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Do open source software projects provide and maintain tests? What metrics are correlated with the test success? This paper answers these questions by executing tests of 452 open source software projects in GitHub and measuring 13 metrics from 77 projects. Only 117 projects passed all test cases. Additionally, the results are correlated with the comment density, public documented API density, and test coverage., PROFES 2014 : Product-Focused Software Process Improvement, December 10-12, 2014, Helsinki, Finland

Use of robust, quantitative tools to measure patient perspectives within product development and regulatory review processes offers the opportunity for medical device researchers, regulators, and other stakeholders to evaluate what matters most to patients and support the development of products that can best meet patient needs. The medical device innovation consortium (MDIC) undertook a series of projects, including multiple case studies and expert consultations, to identify approaches for utilizing patient preference information (PPI) to inform clinical trial design in the US regulatory context. Based on these activities, this paper offers a cogent review of considerations and opportunities for researchers seeking to leverage PPI within their clinical trial development programs and highlights future directions to enhance this field. This paper also discusses various approaches for maximizing stakeholder engagement in the process of incorporating PPI into the study design, including identifying novel endpoints and statistical considerations, crosswalking between attributes and endpoints, and applying findings to the population under study. These strategies can help researchers ensure that clinical trials are designed to generate evidence that is useful to decision makers and captures what matters most to patients.

Consumers often get confused to select the best cloud providers from the huge marketplace. The hesitancy of consumers further escalates when multiple service providers offer the same type and quality of services. To deal with such an uncertainty, the decision-makers always combine multiple factors to make an informed choice. Sentiment mining is one of the key parameters to determine the service quality and get an insight into the business. It assists service providers in precisely deduce consumer's emotions regarding the product. The analysis helps providers fine-tune the product based on consumer's sentiment and accommodates the consumer's request to find an optimal service provider. Several existing literature for cloud service selection mainly focuses on the Quality of Service (QoS) of the offered services. However, very few of them have considered the user experience of a consumer in the decision-making process. Moreover, there is minimal literature that amalgamates Quality of Experience (QoE) with customized Quality of Service (QoS) requirements to decide on a complex framework. The paper addresses the issue by aggregating consumer’s sentiments with customized QoS parameters to choose an optimal service provider. The paper uses the fuzzy Best Worst Method (BWM) to determine the weights of selection criteria and use the fuzzy TOPSIS to handle the uncertain linguistic preference. Analysis results demonstrate the applicability and effectiveness of the framework.

We continue to develop the analytic Langlands program for curves over local fields initiated in our earlier papers, following a suggestion of Langlands and a work of Teschner. Namely, we study the Hecke operators which we introduced in those papers in the case of a projective line with parabolic structures at finitely many points for the group $$PGL_2$$ P G L 2 . We establish most of our conjectures in this case.

Building foundation systems are a significant but understudied contributor to embodied carbon emissions of the built environment, and typically use excess material in prismatic, bending-dominated typologies. This paper identifies and characterizes a promising pathway for reducing the embodied carbon associated with reinforced concrete shallow foundations through an alternative typology, thin shell foundations. The main focus is a quantification and comparison of the environmental impact of typical spread footings and materially efficient shell foundations. Validated analytical engineering equations are applied in a parametric design workflow for the same design load and soil bearing capacity. By iterating through this workflow systematically, insights are gained regarding the applicability of shell foundations to various building typologies and site conditions. Results show that for small column loads and weak soils, shells reduce embodied carbon by about half compared to spread footings. For high applied loads, shells significantly outperform their prismatic counterparts, reducing the environmental impact by almost two-thirds. Foundations are then considered within the context of a whole building structural frame to determine the potential downstream savings when multiple systems are optimized to reduce material use and mass. When floor slabs are shape-optimized in addition to using shell foundations, a building structural system can be constructed for nearly one-quarter of the embodied carbon of a typical system. To take advantage of these potential savings, a method for fabricating thin shell foundations, where earth is compacted and milled to create the formwork, is presented following a review of digital fabrication methods.

Purpose of Review On March 14, 2023, the European Commission (EC) published the much awaited “Proposal for a regulation (…) to improve the Union’s electricity market design.” The proposed regulation reflects the verdict of the EC after several months of fervent debate triggered by the energy crisis that has affected the European region. In this paper, we discuss several crucial elements that are part of the proposed regulation. Recent Findings In a nutshell, we deem the EC has done a great job managing a highly complicated situation. The proposal preserves the crucial role of short-term electricity markets and puts the focus on the key flaw: the perennial incompleteness of long-term power markets. The EC has put forward a large battery of measures, covering different dimensions and with very different potential impacts on the market design. Summary Here we focus on what we consider to be the four key elements of the proposal: (i) the promotion of long-term contracting, (ii) interventions during electricity price crises, (iii) the strategy for an efficient supplier risk management, and (iv) flexibility support schemes and capacity remuneration mechanisms.

This paper describes a relation between the elliptic stable envelopes of a hypertoric variety $$X$$ X and a distinguished K-theory class on the product of the loop hypertoric space $$\widetilde{\mathscr {L}}X$$ L ~ X and its symplectic dual $$\mathscr {P}X^!$$ P X ! . This class intertwines the K-theoretic stable envelopes in a certain limit. Our results are suggestive of a possible categorification of elliptic stable envelopes.

