User-defined Constraints Research Articles

SmartSPEC: A framework to generate customizable, semantics-based smart space datasets

This paper presents SmartSPEC, an approach to generate customizable synthetic smart space datasets using sensorized spaces in which people and events are embedded. Smart space datasets are critical to design, deploy and evaluate systems and applications under issues of heterogeneity, scalability and robustness, leading to cost-effective operation which improves the safety, comfort and convenience experienced by space occupants. However, many challenges exist in obtaining realistic smart space datasets for testing and validation, from a lack of fine-grained sensing to privacy/security concerns. SmartSPEC is a smart space simulator and data generator that leverages a semantic model augmented with user-defined constraints to represent important attributes, relationships, and external domain knowledge for a smart space. We employ machine learning (ML) approaches to extract relevant patterns from a sensorized space, which are used in an event-driven simulation strategy to generate realistic simulated data about the space (events, trajectories, sensor observation datasets, etc.). To evaluate the realism of the generated data, we develop a structured methodology and metrics to assess various aspects of smart space datasets, including trajectories of people and occupancy of spaces. Our experimental study looks at two real-world settings/datasets: an instrumented smart campus building and a city-wide GPS dataset. Our results show the realism of trajectories produced by SmartSPEC (1.4x to 4.4x more realistic than the best synthetic data baseline when compared to real-world data, depending on the scenario and configuration), as well as sensor data derived from such trajectories which adhere to the underlying semantics of the smart space as compared to synthetic sensor data baselines, even under hypothetical changes.

A Novel Dynamic Load Scheduling and Peak Shaving Control Scheme in Community Home Energy Management System Based Microgrids

Load scheduling and peak demand shaving are two critical aspects of the utility grid operation that help both the grid operators as well as end-users. This paper proposes a two-stage community home energy management system for microgrids. The first stage deals with the dynamic clustered community load scheduling scheme. Comparatively flatter power demand was attained using particle swarm optimization (PSO) incorporating user-defined constraints. The new arising or remaining peaks as a consequence of consumer constraints are catered to in the second stage. The second stage proposes a rule-based peak shaving management method for the photovoltaic (PV) systems that are connected to the grid and the battery energy storage systems. The proposed technique determines the dynamic demand and feed-in limits based on the estimations of the upcoming day’s load demand and PV power profiles. Also, the study presents an optimal rule-based management technique for peak shaving of utility grid power that sets the charge/discharge day ahead schedules of the battery. For peak energy minimization, PSO is used to calculate the optimal inputs needed for implementing the appropriate rule-based management strategy. MATLAB is used to test the proposed method for different PV power and load demand patterns, thus, achieving an average improvement of 8.5%.

HAMLET: A framework for Human-centered AutoML via Structured Argumentation

Machine Learning (ML) plays a crucial role in data analysis and data platforms (i.e., integrated sets of technologies that collectively meet end-to-end data needs). In the last decade, we have witnessed an exponential growth in both the complexity and the number of ML techniques; leveraging such techniques to solve real-case problems has become difficult for Data Scientists. Automated Machine Learning (AutoML) tools have been devised to alleviate this task, but easily became as complex as the ML techniques themselves — with Data Scientists losing again control over the process. In this paper, we design and extend HAMLET (Human-centered AutoMl via Logic and argumEnTation), a framework that helps Data Scientists to redeem their centrality. HAMLET enables Data Scientists to express ML constraints in a uniformed human- and machine-readable medium. User-defined constraints are interpreted to drive the exploration of ML pipelines (i.e., Data Pre-processing transformations shape the data so that the ML task can be performed at its best). AutoML retrieves the most performing pipeline instance, and finally, new constraints are learned and integrated through Logic and Argumentation. By doing so, HAMLET not only allows an easy exploitation of the knowledge acquired at each iteration, but also enables its continuous revision via the AutoML tool and the collaboration of both Data Scientists and domain experts.

Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

One essential topic of mining sequential patterns in the data stream is to optimize the time-space computations. However, more importantly, it should pay more attention to the significance of mining results as a large portion of them just response to the user-defined constraints purely by accident and they may have no statistical significance. In this paper, we propose FSSPDS, an efficient two-phase algorithm to discover the significant sequential patterns (SSPs) in the data stream with typical sliding windows, which has never been considered in existing problems. First, for generating SSPs candidates with high-quality, FSSPDS takes testable support and pattern length constraints into account and insignificant patterns were removed timely by a pattern-growth method. In the second phase, appropriate permutation testing is used to test the significance of the SSPs candidates. Exact permutation p values are obtained in a novel combination way based on unconditional Barnard’s test statistic which better reflects the process of data generations and collections. Experimental evaluations show that FSSPDS allows the discovery of SSPs in the data stream and rivals the state-of-the-art approaches efficiently under the control of family-wise error rate (FWER), especially for time efficiency, which was approximately an order of magnitude higher.

