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Reproducibility, the ability to reproduce the results of published papers or studies using their computer code and data, is a cornerstone of reliable scientific methodology. Studies where results cannot be reproduced by the scientific community should be treated with caution. Over the past decade, the importance of reproducible research has been frequently stressed in a wide range of scientific journals such as Nature and Science and international magazines such as The Economist. However, multiple studies have demonstrated that scientific results are often not reproducible across research areas such as psychology and medicine. Statistics, the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data, prides itself on its openness when it comes to sharing both computer code and data. In this article, we examine reproducibility in the field of statistics by attempting to reproduce the results in 93 published papers in prominent journals using functional magnetic resonance imaging (fMRI) data during the 2010–2021 period. Overall, from both the computer code and the data perspective, among all the 93 examined papers, we could only reproduce the results in 14 (15.1%) papers, that is, the papers provide both executable computer code (or software) with the real fMRI data, and our results matched the results in the paper. Finally, we conclude with some author-specific and journal-specific recommendations to improve the research reproducibility in statistics. [ABSTRACT FROM AUTHOR]

This article presents the results of a research in the classroom in the subject Programming I, initial course of computer programming in Systems Engineering and Computing. The research pretend to approach the impact of two methodologies of problem solving based on programming logic and the use of programming languages for its implementation. We use a qualitative perspective, as far as the students' performance as programmers, and quantitative, in what corresponds to the qualification of the written evaluations. It has been used to structure two parallel courses, using different methodologies for solving programming problems. With the first group, a methodology was implemented based on the conceptual resolution of these problems using solutions on paper and with the other one we use resolution of problems based on the intensive use of the computer. The results show a very interesting trend that allows us to make some important inferences regarding the promotion of logic and the approach of solutions in the paper against the use of the computer and the deepening in the purely technological knowledge. Conclusions leave open doors of the discussion about the different and possible ways that the students of programming to appropriate, assimilate and apply the own knowledge of this area. [ABSTRACT FROM AUTHOR]

Contribution: This article features a systematic literature review with the objective of presenting a study that reflects the current scenario of research on the cognitive load theory (CLT) in the domain of teaching and learning computer programming. Background: Computer programming is a highly cognitive skill, requiring mastering multiple competencies, and recognized as being difficult to learn, for this reason, the cognitive load (CL) in the learner’s working memory emerged as an influential concept, making CLT one of the most common theories in computing education research. Research Questions: What are the approaches that relate CLT to teaching and learning computer programming? What CLT-related concepts are covered? What evidence is reported with respect to this context? Methodology: Following a formal protocol, a survey was performed for papers linking CLT to teaching and learning programming published between 2010 and 2020. The selection of papers was based on a set of criteria established to drive the selection process, including alignment with the research questions and paper quality assessment. Findings: The approaches applied in the papers are based on measuring the CL; through instructional design based on the development or use of resources or tools, a range of different pedagogy strategies and the CLT concepts. With respect to the concepts, the subjective measurement technique and worked example effect are the most commonly deployed. As far as the evidence reported, the better part is related to the worked example effect and measuring CLs. [ABSTRACT FROM AUTHOR]

This commentary on John Symons' and Jack Horner's paper, besides sharing its main argument, challenges the authors' statement that there is no effective method to evaluate software-intensive systems as a distinguishing feature of software intensive science. It is underlined here how analogous methodological limitations characterise the evaluations of empirical systems in non-software intensive sciences. The authors' claim that formal methods establish the correctness of computational models rather than of the represented programme is here compared with the empirical adequacy problem typifying the model-based reasoning approach in physics, and the remark that testing all the paths of a software-intensive system is unfeasible is related to the enumerative induction problem in the justification of empirical law-like hypotheses in non-software intensive sciences. [ABSTRACT FROM AUTHOR]