This paper describes a technique on an optimization of tree-structure data by of multi-objective evolutionary algorithm, or multi-objective genetic programming. GP induces bloat of the tree structure as one of the major problem. The cause of bloat is that the tree structure obtained by the crossover operator grows bigger and bigger but its evaluation does not improve. To avoid the risk of bloat, a partial sampling operator is proposed as a mating operator. The size of the tree and a structural distance are introduced into the measure of the tree-structure data as the objective functions in addition to the index of the goodness of tree structure. GP is defined as a three-objective optimization problem. SD is also applied for the ranking of parent individuals instead to the crowding distance of the conventional NSGA-II. When the index of the goodness of tree-structure data is two or more, the number of objective functions in the above problem becomes four or more. We also propose an effective many-objective EA applicable to such the many-objective GP. We focus on NSGA-II based on Pareto partial dominance (NSGA-II-PPD). NSGA-II-PPD requires beforehand a combination list of the number of objective functions to be used for Pareto partial dominance (PPD). The contents of the combination list greatly influence the optimization result. We propose to schedule a parameter r meaning the subset size of objective functions for PPD and to eliminate individuals created by the mating having the same contents as the individual of the archive set., source:Ohki, M. Multi-objective genetic programming with partial sampling and its extension to many-objective. SN Appl. Sci. (2019) 1: 207. https://doi.org/10.1007/s42452-019-0208-y. This is a post-peer-review, pre-copyedit version of an article published in SN, source:https://link.springer.com/article/10.1007/s42452-019-0208-y

Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of AI for simulation, where traditional numerical simulations are supported by artificial intelligence approaches. We focus specifically on a program for training machine learning models supporting a computational fluid dynamics application. We use custom TensorFlow provided by the Poplar SDK to adapt the program for the IPU-POD16 platform and investigate its ease of use and performance scalability. Training a model on data from OpenFOAM simulations allows us to get accurate simulation state predictions in test time. We show how to utilize the popdist library to overcome a performance bottleneck in feeding training data to the IPU on the host side, achieving up to 34{\%} speedup. Due to communication overheads, using data parallelism to utilize two IPUs instead of one does not improve the throughput. However, once the intra-IPU costs have been paid, the hardware capabilities for inter-IPU communication allow for good scalability. Increasing the number of IPUs from 2 to 16 improves the throughput from 560.8 to 2805.8 samples/s., The authors would like to thank Grzegorz Andrejczuk for his ideas and help with investigating data loading overheads. Big thanks to Charis Fisher for her support and valuable comments. Researcher Sergio Iserte was supported by the postdoctoral fellowship APOSTD/2020/026 from Valencian Region Government (GVA) and European Social Funds (ESF). CFD Simulations were executed on Tirant III cluster of the Servei d’Informàtica of the University of Valencia (UV)., Peer Reviewed, Postprint (author's final draft)

Consider a quantum cat map M associated with a matrix $$A\in {{\,\textrm{Sp}\,}}(2n,{\mathbb {Z}})$$ A ∈ Sp ( 2 n , Z ) , which is a common toy model in quantum chaos. We show that the mass of eigenfunctions of M on any nonempty open set in the position–frequency space satisfies a lower bound which is uniform in the semiclassical limit, under two assumptions: (1) there is a unique simple eigenvalue of A of largest absolute value and (2) the characteristic polynomial of A is irreducible over the rationals. This is similar to previous work (Dyatlov and Jin in Acta Math 220(2):297–339, 2018; Dyatlov et al. in J Am Math Soc 35(2):361–465, 2022) on negatively curved surfaces and (Schwartz in The full delocalization of eigenstates for the quantized cat map, 2021) on quantum cat maps with $$n=1$$ n = 1 , but this paper gives the first results of this type which apply in any dimension. When condition (2) fails we provide a weaker version of the result and discuss relations to existing counterexamples. We also obtain corresponding statements regarding semiclassical measures and damped quantum cat maps.

Nanotechnology and nanomaterials have offered sustainable design options for the built environment and enabled architects to design more flexible architectural forms. Carbon nanotubes have excellent mechanical, electrical, thermal, and chemical properties and are useful in a wide range of engineering applications. However, the role of carbon nanotube composites as a functional construction material has large potential and awaits further investigation and exploration. This paper gives an overview of the synthesis and fabrication methods of carbon nanotubes, carbon nanotube properties, different forms of carbon nanotube composites, and application of carbon nanotubes in the construction industry. To explore the prospects for construction use, the aesthetic, structural, and functional characteristics of several futuristic building projects are discussed. This overview proposes a promising material approach for the application of carbon nanotubes in construction and explains the related opportunities and challenges.

Research in deep learning for video game playing has received much attention and provided very relevant results in the last years. Frameworks and libraries have been developed to ease game playing research leveraging Reinforcement Learning techniques. In this paper, we propose to use two of them (RLLIB and GYM) in a very different scenario, such as learning to apply resource management policies in a multi-core server, specifically, we leverage the facilities of both frameworks coupled to derive policies for power-capping. Using RLlib and Gym enables implementing different resource management policies in a simple and fast way and, as they are based on neural networks, guarantees the efficiency in the solution, and the use of hardware accelerators for both training and inference. The results demonstrate that game-learning environments provide an effective support to cast a completely different scenario, and open new research avenues in the field of resource management using reinforcement learning techniques with minimal development effort.