Control of parallelized bioreactors I: dynamic scheduling software for efficient bioprocess management in high-throughput systems

The shift towards high-throughput technologies and automation in research and development in industrial biotechnology is highlighting the need for increased automation competence and specialized software solutions. Within bioprocess development, the trends towards miniaturization and parallelization of bioreactor systems rely on full automation and digital process control. Thus, mL-scale, parallel bioreactor systems require integration into liquid handling stations to perform a range of tasks stretching from substrate addition to automated sampling and sample analysis. To orchestrate these tasks, the authors propose a scheduling software to fully leverage the advantages of a state-of-the-art liquid handling station (LHS) and to enable improved process control and resource allocation. Fixed sequential order execution, the norm in LHS software, results in imperfect timing of essential operations like feeding or Ph control and execution intervals thereof, that are unknown a priori. However, the duration and control of, e.g., the feeding task and their frequency are of great importance for bioprocess control and the design of experiments. Hence, a software solution is presented that allows the orchestration of the respective operations through dynamic scheduling by external LHS control. With the proposed scheduling software, it is possible to define a dynamic process control strategy based on data-driven real-time prioritization and transparent, user-defined constraints. Drivers for a commercial 48 parallel bioreactor system and the related sensor equipment were developed using the SiLA 2 standard greatly simplifying the integration effort. Furthermore, this paper describes the experimental hardware and software setup required for the application use case presented in the second part.

DoG-HiT: A novel VLBI multiscale imaging approach

Context. Reconstructing images from very long baseline interferometry (VLBI) data with a sparse sampling of the Fourier domain (uv-coverage) constitutes an ill-posed deconvolution problem. It requires application of robust algorithms, maximizing the information extraction from all of the sampled spatial scales, and minimizing the influence of the unsampled scales on image quality. Aims. We develop a new multiscale wavelet deconvolution algorithm, DoG-HiT, for imaging sparsely sampled interferometric data, which combines the difference of Gaussian (DoG) wavelets and hard image thresholding (HiT). Based on DoG-HiT, we propose a multistep imaging pipeline for analysis of interferometric data. Methods. DoG-HiT applies the compressed sensing approach to imaging by employing a flexible DoG wavelet dictionary, which is designed to adapt smoothly to the uv-coverage. It uses closure properties as data fidelity terms only, initially, and performs nonconvex, nonsmooth optimization by an amplitude-conserving and total-flux-conserving, hard thresholding splitting. DoG-HiT calculates a multiresolution support as a side product. The final reconstruction is refined through self-calibration loops and imaging with amplitude and phase information applied for the multiresolution support only. Results. We demonstrate the stability of DoG-HiT, and benchmark its performance against image reconstructions made with the CLEAN and regularized maximum-likelihood (RML) methods using synthetic data. The comparison shows that DoG-HiT matches the super-resolution achieved by the RML reconstructions and surpasses the sensitivity to extended emission reached by CLEAN. Conclusions. The application of regularized maximum likelihood methods, outfitted with flexible multiscale wavelet dictionaries, to imaging of interferometric data, matches the performance of state-of-the art convex optimization imaging algorithms and requires fewer prior and user-defined constraints.

HADA: An automated tool for hardware dimensioning of AI applications

In recent years, the uptake of Artificial Intelligence (AI) in industry is increasing. For many AI techniques, like Deep Learning, optimization, planning, etc., computational and storage requirements are significant. The problem of determining what is the right hardware (HW on premise or on the cloud) architecture and its dimensioning for AI algorithms is still crucial. Searching for the optimal solution is often challenging, as it is not trivial to anticipate the behavior of an algorithm on diverse architectures. This is especially true if the AI application must respect quality-of-service constraints or budgets. In this scenario, having an automated decision support tool to match algorithms, user constraints and HW resources would be a great advantage for companies and practitioners working with AI applications.In this paper, we tackle this challenge with an approach that relies on the Empirical Model Learning paradigm, based on the integration of Machine Learning (ML) models into an optimization problem. The key idea is to integrate domain knowledge held by experts with data-driven models that learn the relationships between HW requirements and AI algorithm performances. In particular, the approach starts with benchmarking multiple AI algorithms on different HW resources, generating data used to train ML models; then, optimization is used to find the best HW configuration that respects user-defined constraints (e.g., budget, time, solution quality).In the experimental evaluation we validate our approach on a complex problem, namely online algorithms for energy systems, an area characterized by uncertainty and tight HW and real-time constraints. Results show the effectiveness of our approach and its flexibility: We can train the ML models only once and reuse them in the optimization model to tackle a variety of problems, determined by different data instances and user-defined constraints.