The recent adoption of deep learning-based models for the processing and coding of multimedia signals has brought noticeable gains in performance, which have established deep learning-based solutions as the uncontested state-of-the-art both for computer vision tasks, targeting machine consumption, as well as, more recently, coding applications, targeting human visualization. Traditionally, applications requiring both coding and computer vision processing require first decoding the bitstream and then applying the computer vision methods to the decompressed multimedia signals. However, the adoption of deep learning-based solutions enables the use of compressed domain computer vision processing, with gains in performance and computational complexity over the decompressed domain approach. For point clouds (PCs), these gains have been demonstrated in the single available compressed domain computer vision processing solution, named Compressed Domain PC Classifier, which processes JPEG Pleno PC coding (PCC) compressed streams using a PC classifier largely compatible with the state-of-the-art spatial domain PointGrid classifier. However, the available Compressed Domain PC Classifier presents strong limitations by imposing a single, specific input size which is associated to specific JPEG Pleno PCC configurations; this limits the compression performance as these configurations are not ideal for all PCs due to their different characteristics, notably density. To overcome these limitations, this paper proposes the first Adaptive Compressed Domain PC Classifier solution which includes a novel adaptive bridge model that allows to process the JPEG Pleno PCC encoded bit streams using different coding configurations, now maximizing the compression efficiency. Experimental results show that the novel Adaptive Compressed Domain PC Classifier allows JPEG PCC to achieve better compression performance by not imposing a single, specific coding configuration for all PCs, regardless of its different characteristics. Moreover, the added adaptability power can achieve slightly better PC classification performance than the previous Compressed Domain PC Classifier and largely better PC classification performance (and lower number of weights) than the PointGrid PC classifier working in the decompressed domain. [ABSTRACT FROM AUTHOR]

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV's capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT's ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be "lying" in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast. [ABSTRACT FROM AUTHOR]

We seek a unified and distinctive citation description of both journals and individuals. The journal impact factor has a restrictive definition that constrains its extension to individuals, whereas the h-index for individuals can easily be applied to journals. Going beyond any single parameter, the shape of each negative slope Hirsch curve of citations vs. rank index is distinctive. This shape can be described through five minimal parameters or 'flags': the h-index itself on the curve; the average citation of each segment on either side of h; and the two axis endpoints. We obtain the five flags from real data for two journals and 10 individual faculty, showing they provide unique citation fingerprints, enabling detailed comparative assessments. A computer code is provided to calculate five flags as the output, from citation data as the input. Since papers (citations) can form nodes (links) of a network, Hirsch curves and five flags could carry over to describe local degree sequences of general networks. [ABSTRACT FROM AUTHOR]

Agent-based modelling allows researchers to build artificial pastoral systems that are spatially explicit and allow for the examination of complex interactions between households, herds, and rangelands over long time periods. However, agent-based modelling also necessarily reduces the complexity of the pastoral systems. The question that we examine in this paper is how researchers model pastoral systems and what artificial pastoral systems they create. To answer that question, we systematically reviewed 35 agent-based modelling studies of pastoral systems. We examined how the studies describe the focal pastoral system, how the focal system is represented in a conceptual model, implemented in computer code, and how it emerges as an artificial pastoral system from the simulations. Our review indicates that most models are built by interdisciplinary teams, integrated into empirical studies of pastoral systems, and use a specific pastoral system as its focal system. The research problems explored in the models range from resource management, wealth dynamics, herd demography, sustainability, adaptation, mobility, and conflict. The artificial pastoral systems that emerge from these agent-based modelling studies mostly confirm current theoretical understandings that are based on empirical studies of pastoral systems. There are a few emergent patterns that have not been validated extensively in empirical studies. We conclude with a discussion of the theoretical, methodological, and practical implications of using agent-based models to create artificial pastoral systems. [ABSTRACT FROM AUTHOR]

Alan Turing's "Computing Machinery and Intelligence," published in 1950, is one of the founding texts in the field of artificial intelligence (AI), although the term was not coined until 1958, 4 years after his death. From the treatment of human intelligence as computational and the brain as mechanical to the comparison of animals to machines to the disregard for the materiality of computers to programming as a stand-in for procreation to fiction-inspired science, many of the core tenets that have shaped the field of AI have their origins in Turing's paper. A close analysis of the paper exposes some of the problematic logic underlying these tenets that are now proving damaging for both society and the planet. [ABSTRACT FROM AUTHOR]

This paper introduces a library of algorithms for representing cloud microphysics in numerical models. The library is written in C++, hence the name libcloudph++. In the current release, the library covers three warm-rain schemes: the single- and double-moment bulk schemes, and the particle-based scheme with Monte-Carlo coalescence. The three schemes are intended for modelling frameworks of different dimensionality and complexity ranging from parcel models to multi-dimensional cloud-resolving (e.g. large-eddy) simulations. A two-dimensional prescribed-flow framework is used in example simulations presented in the paper with the aim of highlighting the library features. The libcloudph++ and all its mandatory dependencies are free and open-source software. The Boost.units library is used for zero-overhead dimensional analysis of the code at compile time. The particle-based scheme is implemented using the Thrust library that allows to leverage the power of graphics processing units (GPU), retaining the possibility to compile the unchanged code for execution on single or multiple standard processors (CPUs). The paper includes complete description of the programming interface (API) of the library and a performance analysis including comparison of GPU and CPU setups. [ABSTRACT FROM AUTHOR]