Alasdair Urquhart’s work on models for relevant logics is distinctive in a number of different ways. One key theme, present in both his undecidability proof for the relevant logic R (Urquhart 1984) and his proof of the failure of interpolation in R (Urquhart 1993), is the use of techniques from geometry (Urquhart 2019). In this paper, inspired by Urquhart’s work, I explore ways to generate natural models of R+ from geometries, and different constraints that an accessibility relation in such a model might satisfy. I end by showing that a set of natural conditions on an accessibility relation, motivated by geometric considerations, is jointly unsatisfiable.

Word sense disambiguation (WSD) is a very critical yet challenging task in natural language processing (NLP), which aims at identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory. Existing WSD methods usually focus on learning the semantic interactions between a special ambiguous word and the glosses of its candidate senses and thus ignore complicated relations between the neighboring ambiguous words and their glosses, leading to insufficient learning of the interactions between words in context. As a result, they are difficult to leverage the knowledge from the other ambiguous words which might provide some explicit clues to identify the meaning of current ambiguous word. To mitigate this challenge, this paper proposes a novel neural model based on memory enhancement mechanism for WSD task, which stores the gloss knowledge of previously identified words into a memory, and further utilizes it to assist the disambiguation of the next target word. Extensive experiments, which are conducted on a unified evaluation framework of the WSD task, demonstrate that our model achieves better disambiguation performance than the state-of-the-art approaches (Code: https://github.com/baoshuo/WSD ).

Word sense disambiguation (WSD) is a very critical yet challenging task in natural language processing (NLP), which aims at identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory. Existing WSD methods usually focus on learning the semantic interactions between a special ambiguous word and the glosses of its candidate senses and thus ignore complicated relations between the neighboring ambiguous words and their glosses, leading to insufficient learning of the interactions between words in context. As a result, they are difficult to leverage the knowledge from the other ambiguous words which might provide some explicit clues to identify the meaning of current ambiguous word. To mitigate this challenge, this paper proposes a novel neural model based on memory enhancement mechanism for WSD task, which stores the gloss knowledge of previously identified words into a memory, and further utilizes it to assist the disambiguation of the next target word. Extensive experiments, which are conducted on a unified evaluation framework of the WSD task, demonstrate that our model achieves better disambiguation performance than the state-of-the-art approaches (Code: https://github.com/baoshuo/WSD ).

Background Computational approaches in STEM foster creative extrapolations of ideas that extend the bounds of human perception, processing, and sense-making. Inviting teachers to explore computational approaches in STEM presents opportunities to examine shifting relationships to inquiry that support transdisciplinary learning in their classrooms. Similarly, play has long been acknowledged as activity that supports learners in taking risks, exploring the boundaries and configurations of existing structures, and imagining new possibilities. Yet, play is often overlooked as a crucial element of STEM learning, particularly for adolescents and adults. In this paper, we explore computational play as an activity that supports teachers’ transdisciplinary STEM learning. We build from an expansive notion of computational activity that involves jointly co-constructing and co-exploring rule-based systems in conversation with materials, collaborators, and communities to work towards jointly defined goals. We situate computation within STEM-rich making as a playful context for engaging in authentic, creative inquiry. Our research asks What are the characteristics of play and computation within computational play? And, in what ways does computational play contribute to teachers’ transdisciplinary learning? Results Teachers from grades 3–12 participated in a professional learning program that centered playful explorations of materials and tools using computational approaches: making objects based on rules that produce emergent behaviors and iterating on those rules to observe the effects on how the materials behaved. Using a case study and descriptions of the characteristics of computational play, our results show how familiarity of materials and the context of play encouraged teachers to engage in transdisciplinary inquiry, to ask questions about how materials behave, and to renegotiate their own relationships to disciplinary learning as they reflected on their work. Conc

More than a century ago, A.A. Griffith published the seminal paper establishing the foundational framework for fracture mechanics. The elegant theory creatively introduced the concepts of elastic energy and surface energy to the science of fracture, and solved the problem of brittle fracture of glass materials. Many subsequent milestone studies in fracture mechanics were motivated by the real problems encountered in different materials. The emergence of two-dimensional (2D) materials provides an exciting opportunity to examine fracture processes at the 2D limit. An important question to be addressed is whether the classic Griffith theory is still applicable to 2D materials. Therefore, recent progress in both experimental and theoretical studies of fracture of 2D materials will be briefly reviewed, with new developments and discoveries in relevant techniques and theories highlighted. Given the early stage of exploring fracture behaviors in 2D materials, more emphasis will be placed on challenges and opportunities for this budding field. Graphical abstract

