Miracle-dz/UniFAR-FTUs huggingface dataset:
Stipo pulcherrimae-Festucetalia pallentis Pop 1968 of calcareous petrophytic steppes in Ukraine

Abstract Nine hundred and eight-nine relevés from calcareous petrophytic steppes in Ukraine and its adjacent territories were assessed with the help of expert systems to determine the syntaxonomic affiliation of the plant communities at class and order levels. At least 488 relevés belonging to the class Festuco-Brometea were analyzed using the TWINSPAN algorithm, and 8 distinctive clusters were obtained, recognized as alliances of the order Stipo pulcherrimae-Festucetalia pallentis. A new alliance, Bromopsido cappadocicae-Asphodelinion tauricae, was ascribed to the Crimean Mountains and the presence of two alliances, Diantho lumnitzeri-Seslerion albicantis and Genisto tetragonae-Seselion peucedanifoliae, was confirmed as new for this vegetation in Ukraine. Unlike in the Pannonian Basin, Bromo pannonici-Festucion csikhegyensis alliance communities mentioned in the literature do not occur in Ukraine. Centaureo carbonatae-Koelerion talievii has been provisionally transferred from Festucetalia valesiacae to the order Stipo pulcherrimae-Festucetalia pallentis. Furthermore, we distinguished alliances by their geographic locations and their climatic (thermoregime, cryoregime, light in communities) and edaphic (carbonate content, salinity, and acidity) features.
Modeling Short-Range Interactions in Concentrated Newtonian Fiber Bundle Suspensions

Abstract Sheet Molding Compounds (SMC) offer a cost efficient way to enhance mechanical properties of a polymer with long discontinuous fibers, while maintaining formability to integrate functions, such as ribs, beads or other structural reinforcements. During SMC manufacturing, fibers remain often in a bundled configuration and the resulting fiber architecture determines part properties. Accurate prediction of this architecture by simulation of flow under consideration of the transient rheology and transient fiber orientations can speed up the development process. In particular, the interaction of bundles is of significance to predict molding pressures correctly in a direct simulation approach, which resolves individual fiber bundles. Thus, this work investigates the tangential short-range lubrication forces between fiber bundles with analytical and numerical techniques. A relation between the effective sheared gap between bundles and the bundle separation distance at the contact point is found and compared to experimental results from literature. The result is implemented in an ABAQUS contact subroutine to incorporate short-range interactions in a direct bundle simulation framework.
On the modernization of systems for supporting digital transformation: A research agenda

Digital transformation is changing the attitudes of companies to create value for customers and, this requires making alterations in their working models, organizational structure, and technological systems. However, companies have either legacy systems or monolithic systems, which raises a complex problem since there is a need for technological adaptability to meet the commercial demand for the creation of new digital products or services. In this context, this article conducts a systematic mapping study to assess how far the current literature investigates modernized systems that enable digital transformation to take place in companies. The results suggest there is a conspicuous gap with regard to studies that explore modernization models with integrated systems, or can ensure these systems can evolve without compromising the business. The study also finds gaps in the examination of methods or architectural models, which allow modernized systems to evolve gradually without interrupting the business activities.
Type and interval aware array constraint solving for symbolic execution

Array constraints are prevalent in analyzing a program with symbolic execution. Solving array constraints is challenging due to the complexity of the precise encoding for arrays. In this work, we propose to synergize symbolic execution and array constraint solving. Our method addresses the difficulties in solving array constraints with novel ideas. First, we propose a lightweight method for pre-checking the unsatisfiability of array constraints based on integer linear programming. Second, observing that encoding arrays at the byte-level introduces many redundant axioms that reduce the effectiveness of constraint solving, we propose type and interval aware axiom generation. Note that the type information of array variables is inferred by symbolic execution, whereas interval information is calculated through the above pre-checking step. We have implemented our methods based on KLEE and its underlying constraint solver STP and conducted large-scale experiments on 75 real-world programs. The experimental results show that our method effectively improves the efficiency of symbolic execution. Our method solves 182.56% more constraints and explores 277.56% more paths on average under the same time threshold.
A Survey of Field-based Testing Techniques

Field testing refers to testing techniques that operate in the field to reveal those faults that escape in-house testing. Field testing techniques are becoming increasingly popular with the growing complexity of contemporary software systems. In this article, we present the first systematic survey of field testing approaches over a body of 80 collected studies, and propose their categorization based on the environment and the system on which field testing is performed. We discuss four research questions addressing how software is tested in the field, what is tested in the field, which are the requirements, and how field tests are managed, and identify many challenging research directions.
Analysis of the First Round of 2018 Government Election for the State of Rio de Janeiro Based on Twitter