Learning computer programming is typically difficult for newcomers. Demotivation and learned helplessness have received much attention. Besides the subject's intricacy, low in-class participation has been associated with poor student achievement. This paper presents a follow-up, stage 2 study on the novel instructional technique, Student-Driven Probe Instruction (SDPI), to address low in-class participation in programming courses. Instead of the teacher lecturing/explaining content to the class and asking questions, students were shown a snippet of code or other relevant material and given the option to ask questions beforehand. The study was conducted in two stages: stage 1 pilot and stage 2. This paper presents the results of stage 2, while stage 1 operations and results are discussed briefly. The number of questions asked in class, real-time Trello board postings, and emails/Slack conversations with the instructor were used to track participation. In-class participation showed significant improvement. Average quiz and in-class activity scores showed marginal gains. Results from the endof-course survey show that students preferred SDPI over the traditional lecture style since it stirred their interest in the content and provided them the confidence to ask questions in class. The study is purely exploratory in nature, and no conclusions can be drawn due to the extremely small sample size of the student population. [ABSTRACT FROM AUTHOR]

One of the major challenges arising in internet of military things (IoMT) is accommodating massive connectivity while providing guaranteed quality of service (QoS) in terms of ultra-high reliability. In this regard, this paper presents a class of code-domain nonorthogonal multiple accesses (NOMAs) for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system (JTIDS). In the considered scenario, a satellite equipped with Nr antennas servers K devices including vehicles, drones, ships, sensors, handset radios, etc. Nonorthogonal coded modulation, a special form of multiple input multiple output (MIMO)-NOMA is proposed. The discussion starts with evaluating the output signal to interference-plus-noise (SINR) of receiver filter, leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered. The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved. The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying (MSK) and Gaussian MSK (GMSK), two pivotal modulation formats in IoMT standards such as Link-16 and JITDS. Numerical results show that near capacity performance is offered. Fortunately, the performance is obtained using simple forward error corrections (FECs) of higher coding rate than existing schemes do, while the transmit power is reduced by 6 dB. The proposed design finds wide applications not only in IoMT but also in deep space communications, where ultra reliability and massive connectivity is a keen concern. [ABSTRACT FROM AUTHOR]

The emergence of the Unified Modeling Language (UML) as the de-facto standard for modeling software systems has encouraged the development of automated software tools that facilitate automatic code generation. UML diagrams are used to diagrammatically model and specify the static structure as well as the dynamic behavior of object-oriented systems and the software tools then go ahead and automatically produce code from the given diagrams. In the last two decades substantial work has been done in this area of automatic code generation. This paper is aimed at identifying and classifying this work pertaining to automatic code generation from UML diagrams, restricting the search neither to a specific context nor to a particular programming language. A Systematic literature review (SLR) using the keywords "automatic code generation", "MDE", "code generation" and "UML" is used to identify 40 research papers published during the years 2000-2016 which are broadly classified into three groups: Approaches, Frameworks and Tools. For each paper, an analysis is made of the achievements and the gaps, the UML diagrams used the programming languages and the platform. This analysis helps to answer the main questions that the paper addresses including what techniques or implementation methods have been used for automatic code generation from UML Diagrams, what are the achievements and gaps in the field of automatic code generation from UML diagrams, which UML diagram is most used for automatic code generation from UML diagrams, which programming language source code is mostly automatically generated from the design models and which is the most used target platform? The answers provided in this paper will assist researchers, practitioners and developers to know the current state-of-the-art in automatic code generation from UML diagrams. [ABSTRACT FROM AUTHOR]