Disaster-hit and/or un-electrified remote areas usually have electricity accessibility issues and an abundance of plant-derived debris and wood from destroyed wooden structures; this can be potentially addressed by employing a decentralized ultra-small biomass-fed gasification power generating system. This paper presents an assessment of the technical viability of an ultra-small gasification system that utilizes densified carbonized wood pellets/briquettes. The setup was run continuously for 100 h. A variety of biomass was densified and carbonized by harnessing fugitive heat sources before charging into the reactor. Carbonized briquettes and furnished blends exhibited inferior gasification performance compared to the carbonized pellets. In the absence of tar blockage problems, steady-state conditions were achieved when pre-treated feedstock was used. Under steady-state conditions for carbonized pellets gasification operated at an equivalence ratio of 0.32, cold gas efficiency and carbon conversion achieved 49.2% and 70.5%, respectively. Overall efficiency and maximum power output of 20.3% and 21 kW were realised, respectively. It was found that the system could keep stable while the low heating valve of syngas was over 4 MJ/m3 on condition that avoiding tar blocking issues. The results indicate that the proposed compact ultra-small power generation system is a technically feasible approach to remedy power shortage challenge. In addition, process simulation considering carbonized wood gasification combined power generation was formulated to produce syngas and electricity. Woody pellets with the flow rate of 20 kg/h could generate a 15.18 kW power at the air flow rate of 40 Nm3/h, which is in a good agreement with 15 kW in the 100 h operation. It is indicated that the gasification combined power generation cycle simulated by Aspen simulator could achieve reliable data to assist the complicated experiment operation. © 2022, The Author(s).

This paper introduces IAO, a new swarm intelligence approach for addressing the challenge of task scheduling in cloud computing. The proposed method uses conventional Aquila Optimizer (AO) and Particle Swarm Optimizer (PSO) as a hybrid method based on a novel transition mechanism. The proposed hybrid method, IAO, combined the AO and PSO to avoid the weaknesses they face; these weaknesses are trapped in the local search area and have low solution diversity. The proposed transition mechanism is proposed to acquire proper changes between the search operators in order to keep the improvements; it changes between them when any algorithm gets stuck or the solutions diversity decreases. Several scenarios are conducted and tested to validate the suggested method’s ability to address the task scheduling problem; these scenarios contain various tasks (i.e., 600, 1000, and 2000). The obtained results are compared with other well-known methods in terms of Max, Mean, Min of the Expected Complete Time (ECT), Friedman ranking test, and Wilcoxon signed-rank test. The proposed IAO method achieved better results and promising compared to other comparative methods; it is an excellent scheduling approach for solving any related scheduling problem.

With the rapid deployment of graph neural networks (GNNs) based techniques in a wide range of applications such as link prediction, community detection, and node classification, the explainability of GNNs become an indispensable component for predictive and trustworthy decision making. To achieve this goal, some recent works focus on designing explainable GNN models such as GNNExplainer, PGExplainer, and Gem. These GNN explainers have shown remarkable performance in explaining the predictive results from GNNs. Despite their success, the robustness of these explainers is less explored in terms of vulnerabilities of GNN explainers. Graph perturbations such as adversarial attacks can lead to inaccurate explanations and consequently cause catastrophes. Thus, in this paper, we take the first step and strive to explore the robustness of GNN explainers. To be specific, we first define two adversarial attack scenarios—aggressive adversary and conservative adversary to contaminate graph structures. We then investigate the impacts of the poisoned graphs on the explainability of three prevalent GNN explainers with three standard evaluation metrics: Fidelity+, Fidelity-, and Sparsity. We conduct experiments on synthetic and real-world datasets and focus on two popular graph mining tasks: node classification and graph classification. Our empirical results suggest that GNN explainers are generally not robust to the adversarial attacks caused by graph structural noises.

A model of an information system describes its processes and how these processes manipulate data objects. Object-aware extensions of Petri nets focus on modeling the life-cycle of objects and their interactions. In this paper, we focus on Petri nets with identifiers, where identifiers are used to refer to objects. These objects should “behave” well in the system from inception to termination. We formalize this intuition in the notion of identifier soundness, and show that although this property is undecidable in general, useful subclasses exist that guarantee identifier soundness by construction.

As bats are an important indicator for the health of their habitat, projects in multiple countries monitor bat populations by collecting audio recordings of bat calls. Analysing these recordings is however a tedious task and there is a need for systems that accurately and efficiently detect and classify bat calls. While earlier studies focused on detection and classification separately, in this paper we propose a first approach that combines these two tasks. Moreover, we aim to build a multi-label classifier that is able to detect if multiple bat species are present in the same audio recording. One of the challenges we face is that the available data focuses either on detection or single-label classification, but not on the combined task of detection and multi-label classification. We propose to address this by a data augmentation approach, and demonstrate that the resulting approach achieves the objectives of being accurate and efficient.

With the rapid deployment of graph neural networks (GNNs) based techniques in a wide range of applications such as link prediction, community detection, and node classification, the explainability of GNNs become an indispensable component for predictive and trustworthy decision making. To achieve this goal, some recent works focus on designing explainable GNN models such as GNNExplainer, PGExplainer, and Gem. These GNN explainers have shown remarkable performance in explaining the predictive results from GNNs. Despite their success, the robustness of these explainers is less explored in terms of vulnerabilities of GNN explainers. Graph perturbations such as adversarial attacks can lead to inaccurate explanations and consequently cause catastrophes. Thus, in this paper, we take the first step and strive to explore the robustness of GNN explainers. To be specific, we first define two adversarial attack scenarios—aggressive adversary and conservative adversary to contaminate graph structures. We then investigate the impacts of the poisoned graphs on the explainability of three prevalent GNN explainers with three standard evaluation metrics: Fidelity+, Fidelity-, and Sparsity. We conduct experiments on synthetic and real-world datasets and focus on two popular graph mining tasks: node classification and graph classification. Our empirical results suggest that GNN explainers are generally not robust to the adversarial attacks caused by graph structural noises.

