Pitfalls of local explainability in complex black-box models

Post hoc models are becoming popular as additional tools to evaluate the results of black-box models and to provide explanations of the predictions they give. In this paper the main concerns that Local Induced models raise in the pointwise explanation of heavily overparametrized black-box models are discussed in depth, highlighting some vulnerabilities, some underrated issues and giving some warnings on the potentially negative effect on user trust of this explainability framewor

Adaptive quick reduct for feature drift detection

Data streams are ubiquitous and related to the proliferation of low-cost mobile devices, sensors, wireless networks and the Internet of Things. While it is well known that complex phenomena are not stationary and exhibit a concept drift when observed for a sufficiently long time, relatively few studies have addressed the related problem of feature drift. In this paper, a variation of the QuickReduct algorithm suitable to process data streams is proposed and tested: it builds an evolving reduct that dynamically selects the relevant features in the stream, removing the redundant ones and adding the newly relevant ones as soon as they become such. Tests on five publicly available datasets with an artificially injected drift have confirmed the effectiveness of the proposed method

A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

Protein Gaussian Image (PGI): A protein structural representation based on the spatial attitude of secondary structure

A well-known shape representation usually applied for 3D object recognition is the Extended Gaussian Image (EGI) which maps the histogram of the orientations of the object surface on the unitary sphere. We propose to adopt an analogous “abstract” data-structure named Protein Gaussian Image (PNM) for representing the orientation of the protein secondary structures (e.g. helices or strands) which combines the characteristics of the EGI and the ones of needle maps. The “concrete” data structures is the same as for the EGI, with a hierarchy that starting with a discretization corresponding to the 20 orientations of the icosahedron facets, it is iteratively refined with a factor 4 at each new level (80, 320, 1280, . . . ) up to the maximum precision required. However, in this case to each orientation does not correspond the area of the patches having that orientation but the features of the protein secondary structures having that direction. Among the features we may include the versus (origin versus surface or vice versa), the length of the structure (e.g. the number of amino acids), biochemical properties, and even the sequence of the amino acids (stored as a list). We consider this representation very effective for a preliminary screening when looking in a protein data base for retrieval of a given structural block, or a domain, or even an entire protein. In fact, on this structure it is possible to identify the presence of a given motif, or also sheets (note that parallel or anti-parallel β-sheets are characterized by common or opposite directions of ladders). Herewith some known proteins are described with common typical motifs easily marked in the PGI

EXPLOITING HIGHER ORDER UNCERTAINTY IN IMAGE ANALYSIS

Soft computing is a group of methodologies that works synergistically to provide flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. Soft computing methodologies (involving fuzzy sets, neural networks, genetic algorithms, and rough sets) have been successfully employed in various image processing tasks including image segmentation, enhancement and classification, both individually or in combination with other soft computing techniques. The reason of such success has its motivation in the fact that soft computing techniques provide a powerful tools to describe uncertainty, naturally embedded in images, which can be exploited in various image processing tasks. The main contribution of this thesis is to present tools for handling uncertainty by means of a rough-fuzzy framework for exploiting feature level uncertainty. The first contribution is the definition of a general framework based on the hybridization of rough and fuzzy sets, along with a new operator called RF-product, as an effective solution to some problems in image analysis. The second and third contributions are devoted to prove the effectiveness of the proposed framework, by presenting a compression method based on vector quantization and its compression capabilities and an HSV color image segmentation technique

Red mud-blast furnace slag-based alkali-activated materials

The aluminum Bayer production process is widespread all over the world. One of the waste products of the Bayer process is a basic aluminosilicate bauxite residue called red mud. The aluminosilicate nature of red mud makes it suitable as a precursor for alkali-activated materials. In this work, red mud was mixed with different percentages of blast furnace slag and then activated by sodium silicate solution at different SiO2/Na2O ratios. Obtained samples were characterized by chemical–physical analyses and compressive strength determination. Very high values of compressive strength, up to 50 MPa, even for high percentage of red mud in the raw mixture (70 wt.% of RM in powder mixture), were obtained. In particular, the higher compressive strength was measured for cubic samples containing 50 wt.% of RM, which showed a value above 70 MPa. The obtained mixtures were characterized by no or scarce environmental impact and could be used in the construction industry as an alternative to cementitious and ceramic materials

Fibre-reinforced geopolymer concretes for sensible heat thermal energy storage: Simulations and environmental impact

Power plants based on solar energy are spreading to accomplish the incoming green energy transition. Besides, affordable high-temperature sensible heat thermal energy storage (SHTES) is required. In this work, the temperature distribution and thermal performance of novel solid media for SHTES are investigated by finite element method (FEM) modelling. A geopolymer, with/without fibre reinforcement, is simulated during a transient charging/discharging cycle. A life cycle assessment (LCA) analysis is also carried out to investigate the environmental impact and sustainability of the proposed materials, analysing the embodied energy, the transport, and the production process. A Multi-Criteria Decision Making (MCDM) with the Analytical Hierarchy Process (AHP) approach, taking into account thermal/environmental performance, is used to select the most suitable material. The results show that the localized reinforcement with fibres increases thermal storage performance, depending on the type of fibre, creating curvatures in the temperature profile and accelerating the charge/discharge. High-strength, high-conductivity carbon fibres performed well, and the simulation approach can be applied to any fibre arrangement/material. On the con-trary, the benefit of the fibres is not straightforward according to the three different scenarios developed for the LCA and MCDM analyses, due to the high impact of the fibre production processes. More investigations are needed to balance and optimize the coupling of the fibre material and the solid medium to obtain high thermal performance and low impacts

Alkali-Activated Red Mud and Construction and Demolition Waste-Based Components: Characterization and Environmental Assessment

The aluminum Bayer production process is the most diffused process in the world, but it creates a high amount of basic waste material known as red mud (RM). The use of RM as a precursor of alkali-activated materials is one of the best opportunities for both the ecosystem and the economy. In the present work, mortar samples were obtained by alkali activation of RM with various percentages of blast-furnace slag (BFS) and inert construction and demolition sands. This process creates samples that have a low environmental impact and that can be used as an alternative in the construction industry to cement materials or ceramic ones. The development of these new materials could also represent a way to reduce the CO2 emissions linked to cement and ceramic brick production. In the present study, cubic 40 mm samples reported very interesting values in compressive strength, with a maximum of about 70 MPa for low environmental impact mortars. With such a material, it is possible to create solid bricks for structural use and concrete tiles for road paving or use it for other purposes. Mortar specimens were prepared and characterized, and an LCA analysis with a “cradle-to-gate” approach was carried out for a comparison of the environmental impact of the studied mortars with other materials currently marketed

Zeolite-based monoliths for water softening by ion exchange/precipitation process

In this work, the design of a monolithic softener obtained by geopolymer gel conversion is proposed. The softener used consists in a geopolymeric macroporous matrix functionalized by the co-crystallization of zeolite A and X in mixture. The dual nature of the proposed material promotes a softening process based on the synergistic effect of cation exchange and alkaline precipitation. A softening capacity of 90% and 54% for Ca2+ and Mg2+ respectively was attained in 24 h. In fact, the softener reported a Cation Exchange Capacity (CEC) value of 4.43 meq g−1. Technical features such as density, porosity and mechanical resistance were also measured. The use of this monolithic softener can improve performance and sustainability of hardness removal from tap water, reducing the production of sludge and adding the possibility to partially regenerate or reuse it