Software engineering (SE) research should be relevant to industrial practice. There have been regular discussions in the SE community on this issue since the 1980's, led by pioneers such as Robert Glass. As we recently passed the milestone of "50 years of software engineering", some recent positive efforts have been made in this direction, e.g., establishing "industrial" tracks in several SE conferences. However, many researchers and practitioners believe that we, as a community, are still struggling with research relevance and utility. The goal of this paper is to synthesize the evidence and experience-based opinions shared on this topic so far in the SE community, and to encourage the community to further reflect and act on the research relevance. For this purpose, we have conducted a Multi-vocal Literature Review (MLR) of 54 systematically-selected sources (papers and non peer-reviewed articles). Instead of relying on and considering the individual opinions on research relevance, mentioned in each of the sources, the MLR aims to synthesize and provide the "holistic" view on the topic. The highlights of our MLR findings are as follows. The top three root causes of low relevance, discussed in the community, are: (1) Researchers having simplistic views (or wrong assumptions) about SE in practice; (2) Lack of connection with industry; and (3) Wrong identification of research problems. The top three suggestions for improving research relevance are: (1) Using appropriate research approaches such as action-research; (2) Choosing relevant (practical) research problems; and (3) Collaborating with industry. By synthesizing all the discussions on this important topic so far, this paper aims to encourage further discussions and actions in the community to increase our collective efforts to improve the research relevance. Furthermore, we raise the need for empirically-grounded and rigorous studies on the relevance problem in SE research, as carried out in other fields such as management science. [ABSTRACT FROM AUTHOR]

Construction project delays can stem from a myriad of factors and pose significant challenges for owners and contractors. While numerous methods for analyzing delays exist, the majority are designed for network scheduling, lack contemporaneous tracking (i.e., do not allow for identifying and tracking delays as they occur), and struggle with attributing delays to specific stakeholders. Therefore, this paper introduces a novel graphical approach to monitor delays by comparing as-planned and as-built schedules at the activity level. Distinguished by its contemporaneous nature, the new method allows real-time tracking and quantification of delays. This facilitates identifying their origin and potential causes. Rules for constructing the graphs of various delay scenarios are demonstrated for typical activity combinations. The effectiveness of the proposed method is validated via a case study that showcases its practical application. Implementing it is straightforward, and while the method could be applied to network schedules, its relevance lies in linear and repetitive schedules. The cyclic nature of tasks in such schedules underscores the importance of contemporaneous delay tracking to facilitate swift remedial actions and pinpoint liability. Practical Applications: In this study, the authors present a new way to track delay using graphs by comparing starts, productivities, and finishes of as-planned and as-built schedules at the activity level. Because it is contemporaneous, the new approach tracks and measures delays in real time, currently manually, but easily implemented as computer code. This method makes it easier to find the source and possible causes of activity and project delays. Detailed rules are derived for common activity scenarios and interactions on how to create the respective graphs. A real-world study demonstrates how the proposed method can be used to substantiate its potential value to practitioners in industry. The proposed approach is practical, and while it could be used for network schedules, it is especially useful for linear and repetitive schedules. [ABSTRACT FROM AUTHOR]

Teaching programming is a complex process requiring learning to develop different skills. To minimize the challenges faced in the classroom, instructors have been adopting active methodologies in teaching computer programming. This article presents a Systematic Mapping Study (SMS) to identify and categorize the types of methodologies that instructors have adopted for teaching programming. We evaluated 3,850 papers published from 2000 to 2022. The results provide an overview and comprehensive view of active learning methodologies employed in teaching programming, technologies, programming languages, and the metrics used to observe student learning in this context. In the results, we identified thirty-seven different ALMs adopted by instructors. We realized that seventeen publications describe teaching approaches that combine more than one ALM, and the most reported methodologies in the studies are Flipped Classroom and Gamification-Based Learning. In addition, we are proposing an educational and collaborative tool called CollabProg, which summarizes the primary active learning methodologies identified in this SMS. CollabProg will assist instructors in selecting appropriate ALMs that align with their pedagogical requirements and teaching programming context. [ABSTRACT FROM AUTHOR]

This work studied in detail for the first time the bending of laminated composite plates subjected t mechanical variations by new theory Trigonometric and Trigonometric-Hyperbolic functions of shear deformation. From the Euler-Lagrange hypothesis and the equations of the shear deformation theory, we will develop a present method. One of the most important problems of composite plates is the analysis of their bending behavior. The correct approach used to study their bending behavior includes two trigonometric and trigonometric-hyperbolic functions satisfying the null shear stress condition at the free edges. In this paper the bending problem is solved analytically by developing a computational code and numerically solved by Finite Element Method. In order to simplify the study of the bending behavior, an approach taking into consideration the effect of the transverse shear deformation without the shear coefficient of correction with only four unknowns has been developed while requiring five or more unknowns for other theories. Convergence analysis has been carried and the results are compared to open literature available for plate bending analysis. The approach proves to be simple and useful in analyzing the bending behavior of composite layered plates. [ABSTRACT FROM AUTHOR]