Web-based chatbots provide website owners with the benefits of increased sales, immediate response to their customers, and insight into customer behaviour. While Web-based chatbots are getting popular, they have not received much scrutiny from security researchers. The benefits to owners come at the cost of users’ privacy and security. Vulnerabilities, such as tracking cookies and third-party domains, can be hidden in the chatbot’s iFrame script. This paper presents a large-scale analysis of five Web-based chatbots among the top 1-million Alexa websites. Through our crawler tool, we identify the presence of chatbots in these 1-million websites. We discover that 13,392 out of the top 1- million Alexa websites (1.58%) use one of the five analysed chatbots. Our analysis reveals that the top 300k Alexa ranking websites are dominated by Intercom chatbots that embed the least number of third-party domains. LiveChat chatbots dominate the remaining websites and embed the highest samples of third-party domains. We also find that 721 (5.38%) web-based chatbots use insecure protocols to transfer users’ chats in plain text. Furthermore, some chatbots heavily rely on cookies for tracking and advertisement purposes. More than two-thirds (68.92%) of the identified cookies in chatbot iFrames are used for ads and tracking users. Our results show that, despite the promises for privacy, security, and anonymity given by most websites, millions of users may unknowingly be subject to poor security guarantees by chatbot service providers.

This paper introduces IAO, a new swarm intelligence approach for addressing the challenge of task scheduling in cloud computing. The proposed method uses conventional Aquila Optimizer (AO) and Particle Swarm Optimizer (PSO) as a hybrid method based on a novel transition mechanism. The proposed hybrid method, IAO, combined the AO and PSO to avoid the weaknesses they face; these weaknesses are trapped in the local search area and have low solution diversity. The proposed transition mechanism is proposed to acquire proper changes between the search operators in order to keep the improvements; it changes between them when any algorithm gets stuck or the solutions diversity decreases. Several scenarios are conducted and tested to validate the suggested method’s ability to address the task scheduling problem; these scenarios contain various tasks (i.e., 600, 1000, and 2000). The obtained results are compared with other well-known methods in terms of Max, Mean, Min of the Expected Complete Time (ECT), Friedman ranking test, and Wilcoxon signed-rank test. The proposed IAO method achieved better results and promising compared to other comparative methods; it is an excellent scheduling approach for solving any related scheduling problem.

Numerous methods for bottleneck detection, along novel approaches for bottleneck prediction, are available in literature. To facilitate the development and application of such methods, this paper proposes a holistic methodology for Bottleneck Analysis in dynamic value streams. Analogous to established data analytics levels, namely descriptive, diagnostic, predictive, and prescriptive analytics, the methodology specifies objectives for data-driven Bottleneck Analysis. Based on state-of-the-art bottleneck detection methods, the methodology provides measures for the diagnosis of bottleneck severity and frequency. Additionally, it considers prediction methods to anticipate emerging bottlenecks, depending on available databases. Finally, the methodology provides a context for the yet unexplored field of bottleneck prescription, which aims to mitigate bottleneck effects by data-driven control recommendations. Further practical application of the methodology has to confirm its suitability as a holistic framework for analyzing bottlenecks in dynamic value streams.

The inclusion of ethics and integrity in preservice teacher education can refer to several contexts. The first context is academic integrity for preservice teachers with a focus on incorporating academic integrity in their own studies within higher education, as well as teaching them different components of academic writing including proper referencing techniques to enable them to write their own academic texts. The second context is developing the professional ethics of these future teachers, as a lived practice as well as through ethical codes. The third context is giving preservice teachers pedagogical and didactic knowledge and instruments to teach academic integrity and ethics to their future students in primary and secondary education, in order to prepare them for their own higher education endeavours. While attention is paid to the first and the second context, the third one is only rarely treated as a part of preservice teacher education as it is usually assumed that the first context is indirectly giving enough preparation for a preservice teacher to subsequently mediate ethics and integrity to his or her students. In this paper, we argue that this assumption is problematic and propose the inclusion of the third context – preparing future teachers to teach academic integrity and ethics – in preservice teacher education. Although the chapter uses the preservice teacher education in Sweden as a starting point, the three ethical contexts that are identified are not limited to any particular national preservice education system but are relevant in other similar frameworks as well.

