm discretization to be applicable on the new 100b-Digit benchmark. V-shape transfer function is applied for binarization of the variables from the Distance-based Success History Differential Evolution (DISH) to create the new DISHv algorithm, which is then run on the 100b-Digit benchmark. The preliminary results for the DISHv algorithm are then reported for a basic value-to-reach termination criterion over 100b-Digit functions. This combination of 100b-Digit benchmark and DISHv algorithm demonstrates how to bridge a gap between recent continuous optimizers and different discrete benchmarks, and vice-versa.
Solving the parameterless firefighter problem using multiobjective evolutionary algorithms

The Firefighter Problem (FFP) is a graph-based optimization problem that is an abstraction of real-life problems such as epidemics control, economic crises prevention, etc. In the FFP spreading of fire is simulated on a graph in discrete time steps. In the original formulation of the problem a fixed number of graph nodes Nf can be defended in each time step. In this paper the problem is reformulated, and three different solution representations are studied. In one of the representations (N+P), the Nf parameter is a decision variable and in the other two (P using permutations and T using integer vectors) it is determined when the solution is decoded. Because higher Nf values mean more resources used for defense it is desirable to minimize this value, but on the other hand we want to minimize the number of graph nodes consumed by fire. Therefore the Parameterless FFP is tackled using two well-known multiobjective evolutionary algorithms: the MOEA/D and the NSGA-II as a multiobjective optimization problem with two and three objectives. The results presented in the paper show that for the Parameterless FFP the best solution representation is N+P.
Improving the algorithmic efficiency and performance of channel-based evolutionary algorithms

Concurrent evolutionary algorithms use threads that communicate via messages. Parametrizing the work in every thread and the way they communicate results is a major challenge in its design. In this paper we work with concurrent evolutionary algorithms implemented in Perl 6, and explore different options of single-thread evolution parametrization, communication and mixing of results, showing that scalability is achieved in a multi-core environment.
A Tour of Gallifrey, a Language for Geodistributed Programming

Programming eﬃcient distributed, concurrent systems requires new abstractions that go beyond traditional sequential programming. But programmers already have trouble getting sequential code right, so simplicity is essential. The core problem is that low-latency, high-availability access to data requires replication of mutable state. Keeping replicas fully consistent is expensive, so the question is how to expose asynchronously replicated objects to programmers in a way that allows them to reason simply about their code. We propose an answer to this question in our ongoing work designing a new language, Gallifrey, which provides orthogonal replication through restrictions with merge strategies , contingencies for conﬂicts arising from concurrency, and branches , a novel concurrency control construct inspired by version control, to contain provisional behavior. 2012 ACM Subject Classiﬁcation Software and its engineering → Cooperating communicating processes; Software and its engineering → Massively parallel systems; Software and its engineering → Distributed programming languages
Greg, ML: Automatic Diagnostic Suggestions. Humanity is Overrated. Or Not

Recently machine-learning techniques have been applied in a variety of fields. One of the most promising and challenging is handling medical records. In this paper we present Greg, ML, a machine-learning tool for generating automatic diagnostic suggestions based on patient profiles. At the core of our system there are two machine learning classifiers: a natural-language module that handles reports of instrumental exams, and a profile classifier that outputs diagnostic suggestions to the doctor. After discussing the architecture we present some experimental results based on the working prototype we have developed. Finally, we examine challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned developing the tool. In this respect, despite the ironic title of this paper, we underline that Greg should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment.
The Art of Mathematical Biology: a Foreword for the Launch of BIOMATH

We cannot debate that the advances made over the past 30 years in molecular biology, biochemistry and cell biology have dramatically changed biology and the biomedical sciences. Academic institutions and funding agencies increasingly recognize the interdisciplinary nature of biology and medicine, including the important role mathematical, statistical and computational modeling play in the analysis and interpretation of data and information. By and large, biologists and biomedical scientists appear to have more appreciation for mathematical biology research because they are increasingly attracted to discover new biological principles and mechanisms in collaboration with mathematicians.
A Study on the Usage of Web 2.0 Applications by Basic Medical Science Students, in the Light of e-Learning Implementation

Web 2.0 represents the changing face of people’s activities on the Web, from a mere place to access information towards a much “cool” place to create / write / share / collaborate / network with their intellectual involvement. Web 2.0 is changing all aspects of academic life including practice and training of medicine. When American University of Antigua integrates to the e-learning Systems, there was a need to study the present student population before implementing the facilities, about their usage of Web 2.0 applications for learning and research purposes. This dissertation is based on a survey for analyzing the awareness of the students about Web 2.0 applications and their expectations during integration of e-learning technologies. Major finding are wikis, instant messaging, media sharing, social networking and VoIP show high usage by major group of students, where very less usage of podcasts, social book marking, blogs, feeds is found. More students mainly want wikis, forums to be integrated to in elearning system along with media/file sharing, streaming, chat rooms, blogs and book marking.
Optimized Renal Transporter Quantification by Using Aquaporin 1 and Aquaporin 2 as Anatomical Markers: Application in Characterizing the Ontogeny of Renal Transporters and Its Correlation with Hepatic Transporters in Paired Human Samples

Renal transporters, which are primarily located in the proximal tubules, play an important role in secretion and nephrotoxicity of drugs. The goal of this study was to characterize the age-dependent protein abundance of human renal transporters. A total of 43 human kidneys, 26 of which were paired with livers from the same donors, were obtained and classified into three age groups: children (< 12 years), adolescents (12 to < 18 years), and adults (> 18 years). Protein abundance of kidney-specific anatomical markers, aquaporins 1 and 2 (markers of proximal and distal/collecting tubules, respectively), and 17 transporters was quantified by LC-MS/MS proteomics. Six out of 43 kidney samples were identified as outliers (Grubbs’ test) that were significantly different from the others with relatively higher aquaporin 2 to aquaporin 1 ratio, indicating that these cortex samples were likely contaminated by medulla (representing distal/collecting tubules). No significant age-related changes (age > 1 year) were observed for renal transporter abundance, albeit OCT2 abundance was modestly higher (< 50%) in adolescents than that in adults. Higher protein-protein correlation between transporters was observed in the kidney but abundance of transporters between tissues was not correlated. The use of aquaporins 1 and 2 provides a method for identifying kidney cortex with significant contamination from medulla containing distal and collecting tubules. The abundance and protein-protein correlation data can be used in physiologically based pharmacokinetic (PBPK) modeling and simulation of renal drug disposition and clearance in pediatric populations.