FluidHarmony: Defining an equal-tempered and hierarchical harmonic lexicon in the Fourier space

FluidHarmony is an algorithmic method for defining a hierarchical harmonic lexicon in equal temperaments. It utilizes an enharmonic weighted Fourier transform space to represent pitch class set (pcsets) relations. The method ranks pcsets based on user-defined constraints: the importance of interval classes (ICs) and a reference pcset. Evaluation of 5,184 Western musical pieces from the 16th to 20th centuries shows FluidHarmony captures 8% of the corpus's harmony in its top pcsets. This highlights the role of ICs and a reference pcset in regulating harmony in Western tonal music while enabling systematic approaches to define hierarchies and establish metrics beyond 12-TET.

A Topological Approach to Gait Generation for Biped Robots

This article describes a topological approach to generating families of open- and closed-loop walking gaits for underactuated 2-D and 3-D biped walkers subject to configuration inequality constraints, physical holonomic constraints (e.g., closed-loop linkages), and virtual holonomic constraints (user-defined constraints enforced through feedback control). Our method constructs implicitly defined manifolds of feasible periodic gaits within a state-time-control space that parameterizes the biped’s hybrid trajectories. Since equilibrium configurations of the biped often belong to such manifolds, we use equilibria as “templates” from which to grow the gait families. Equilibria are reliable seeds for the construction of gait families, eliminating the need for random, intuited, or bio-inspired initial guesses at feasible trajectories in an optimization framework. We demonstrate the approach on several 2-D and 3-D biped walkers.

Learning GUI Completions with User-defined Constraints

A key objective in the design of graphical user interfaces (GUIs) is to ensure consistency across screens of the same product. However, designing a compliant layout is time-consuming and can distract designers from creative thinking. This paper studies layout recommendation methods that fulfill such consistency requirements using machine learning. Given a desired element type and size, the methods suggest element placements following real-world GUI design processes. Consistency requirements are given implicitly through previous layouts from which patterns are to be learned, comparable to existing screens of a software product. We adopt two recently proposed methods for this task, a Graph Neural Network (GNN) and a Transformer model, and compare them with a custom approach based on sequence alignment and nearest neighbor search (kNN) . The methods were tested on handcrafted datasets with explicit layout patterns, as well as large-scale public datasets of diverse mobile design layouts. Our results show that our instance-based learning algorithm outperforms both neural network approaches. Ultimately, this work contributes to establishing smarter design tools for professional designers with explainable algorithms that increase their efficacy.

Formalising the R of Reduce in a Circular Economy Oriented Design Methodology for Pedestrian and Cycling Bridges

The construction industry consumes over 32% of the annually excavated natural resources worldwide. Additionally, it is responsible for 25% of the annually generated solid waste. To become a more sustainable industry, a circular economy is necessary: resources are kept in use as long as possible, aiming to reduce and recirculate natural resources. In this paper, the investigation focuses on pedestrian truss bridges of the types Warren and Howe. Many pedestrian bridges currently find themselves in their end-of-life phase and most commonly these bridges are demolished and rebuilt, thus needing a lot of new materials and energy. The aim is thus first and foremost to reduce the amount of necessary new materials. For this reason, a design tool will be created, using the software ‘Matlab’, in which truss bridges can be evaluated and compared in the conceptual design stage. The tool is based on the theory of morphological indicators: the volume indicator, displacement indicator, buckling indicator and first natural frequency indicator. These allow a designer to determine the most material efficient Warren or Howe truss bridge design with user-defined constraints concerning deflection, load frequency, buckling and overall dimension. Subsequently, the tool was tested and compared to calculations made in the finite element modelling software Diamonds. In total, 72 steel bridge structures were tested. From these it could be concluded that the manual calculations in Diamonds in general confirmed the results obtained with the automated design tool based on morphological indicators. As such, it allows a designer to converge more quickly towards the best performing structure, thus saving time, materials, and corresponding costs and energy.