The experiences and viewpoints of adolescents as regards dating violence in southern countries remain poorly documented. It is thus difcult to have an overall understanding of the phenomenon. Based on social representation theory, this paper attempts to fll this gap by answering the question: What is dating violence for Chilean adolescents? Knowing what adolescents consider to be dating violence and the contextual elements that infuence their perceptions is essential to developing efective prevention strategies. This is particularly important as prevention policies are currently non-existent in Chile, despite the fact that adolescents living there must deal with many risk factors. In this context, a qualitative multi-methodological study (free association and focus groups) was conducted with 142 Chilean adolescents from public and private schools. This study showed that the explanations of dating violence given by boys and girls were infuenced by gender and a context of ambivalent sexism. It also took place in a changing socio-cultural Chilean context. Dating violence used by boys was driven by machismo and its exercise by girls was perceived to come from cultural changes concerning women's rights. In addition, the physical nature of dating violence was deeply entrenched in the study participants' social representations, the fgurative image being hits. Based on these results, we recommend that future research and prevention strategies should consider the sociocultural context and gender as aspects likely to infuence both the adolescents' social representations of dating violence and their behaviors.

Current trends in sports monitoring are characterized by the massive collection of tech-based biomechanical, physiological and performance data, integrated through mathematical algorithms. However, the application of algorithms, predicated on mechanistic assumptions of how athletes operate, cannot capture, assess and adequately promote athletes’ health and performance. The objective of this paper is to reorient the current integrative proposals of sports monitoring by re-conceptualizing athletes as complex adaptive systems (CAS). CAS contain higher-order perceptual units that provide continuous and multilevel integrated information about performer–environment interactions. Such integrative properties offer exceptional possibilities of subjective monitoring for outperforming any objective monitoring system. Future research should investigate how to enhance this human potential to contribute further to athletes’ health and performance. This line of argument is not intended to advocate for the elimination of objective assessments, but to highlight the integrative possibilities of subjective monitoring.

This paper highlights the crucial enabling factors in menstrual hygiene management. The use of products depends on various factors such as access to water, privacy, social, cultural and econmic. Gender equality and women’s empowerment are integral parts of the Sustainable Development Goals (SDGs). Half of the world’s population are women, and women have specific needs to manage the menstrual cycle during their lifetime. To manage the bleeding during the menstrual cycle, girls and women use different products, depending on their accessibility and affordability. They are (a) disposable—one-time use products such as disposable pads, tampons, and (b) reusable products—reusable products such as cloth, washable and reusable cloth pads, menstrual cups, and period panties. The literature search revealed that there are limited studies related to the use of menstrual products and the impacts of these products on women’s health and the environment. A rapid review of the literature identified factors responsible for choosing a particular type of menstrual product, the perceptions of women using the products, and their implications on health and the environment. The study concluded with the need to study the type of menstrual products preferred by women using appropriate variables, address the issues of disposal systems, provide adolescent girls with adequate infrastructure, provide access to affordable sanitary products and gender equity to manage their periods with dignity, and increasing awareness regarding sustainable/ reusable menstrual products, suggesting further investigation in menstrual hygiene management.

Urban air quality and climate protection are two major challenges for future mobility systems. Despite the steady reduction of pollutant emissions from vehicles over past decades, local immission load within cities partially still reaches heights, which are considered potentially hazardous to human health. Although traffic-related emissions account for a major part of the overall urban pollution, modelling the exact interaction remains challenging. At the same time, even lower vehicle emissions can be achieved by using synthetic fuels and the latest exhaust gas cleaning technologies. In the paper at hand, a neural network modelling approach for traffic-induced immission load is presented. On this basis, a categorization of vehicle concepts regarding their immission contribution within an impact scale is proposed. Furthermore, changes in the immission load as a result of different fleet compositions and emission factors are analysed within different scenarios. A final comparison is made as to which modification measures in the vehicle fleet offer the greatest potential for overall cleaner air.

This paper highlights the crucial enabling factors in menstrual hygiene management. The use of products depends on various factors such as access to water, privacy, social, cultural and econmic. Gender equality and women’s empowerment are integral parts of the Sustainable Development Goals (SDGs). Half of the world’s population are women, and women have specific needs to manage the menstrual cycle during their lifetime. To manage the bleeding during the menstrual cycle, girls and women use different products, depending on their accessibility and affordability. They are (a) disposable—one-time use products such as disposable pads, tampons, and (b) reusable products—reusable products such as cloth, washable and reusable cloth pads, menstrual cups, and period panties. The literature search revealed that there are limited studies related to the use of menstrual products and the impacts of these products on women’s health and the environment. A rapid review of the literature identified factors responsible for choosing a particular type of menstrual product, the perceptions of women using the products, and their implications on health and the environment. The study concluded with the need to study the type of menstrual products preferred by women using appropriate variables, address the issues of disposal systems, provide adolescent girls with adequate infrastructure, provide access to affordable sanitary products and gender equity to manage their periods with dignity, and increasing awareness regarding sustainable/ reusable menstrual products, suggesting further investigation in menstrual hygiene management.

Current trends in sports monitoring are characterized by the massive collection of tech-based biomechanical, physiological and performance data, integrated through mathematical algorithms. However, the application of algorithms, predicated on mechanistic assumptions of how athletes operate, cannot capture, assess and adequately promote athletes’ health and performance. The objective of this paper is to reorient the current integrative proposals of sports monitoring by re-conceptualizing athletes as complex adaptive systems (CAS). CAS contain higher-order perceptual units that provide continuous and multilevel integrated information about performer–environment interactions. Such integrative properties offer exceptional possibilities of subjective monitoring for outperforming any objective monitoring system. Future research should investigate how to enhance this human potential to contribute further to athletes’ health and performance. This line of argument is not intended to advocate for the elimination of objective assessments, but to highlight the integrative possibilities of subjective monitoring.