Social networks such as Twitter have become the main platforms for political debate in the field of Information Systems, making it an effective tool for candidates and parties to promote campaigns and government projects.In this context, this study aims to understand the dynamics of campaigns in Twitter during the first round of the government election in the State of Rio de Janeiro, Brazil, in 2018, and whether the electoral dispute was reflected in any way by activities on such platform. The approach involved observing candidates’ and users’ behaviors based on posts and interactions. We noticed the presence of automated accounts during the campaign using the Botometer API. Finally, the most active and most cited candidate was not the first one in the dispute, and the sudden change that occurred in the last days of the electoral race was noticed.
Solving QUBO with GPU parallel MOPSO

The Quadratic Unconstrained Binary Optimization problem (QUBO) is an NP-hard optimization problem. QUBO can be reduced from many other combinatorial optimization problems. Hence, if we can solve QUBO, we can also solve many other problems. The paper proposes a novel method to solve QUBO by reducing it into the Bi-objective Bound-constrained Continuous Optimization problem (BBCO) and then solving the BBCO with Multi-Objective Particle Swarm Optimization (MOPSO). Using 45 benchmark problem instances, the paper also shows that a GPU parallel implementation of the proposed method on the CUDA architecture finds solutions with low relative errors for the benchmark instances at most 100 variables (76% of the benchmark instances) and runs up to 202 times faster than the corresponding multi-threaded CPU program on four physical cores.
Information System applied in Construction Quality Control: A Mapping Study

Civil construction has increasingly invested in techniques for quality assurance and control. Service inspection is a key activity in the quality control process and executed by specialized professionals in order to register the quality of services. Through these inspections, it is possible to detect faults during construction. To get an overview of the approaches/techniques of information systems that have been used to support the inspection activity, a systematic mapping of the literature has been done. The literature mapping indicates the use of information systems to collect inspection data, mainly using technologies such as laser scanner, augmented reality, internal positioning and forms. Some works also use previous construction models, generated using BIM and 3D CAD models,to aid the inspection activity. Result analysis indicates that there are signs of immaturity within there search area, and it is recommended that existing systems are complemented with inspection issues tracking and quality control history features.
Dealing with a problematic roundabout by optimizing a traffic light system through evolutionary computation

The Padre Anchieta roundabout (Tenerife) takes on a substantial amount of traffic which, at peak times, often causes significant traffic jams. With the aim of alleviating traffic congestion, this paper analyses the possibility of installing a set of traffic lights in the roundabout. The duration of the phases of traffic lights are optimized through a genetic algorithm. SUMO, an open source traffic simulator, is used to evaluate the traffic of the area by considering real traffic data. Seven different scenarios are evaluated with respect to the roundabout, three of them without traffic lights and the other four with them. Simultaneously, the number of pedestrians, and the particular location of the traffic lights, are modified in each of the aforementioned scenarios, when applicable. To determine which parameters of the evolutionary algorithm provided the best results, a previous parameter setting study based on a statistical comparison procedure was performed. Particularly, the crossover operator and the population size, were analyzed. From the results obtained in the experimental assessment, we conclude that the use of an optimized traffic light system would not improve traffic flow in the roundabout. Another important conclusion is that the larger the number of pedestrians, the slower the traffic flow.
The Role of Formalism in System Requirements

A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, which is good for understandability but lacks precision. To make requirements precise, researchers have for years advocated the use of mathematics-based notations and methods, known as “formal. ” Many exist, differing in their style, scope, and applicability. The present survey discusses some of the main formal approaches and compares them to informal methods. The analysis uses a set of nine complementary criteria, such as level of abstraction, tool availability, and traceability support. It classifies the approaches into five categories based on their principal style for specifying requirements: natural-language, semi-formal, automata/graphs, mathematical, and seamless (programming-language-based). It includes examples from all of these categories, altogether 21 different approaches, including for example SysML, Relax, Eiffel, Event-B, and Alloy. The review discusses a number of open questions, including seamlessness, the role of tools and education, and how to make industrial applications benefit more from the contributions of formal approaches.
Dynamic adaptation of decomposition vector set size for MOEA/D

The Multi Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a popular algorithm for solving Multi Objective Problems (MOP). The main characteristic of MOEA/D is to use a set of weight vectors to break the MOP into a set of single-objective sub problems. It is well known that the performance of MOEA/D varies greatly depending on the number of weight vectors. However, the appropriate value for this hyper-parameter is likely to vary depending on the problem, as well as the stage of the search. In this study, we propose a robust MOEA/D variant that evaluates the progress of the search, and deletes or creates weight vectors as necessary to improve the optimization or to avoid search stagnation. The performance of the proposed algorithm is evaluated on the DTLZ and ZDT benchmark. We observed that the proposed method without needing to explicitly choose the number of weight vectors is equivalent to MOEA/D with fine tuned vectors and superior than MOEA/D with poorly tuned vectors.
Legally Enforceable Smart-Contract Languages