Solid waste optimization life‐cycle framework in Python (SwolfPy)

This paper describes a novel open-source life-cycle optimization framework for solid waste and sustainable materials management applications named solid waste optimization life-cycle framework in Python (SwolfPy). The current version includes life-cycle models for landfills, mass burn waste-to-energy, gasification, centralized composting, home composting, anaerobic digestion, material recovery facilities, refuse-derived fuel facilities, material recycling, transfer stations, and single-family collection. Compared to existing frameworks, SwolfPy streamlines data input/output processes, improves model integration and modularity, provides a wide variety of data visualization and customization, speeds up uncertainty analysis and optimization, and has a user-friendly graphical user interface (GUI). SwolfPy's GUI allows users to define solid waste management networks and scenarios as well as perform comparative life cycle assessments (LCAs), contribution analyses, uncertainty analyses, and optimization. SwolfPy is implemented in Python using Pandas, NumPy, and SciPy for computational tasks, PySide2 for creating the GUI, and Brightway2 for storing life-cycle inventory data and performing the LCA calculations. SwolfPy is modular and flexible, which enables it to be easily coupled with other packages and to facilitate the addition of new processes, materials, environmental flows and impacts, and methodologies. SwolfPy uses sequential least-squares programming for constrained nonlinear optimization to find systems and strategies that minimize cost or environmental emissions and impacts while meeting user-defined constraints. An illustrative case study with 44 materials, 4 collection processes, and 6 treatment processes is presented, and SwolfPy performs 10,000 Monte Carlo iterations in 16 min and finds optimal solutions in 10–25 min on a Windows 10 machine with a CPU speed of 3.60 GHz and 8 logical processors. This article met the requirements for a Gold-Gold Badge. JIE data openness badge described at http://jie.click/badges.

Fractal construction of constrained code words for DNA storage systems

The use of complex biological molecules to solve computational problems is an emerging field at the interface between biology and computer science. There are two main categories in which biological molecules, especially DNA, are investigated as alternatives to silicon-based computer technologies. One is to use DNA as a storage medium, and the other is to use DNA for computing. Both strategies come with certain constraints. In the current study, we present a novel approach derived from chaos game representation for DNA to generate DNA code words that fulfill user-defined constraints, namely GC content, homopolymers, and undesired motifs, and thus, can be used to build codes for reliable DNA storage systems.

Understanding fracture mechanisms via validated virtual tests of encapsulation-based self-healing concrete beams

The fracture process in self-healing concrete with embedded brittle capsules entails challenges in terms of understanding how and when these capsules break to release the agent. This paper presents a combined experimental–numerical investigation in which a versatile three-dimensional simulation model is developed to investigate the fracture of this type of beams under three-point bending load. The model allows for correlating the overall strength with different damage events occurring in every constituent: the concrete, the capsules and the capsule-concrete interface. The constitutive concrete damage model uses a pressure-dependent failure initiation criterion followed by a bilinear softening law, whose parameters are validated by using a modified Nelder-Mead optimization algorithm enriched with user-defined constraints aimed at increasing the convergence ratio. The validated virtual model demonstrates that the ratio of capsule slenderness to concrete-capsule interface strength is the key parameter for an effective self-healing process as it gives full control to break the capsule at the right moment. The shorter the capsule is, the longer the breakage process can be. Additionally, it has been found that by increasing the length of the capsules can help to enhance the overall fracture energy of the beam even after fully broken.

Disaster and Risk Mitigation at The Toll Road Planning Stage

PT. Hutama Karya (Persero), according to Presidential Regulation No. 100 of 2014 and No. 117 of 2015, obtained an assignment to construct and operate 24 sections of Trans Sumatera Toll Road along 2,789 km, including Padang - Pekanbaru Toll Road, where almost all of the segments are located in fault areas and in areas with diverse rock formation. In terms of the number of fault locations, the toll road has a greater risk of earthquakes. Whereas in terms of varying rock formations, construction planning and improper structure determination will cause a highly cost-enhancing effect. In the planning stage, the selection of route is one of the mitigations to minimize the risks and impacts of the earthquake disaster. Toll road trajectories are designed optimally by considering the movement of the earth’s plates based on fault data on these locations and data on rock formations for the construction and structure plan of the Toll Road. Input data needed is Geological Secondary Data and Topographic Data containing information on fault areas and rock formations. Therefore, planning with Quantm Trimble software is the right solution. Determining the route with Quantm Trimble software is one of the effective and efficient methods. The main key in determining routes by Quantm Trimble is the software algorithm which can determine the route quickly by considering the main constraints such as avoiding fault areas, avoiding an area with certain rock formations also determining the construction methods on certain rock formation areas. Quantm Trimble software is able to generate several alternative routes based on user-defined constraints, including accommodating the automatic selected smoothing process according to the specified road geometry standard. The software greatly accommodates the determination of the plan by considering risk and disaster management, as well as being able to manage costs well by determining the construction method plan quickly and accurately.