Large-scale dark web marketplaces have been around for more than a decade. So far, academic research has mainly focused on drug and hacking-related offers. However, data markets remain understudied, especially given their volatile nature and distinct characteristics based on shifting iterations. In this paper, we perform a large-scale study on dark web data markets. We first characterize data markets by using an innovative theoretical legal taxonomy based on the Council of Europe's Cybercrime Convention and its implementation in Dutch law. The recent Covid-19 pandemic showed that cybercrime has become more prevalent with the increase of digitalization in society. In this context, important questions arise regarding how cybercrime harms are determined, measured, and prioritized. We propose a determination of harm based on criminal law qualifications and sanctions. We also address the empirical question of what the economic activity on data markets looks like nowadays by performing a comprehensive measurement of digital goods based on an original dataset scraped from twelve marketplaces consisting of approximately 28,000 offers from 642 vendors. The resulting analysis combines insights from the theoretical legal framework and the results of the measurement study. To our knowledge, this is the first study to combine these two elements systematically.

This paper highlights the crucial enabling factors in menstrual hygiene management. The use of products depends on various factors such as access to water, privacy, social, cultural and econmic. Gender equality and women’s empowerment are integral parts of the Sustainable Development Goals (SDGs). Half of the world’s population are women, and women have specific needs to manage the menstrual cycle during their lifetime. To manage the bleeding during the menstrual cycle, girls and women use different products, depending on their accessibility and affordability. They are (a) disposable—one-time use products such as disposable pads, tampons, and (b) reusable products—reusable products such as cloth, washable and reusable cloth pads, menstrual cups, and period panties. The literature search revealed that there are limited studies related to the use of menstrual products and the impacts of these products on women’s health and the environment. A rapid review of the literature identified factors responsible for choosing a particular type of menstrual product, the perceptions of women using the products, and their implications on health and the environment. The study concluded with the need to study the type of menstrual products preferred by women using appropriate variables, address the issues of disposal systems, provide adolescent girls with adequate infrastructure, provide access to affordable sanitary products and gender equity to manage their periods with dignity, and increasing awareness regarding sustainable/ reusable menstrual products, suggesting further investigation in menstrual hygiene management.

Current trends in sports monitoring are characterized by the massive collection of tech-based biomechanical, physiological and performance data, integrated through mathematical algorithms. However, the application of algorithms, predicated on mechanistic assumptions of how athletes operate, cannot capture, assess and adequately promote athletes’ health and performance. The objective of this paper is to reorient the current integrative proposals of sports monitoring by re-conceptualizing athletes as complex adaptive systems (CAS). CAS contain higher-order perceptual units that provide continuous and multilevel integrated information about performer–environment interactions. Such integrative properties offer exceptional possibilities of subjective monitoring for outperforming any objective monitoring system. Future research should investigate how to enhance this human potential to contribute further to athletes’ health and performance. This line of argument is not intended to advocate for the elimination of objective assessments, but to highlight the integrative possibilities of subjective monitoring.

ver the years, people decrease their ability to remember certain events, which is more evident in older adults. Therefore, cognitive health professionals use tools and methods to train and assess attention and memory; among those solutions are found serious games. These games are intended to teach, while the player has fun. These games’ demand an important amount of Human–Computer Interaction (HCI) characteristics, which are not formally included during their development. A previous contribution, presented a first approach of a method to build serious games, named ADE2. In those studies, we established as further work the need of perform the empirical evaluation of the solution. Therefore, this paper presents an experimental process, where the ADE2 method has been evaluated by 22 software engineers, who evaluated it by using the Method Evaluation Model (MEM). The results show that the ADE2 method can be successfully adopted by the software engineering community, and the actions performed, by following each step of the method, can be reached effectively and efficiently.

Although traditional recommendation methods trained on observational interaction information have engendered a significant impact in real-world applications, it is challenging to disentangle users’ true interests from interaction data. Recent disentangled learning methods emphasize on untangling users’ true interests from historical interaction records, which however overlook auxiliary information to correct bias. In this paper, we design a novel method called SeDLR (Semantics Disentangled Learning Recommendation) to bridge this gap. Particularly, by leveraging rich heterogeneous information networks (HIN), SeDLR is capable of untangling high-order user-item relationships into multiple independent components according to their semantic user intents. In addition, SeDLR offers reliable explanations for the disentangled graph embeddings by the designed Monte Carlo edge-drop component. Finally, we conduct extensive experiments on two benchmark datasets and achieve state-of-the-art performance compared against recent strong baselines.

In the 1950’s, Ford and Fulkerson introduced dynamic flows by incorporating the notion of time into the network flow model (Oper. Res., 1958). In this paper, motivated by real-world applications including route planning and evacuations, we extend the framework of multi-commodity dynamic flows to the heterogeneous commodity setting by allowing different transit times for different commodities along the same edge. We first show how to construct the time-expanded networks, a classical technique in dynamic flows, in the heterogeneous setting. Based on this construction, we give a pseudopolynomial-time algorithm for the quickest flow problem when there are two heterogeneous commodities. We then present a fully polynomial-time approximation scheme when the nodes have storage for any number of heterogeneous commodities. The algorithm is based on the condensed time-expanded network technique introduced by Fleischer and Skutella (SIAM J. Comput., 2007).