Regression testing is a means to assure that a change in the software, or its execution environment, does not introduce new defects. It involves the expensive undertaking of rerunning test cases. Several techniques have been proposed to reduce the number of test cases to execute in regression testing, however, there is no research on how to assess industrial relevance and applicability of such techniques. We conducted a systematic literature review with the following two goals: firstly, to enable researchers to design and present regression testing research with a focus on industrial relevance and applicability and secondly, to facilitate the industrial adoption of such research by addressing the attributes of concern from the practitioners' perspective. Using a reference-based search approach, we identified 1068 papers on regression testing. We then reduced the scope to only include papers with explicit discussions about relevance and applicability (i.e. mainly studies involving industrial stakeholders). Uniquely in this literature review, practitioners were consulted at several steps to increase the likelihood of achieving our aim of identifying factors important for relevance and applicability. We have summarised the results of these consultations and an analysis of the literature in three taxonomies, which capture aspects of industrial-relevance regarding the regression testing techniques. Based on these taxonomies, we mapped 38 papers reporting the evaluation of 26 regression testing techniques in industrial settings. [ABSTRACT FROM AUTHOR]

The embryonic architecture, which draws inspiration from the biological process of ontogeny, has built-in mechanisms for self-repair. The entire genome is stored in the embryonic cells, allowing the data to be replicated in healthy cells in the event of a single cell failure in the embryonic fabric. A specially designed genetic algorithm (GA) is used to evolve the configuration information for embryonic cells. Any failed embryonic cell must be indicated via the proposed Built-in Self-test (BIST) the module of the embryonic fabric. This paper recommends an effective centralized BIST design for a novel embryonic fabric. Every embryonic cell is scanned by the proposed BIST in case the self-test mode is activated. The centralized BIST design uses less hardware than if it were integrated into each embryonic cell. To reduce the size of the data, the genome or configuration data of each embryonic cell is decoded using Cartesian Genetic Programming (CGP). The GA is tested for the 1-bit adder and 2-bit comparator circuits that are implemented in the embryonic cell. Fault detection is possible at every function of the cell due to the BIST module's design. The CGP format can also offer gate-level fault detection. Customized GA and BIST are combined with the novel embryonic architecture. In the embryonic cell, self-repair is accomplished via data scrubbing for transient errors. [ABSTRACT FROM AUTHOR]

This paper is the second part of a two-part research work intended at realizing a systematic computational and experimental analysis of the principal data-driven identification procedures based on the Observer/Kalman Filter Identification Methods (OKID) and the Numerical Algorithms for Subspace State-Space System Identification (N4SID). More specifically, this second paper treats the presentation of the numerical analysis and the experimental testing carried out in this study. To perform a systematic comparison, the identification methods mentioned before are implemented in a general-purpose computer program developed in the MATLAB computational environment. To this end, a simple two-degrees-of-freedom dynamical model of a vibrating mechanical system is considered first as a demonstrative example. The demonstrative example is used to carry out a numerical analysis of the performance of the computational methods of interest for this investigation. Subsequently, an experimental analysis is carried out focusing on a three-dimensional structure that vibrates under the effect of an external source of impulsive excitation. To perform a thorough analysis, the flexible structure employed as an experimental case study is modeled starting from its CAD geometric model and assuming different levels of complexity, which range from a simple three-degrees-of-freedom lumped parameter model to a relatively more complex linear finite element model. In the paper, the mechanical models of the structural system considered as illustrative examples are principally employed for comparing the results arising from the modal analysis. The computational and experimental analysis of these structural models turned out to be useful to trace guidelines for evaluating the effectiveness and the efficiency of the numerical and experimental results obtained from the identification process. In this study, a numerical and experimental analysis of the OKID algorithms and the N4SID methods is developed. Both classes of techniques enable the time-domain state-space system identification, that is, they construct an estimation of the state, input influence, output influence, and direct transmission matrices which characterize the dynamic properties of a mechanical system. The present investigation demonstrates that, if properly tuned, both the OKID methods and the N4SID algorithms lead to consistent numerical and experimental results, even in the case when the input-output measurements used for performing the identification procedure are affected by a certain degree of noise. The numerical and experimental results found in this second part of the research work confirmed the efficacy of the time-domain system identification methodologies described in the first part of the paper. [ABSTRACT FROM AUTHOR]

Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior's integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN-2SAT). The SRBFNN's objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language. With the use of SRBFNN-2SAT, a training method based on these algorithms has been presented, then training has been compared among algorithms, which were applied in Microsoft Visual C++ software using multiple metrics of performance, including Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Systematic Error (SD), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight, accompanied by the slightest iteration error, which minimizes the objective function of SRBFNN-2SAT. [ABSTRACT FROM AUTHOR]