PEFT-based Trade-off Schedule Plan for Execution IoT Applications in Cloud Environment

Internet of Things (IoT) consists of physical things or objects that are connected through a network of sensors, software, and electronics. Mainly, IoT devices are of restricted processing and storage capacities. On the other hand, infinite storage and processing capacities are available over the cloud. Thus by merging these two methods, the mutual advantages have been discussed in the past years. Due to heterogeneous resource requirements of IoT application’s task, it is not so easy to schedule these over the cloud. Existing heuristic-based scheduling strategies had mainly developed considering either minimization of makespan or minimization of cost. But, for the present scenario, an efficient schedule plan is required to schedule IoT applications over cloud that minimizes the execution time along with execution cost while preserving the task dependencies constraints as well as user-defined constraints like budget and deadline. In this paper, Cost–time computational intelligent heuristics based upon PEFT i.e. CTPEFT has been proposed to give a trade-off schedule plan between computational cost and time beneath the user-specified deadline and budget constraints. To validate the effectiveness of the proposed heuristic, extensive simulation experiments have been conducted while considering different synthetic IoT application tasks. CTPEFT has been compared with our previously proposed algorithm, i.e. BDHEFT as well as with other competing algorithms like BHEFT, and HCPPEFT. Simulation results shows that CTPEFT heuristic can generate better cost-makespan trade-off plan as compared to BDHEFT, BHEFT, and HCPPEFT.

PRUC

This paper introduces a generalized spatial regionalization problem, namely, PRUC ( P -Regions with User-defined Constraint) that partitions spatial areas into homogeneous regions. PRUC accounts for user-defined constraints imposed over aggregate region properties. We show that PRUC is an NP-Hard problem. To solve PRUC, we introduce GSLO (Global Search with Local Optimization), a parallel stochastic regionalization algorithm. GSLO is composed of two phases: (1) Global Search that initially partitions areas into regions that satisfy a user-defined constraint, and (2) Local Optimization that further improves the quality of the partitioning with respect to intra-region similarity. We conduct an extensive experimental study using real datasets to evaluate the performance of GSLO. Experimental results show that GSLO is up to 100× faster than the state-of-the-art algorithms. GSLO provides partitioning that is up to 6× better with respect to intra-region similarity. Furthermore, GSLO is able to handle 4× larger datasets than the state-of-the-art algorithms.

Human motion reconstruction using deep transformer networks

• An attention-based deep neural network for natural human motion reconstruction. • An effective model for producing realistic human motion from very few constraints. • A deep neural network outperforming previous RNN-based methods and the Transformer. • The best feature combination for faithful human motion reconstruction. Establishing a human motion reconstruction system from very few constraints imposed on the body has been an interesting and important research topic because it significantly reduces the degrees of freedom to be managed. However, it is a well-known mathematically ill-posed problem as the dimension of constraints is much lower than that of the human pose to be determined. Therefore, it is challenging to directly reconstruct the whole body joint information from very few constraints due to many possible solutions. To address this issue, we present a novel deep learning framework with an attention mechanism using large-scale motion capture (mocap) data for mapping very few user-defined constraints into the human motion as realistically as possible. Our system is built upon the attention networks for looking back further to achieve better results. Experimental results show that our network model is capable of producing more accurate results compared with previous approaches. We also conducted several experiments to test all possible combinations of the features extracted from the mocap data, and found the best feature combination to generate high-quality poses.

Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming

We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining methods encoded in ASP to extract interesting rules. We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation, and how rules can be used as explanations to help the user better understand the models. Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates that our approach is applicable to a wide range of classification tasks.

Preliminary study on PFC3D microparameter calibration using optimization of an artificial neural network

Microparameter calibration for matching macroscopic responses of particle flow code 3D (PFC3D) models is generally conducted through trial-and-error which is inefficient and time-consuming. Several automatic calibration methods have been proposed but they are still limitations in the number of calibratable microparameters, range of macroscopic responses and degree of freedom in user-defined constraints. To overcome such limitations, a novel calibration method is proposed utilizing the constrained optimization of an artificial neural network (ANN). The ANN is trained with 600 PFC3D simulations to predict the unconfined compressive strength (UCS), Young’s modulus (E) and Poisson’s ratio (v) of a PFC3D model for a given set of 15 microparameter values. Unlike other ANN-based DEM calibration methods, the proposed method calibrates microparameters by optimizing the ANN inputs rather than obtaining the calibrated values as the ANN outputs. The integration of a PFC3D-mimicking ANN with constrained optimization enables microparameter calibration for a wider range of microparameters, macroscopic responses and a higher degree of freedom in user-defined constraints.