Smart contracts are a key component of today’s blockchains. They are critical in controlling decentralized autonomous organizations (DAO). However, smart contracts are not yet legally binding nor enforceable; this makes it difficult for businesses to adopt the DAO paradigm. Therefore, this study reviews existing Smart Contract Languages (SCL) and identifies properties that are critical to any future SCL for drafting legally binding contracts. This is achieved by conducting a Systematic Literature Review (SLR) of white- and grey literature published between 2015 and 2019. Using the SLR methodology, 45 Selected and 28 Supporting Studies detailing 45 state-of-the-art SCLs are selected. Finally, 10 SCL properties that enable legally compliant DAOs are discovered, and specifications for developing SCLs are explored.
Solving job shop scheduling problems without using a bias for good solutions

The most basic concept of (meta-)heuristic optimization is to prefer better solutions over worse ones. Algorithms utilizing Frequency Fitness Assignment (FFA) break with this idea and instead move towards solutions whose objective value has been encountered less often so far. We investigate whether this approach can be applied to solve the classical Job Shop Scheduling Problem (JSSP) by plugging FFA into the (1+1)-EA, i.e., the most basic local search. As representation, we use permutations with repetitions. Within the budget chosen in our experiments, the resulting (1+1)-FEA can obtain better solutions in average on the Fisher-Thompson, Lawrence, Applegate-Cook, Storer-Wu-Vaccari, and Yamada-Nakano benchmark sets, while performing worse on the larger Taillard and Demirkol-Mehta-Uzsoy benchmarks. We find that while the simple local search with FFA does not outperform the pure algorithm, it can deliver surprisingly good results, especially since it is not directly biased towards searching for them.
Empirically evaluating readily available information for regression test optimization in continuous integration

Regression test selection (RTS) and prioritization (RTP) techniques aim to reduce testing efforts and developer feedback time after a change to the code base. Using various information sources, including test traces, build dependencies, version control data, and test histories, they have been shown to be effective. However, not all of these sources are guaranteed to be available and accessible for arbitrary continuous integration (CI) environments. In contrast, metadata from version control systems (VCSs) and CI systems are readily available and inexpensive. Yet, corresponding RTP and RTS techniques are scattered across research and often only evaluated on synthetic faults or in a specific industrial context. It is cumbersome for practitioners to identify insights that apply to their context, let alone to calibrate associated parameters for maximum cost-effectiveness. This paper consolidates existing work on RTP and unsafe RTS into an actionable methodology to build and evaluate such approaches that exclusively rely on CI and VCS metadata. To investigate how these approaches from prior research compare in heterogeneous settings, we apply the methodology in a large-scale empirical study on a set of 23 projects covering 37,000 CI logs and 76,000 VCS commits. We find that these approaches significantly outperform established RTP baselines and, while still triggering 90% of the failures, we show that practitioners can expect to save on average 84% of test execution time for unsafe RTS. We also find that it can be beneficial to limit training data, features from test history work better than change-based features, and, somewhat surprisingly, simple and well-known heuristics often outperform complex machine-learned models.
Log-based slicing for system-level test cases

Regression testing is arguably one of the most important activities in software testing. However, its cost-effectiveness and usefulness can be largely impaired by complex system test cases that are poorly designed (e.g., test cases containing multiple test scenarios combined into a single test case) and that require a large amount of time and resources to run. One way to mitigate this issue is decomposing such system test cases into smaller, separate test cases---each of them with only one test scenario and with its corresponding assertions---so that the execution time of the decomposed test cases is lower than the original test cases, while the test effectiveness of the original test cases is preserved. This decomposition can be achieved with program slicing techniques, since test cases are software programs too. However, existing static and dynamic slicing techniques exhibit limitations when (1) the test cases use external resources, (2) code instrumentation is not a viable option, and (3) test execution is expensive. In this paper, we propose a novel approach, called DS3 (Decomposing System teSt caSe), which automatically decomposes a complex system test case into separate test case slices. The idea is to use test case execution logs, obtained from past regression testing sessions, to identify "hidden" dependencies in the slices generated by static slicing. Since logs include run-time information about the system under test, we can use them to extract access and usage of global resources and refine the slices generated by static slicing. We evaluated DS3 in terms of slicing effectiveness and compared it with a vanilla static slicing tool. We also compared the slices obtained by DS3 with the corresponding original system test cases, in terms of test efficiency and effectiveness. The evaluation results on one proprietary system and one open-source system show that DS3 is able to accurately identify the dependencies related to the usage of global resources, which vanilla static slicing misses. Moreover, the generated test case slices are, on average, 3.56 times faster than original system test cases and they exhibit no significant loss in terms of fault detection effectiveness.
Topic Modeling: Comparison of LSA and LDA on Scientific Publications