This paper presents a screw dynamics method for multi-rigid-body systems. It establishes the relationship between velocity screw (twist) and force screw (wrench) in the theorem of momentum screw. Then, the structure of a 2UPS + 2UPU parallel manipulator is introduced as an example of application. By analyzing the constraint wrench of each limb and the twist of the moving platform in the theorem of momentum screw, the dynamics equation for solving the driving force makes the method very convenient for computer programming. The dynamics equation can be reorganized into a non-homogeneous linear equation and establishes the relationship between the constraint wrench of each limb and the twist of the moving platform in screw coordinate. The outstanding advantage of the algorithm proposed in this paper is that the average calculation time is only 83.21% of that of the Newton–Euler method for the 2UPS + 2UPU parallel manipulator on the same computer. This methodology provides a convenient canonical form for the dynamics analysis of multi-rigid-body systems. [ABSTRACT FROM AUTHOR]

Reliable and reproducible research is an important cornerstone of science, and version control systems not only make reproducible research possible in a rapid and easy way, but also provide a way of collaborating with co-authors. The purpose of this methodological paper is to present Git, a very successful version control system and how it can be used by economists working together on their papers and the accompanying computer code. Version control systems also make sharing the findings with the rest of the scientific community more easy and streamlined. To understand how version control systems came to be, one must be familiar with the history of free software. In the introduction, I will present free software and its philosophy and show how version control systems make free software possible. In the second section I present Git which is a widely used version control system. In the third section I show a basic usage of Git. In the fourth section, I conclude. [ABSTRACT FROM AUTHOR]

In cloud networks, especially those with distributed computing setups and data centers, one of the biggest obstacles is allocating resources. This is the key area, and this must be balanced between optimizing system performance on one side and affordability, stability (reliance) of operation, and energy efficiency. The importance of improving resource allocation methodologies in these complex cloud computing systems is recognized, and therefore this paper comes with an appropriate title-"Enhanced Feature-Driven Multi-Objective Learning for Optimal Cloud Resource Allocation" (OCRA), which integrates together both the latest machine learning techniques as well as traditional concepts from research into cloud computing. OCRA capably analyzes historical files on CPU, memory, disk and network usage. In addition to neatly assimilating large data sets such as that was the compliance rate with past SLAs or workload frequencies over certain time periods and resource allocations; even their patterns of service requests are an important piece of information for many busy people's lives today the adaptive mechanism is one of the defining traits of the model. It can accurately anticipate changes in resource demand and immediately adjust supply, fully able to respond rapidly when fluctuations arise suddenly or unexpectedly. Multi-Objective Random Forests are at the very core of OCRA. Each tree for decision making is specially designed to meet a particular performance objective in mind. Combining these trees into a Random Forest ensemble increases not only the model's predictive accuracy but also its stability. Pareto optimization is wisely used to maintain a balance among performance indicators, without an excessive focus on one effect alone. OCRA is proven empirically through experimental studies where key performance indicators such as Resource Utilization Rate and Quality of Service (QoS) Adherence Rate are taken into account. OCRA is both energy-efficient, an important attribute in today's environmentally conscious world, and does not sacrifice performance. As far as speed, flexibility and overall efficiency are concerned, OCRA has always been superior to the other cloud resources allocation programs of its own day. While it's still not quite ready for users who don't have a firm background in computer science or programming skills (ocra is plotted on 0-x), with sufficient memory and dominant minutes turn into mechanical equipment without configuration services. [ABSTRACT FROM AUTHOR]

Due to the development of social networks and the Internet of things, we recently have faced with large datasets. High-dimensional data is mixed with redundant and irrelevant features, so the performance of machine learning methods is reduced. Feature selection is a common way to tackle this issue with the aim of choosing a small subset of relevant and non-redundant features. Most of the existing feature selection works are for supervised applications, which assume that the information on class labels is available. While in many real-world applications, it is not possible to provide complete knowledge of class labels. To overcome this shortcoming, an unsupervised feature selection method is proposed in this paper. The proposed method uses the matrix factorization-based regularized self-representation model to weight features based on their importance. Here, we initialize the weights of features based on the correlation among features. Several experiments are performed to evaluate the effectiveness of the proposed method. Then the results are compared with several baselines and state-of-the-art methods, which show the superiority of the proposed method in most cases. [ABSTRACT FROM AUTHOR]