Our so-called information society is characterized by an overabundance of information which results from a growing digitization. Generally this information is in the form of unlabeled text which we cannot strictly attribute to a thematic domain. This makes the task of setting a thematic vision for a collection of data a difficult challenge. Thus, it might be useful to resort to unsupervised algorithms to tackle the topic modeling problem. Such a modeling is interested in analyzing text to capture the sens of the terms with respect to their contexts in the natural language. In this paper, we conduct an empirical comparative study between two important topic modeling approaches, latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). On one hand, the specificity of this work lies in the use of scientific publication corpus, knowing that scientific documents use very specialized vocabulary. We have used the scientific papers of the NIPS (Neural Information Processing Systems) conference. On the other hand, we have investigated the bi-gram collocation and the lemmatization on LSA and LDA. The obtained results, in As a programming language, we use Python. NLTK (Natural Language Toolkit) library [1] is used for natural language processing such as tokenization, stemming and stop words removal. Regarding topic modeling, we use Gensim, a robust and scalable open source library. It performs various tasks such as, building document representations and corpora, performing topic identification a terms of running time and topic coherence (Cv and UMass) are in favor of LDA. The lemmatization task positively influence the Cv coherence. Also the UMass coherence is insensitive to bi-grams and lemmatization.
Study on synthesis of ligustilide by a three-step method

Ligustilide was synthesized using phthalide as main raw materials by a three-step method.The optimum experimental conditions such as light,temperature and inert gas were optimized and was charactererized by HPLC-MS.The results showed that the yield of step 1 was 96%,step 2 was 68%,step 3 was 59%,and the total yield was 39%,the purity of produce was 94.39%. The side reaction can be reduced and the yield and purity of ligustilide can be improved by having low temperature,keeping away from light and air,using petroleum ether-acetone as eluent.
An approximate MIP-DoM calculation for multi-objective optimization using affinity propagation clustering algorithm

Dominance move (DoM) is a quality indicator that compares two solution sets in a Pareto-optimal sense. The main issue related to DoM is its computational expense. A recent paper proposed a mixed-integer programming (MIP) approach for computing DoM that exhibited a computational complexity that is linear to the number of objectives and polynomial to the number of solutions. Even with this property, considering practical situations, the MIP-DoM calculation on some problems may take many hours. This paper presents an approximation method to deal with the problem using a cluster-based and divide-and-conquer strategy. Some experiments are tested, showing that the cluster based-algorithm is computationally much faster and makes a small percentage error from the original DoM value.
Coevolutionary computation for adversarial deep learning

GECCO ’21 Companion, July 10–14, 2021, Lille, France © 2021 Copyright is held by the owner/author(s). ACM ISBN 978-1-4503-8351-6/21/07. https://doi.org/10.1145/3449726.3461419  Jamal Toutouh is a MSCA Postdoctoral Fellow at the ALFA group (CSAIL-MIT) and the NEO team (University of Málaga). He obtained his Ph.D. in Computer Engineering at the University of Malaga. The dissertation, “Natural Computing for Vehicular Networks,” was awarded the 2018 Best Spanish Ph.D. Thesis in Smart Cities. The dissertation focused on analyzing and devising machine learning (ML) methods inspired by Nature to address Smart Mobility problems. His current research explores the combination of Nature-inspired methods applied to ML and deep learning. He uses his novelty methods to Smart Cities and Climate Change. Instructors
Hybrid encodings for neuroevolution of convolutional neural networks: a case study

The Neuroevolution of Convolutional Neural Networks have yield into highly competitive results in the field of visual recognition in contemporary years. Some of the most recent advances in this field have been related to the design of neural encodings to represent these highly complex Deep Learning structures. Hybrid encodings have shown potential at distributing the representation of these networks into different sub-structures and thus improving the search. In this paper, we propose a compact hybrid encoding, which is used in an evolutionary framework called Deep Genetic Algorithm (DeepGA). We assess the performance of our simple representation against a hybrid encoding based on DenseBlocks, to evaluate how certain encodings might bias the search towards larger CNNs, in both single- and multi-objective scenarios. Our case study is the classification of lung conditions in chest X-ray images.