Recently, the remarkable effect of applying Dynamic Graph Neural Networks (DGNNs) to traffic speed prediction has received wide attention. Existing DGNN-based researches usually use a pre-defined or an adaptive matrix to capture the spatial correlations in traffic data. However, these static matrices are not enough to match the dynamic characteristics of spatial correlations. We argue that the global changes and local fluctuations of spatial correlations are dynamic with different frequencies. To this end, in this paper, we propose a Two-Tower DGNN (T2 -GNN) framework which divides the traffic data into a seasonal static component and an acyclic dynamic component, thus enhancing traffic speed prediction. The two components generated by an auto-decomposing block reflect global changes and local fluctuations of spatial correlations, respectively. Moreover, we use two parallel dynamic graph generation layers to construct a seasonal graph and an acyclic graph at each time step. In this way, the high-level representations of these two kinds of dynamic changes are learned through two dynamic graph convolution layers. Besides, the impact of fixed road network structure is modeled on the pre-defined graph and added to the spatial correlations. And we capture temporal correlations in temporal block before modeling spatial correlations. Finally, skip connections are used to converge the spatial-temporal correlations for final prediction. Experimental results on an urban dataset and two highway datasets show our proposed framework achieves the state-of-the-art prediction performances in terms of Mean Average Error (MAE) and Root Mean Squared Error (RMSE).

The creation of a digital twin of rail infrastructure assets places greater emphasis on requirements engineering, model-based delivery methods, and digital information management to support the creation of both physical and virtual deliverables. However, requirements engineering capabilities are latent in comparison to complex discrete manufacturing. In this paper, we explore requirements engineering practices in Australian rail infrastructure projects creating digital twins for asset management and operations. An investigation of the challenges encountered by project teams during the development of asset information requirements for physical and digital deliverables was conducted using an in-depth literature review together with semi-structured interviews with rail project delivery teams. Challenges to the maturity of requirements engineering were categorised according to their main characteristics. The process, technology and supply chain issues identified provide empirical evidence of the pain points faced by delivery teams in developing asset information requirements in support of the creation of a digital twin. Findings serve as a starting point for further research into the development of requirements engineering methods distinguished by systems-based approaches.

Graphlets, being the fundamental building blocks, are essential for understanding and analysing complex networks. The original notion of graphlets, however, is unable to encode edge attributes in many types of networks, especially in egocentric social networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Through applying the proposed method to a case study of chronic pain patients, we find that not only a patient’s social network structure could inform his/her perceived pain grade, but also particular types of social relationships, such as friends, colleagues and healthcare workers, are more important in understanding the effect of chronic pain. Further, we demonstrate that including TyE-GDV as additional features leads to significant improvement in a typical machine learning task.

Weed detection and classification are considered one of the most vital tools in identifying and recognizing plants in agricultural fields. Recently, machine learning techniques have been rapidly growing in the precision farming area related to plants, as well as weed detection and classification techniques. In digital agricultural analysis, these techniques have played and will continue to play a vital role in mitigating health, agricultural, and environmental impacts, improving sustainability, and reducing herbicides. Deep learning-based models are employed to solve the more sophisticated agricultural issues using individual CNN networks and hybrid models. Such models showed promising results. This paper highlights the major trends from the particular review of detection and classification approaches for weed plants. This review elaborates on the aspects of using traditional methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the benefits and limitations of existing machine learning techniques, including deep learning techniques, and introduces several related plant leaves, weed datasets, and weeding machinery. Evaluation of the existing techniques has been compared, taking into account the real-world dataset used, images’ capacity, and shortcomings. Furthermore, this study helps to introduce the promising results and identify critically the remaining challenges in achieving robust weed detection, which could support noteworthy agricultural problems and assist researchers in the future. The significance of this study is to provide the potential techniques for solving illumination, overlapping, and occlusion issues of leafy plants, as well as other plant issues.

The spontaneous assembly of materials from elementary building blocks is one of the most intriguing natural phenomena. Conventional modeling relies physical approaches to examine such processes. In this paper, a framework is proposed to offer an alternative paradigm, via the use of deep learning, and specifically the use of generative adversarial models as well as a combination of natural language processing and transformer neural nets to create hierarchical assemblies of building blocks. We study the assembly of elementary flame particles into hierarchical materials with features across scales, illustrating the Universality–Diversity Principle (UDP), and create novel material using additive manufacturing. Graphical abstract

This paper describes fundamental principles for two-dimensional pattern design of folded robots, specifically mobile robots consisting of closed-loop kinematic linkage mechanisms. Three fundamental methods for designing closed-chain folded four-bar linkages – the basic building block of these devices – are introduced. Modular connection strategies are also introduced as a method to overcome the challenges of designing assemblies of linkages from a two-dimensional sheet. The result is a design process that explores the tradeoffs between the complexity of linkage fabrication and also allows the designer combine multiple functions or modes of locomotion. A redesigned modular robot capable of multi-modal locomotion and grasping is presented to embody these design principles., National Science Foundation (Grants EFRI-1240383 and CCF- 1138967)

AbstractMachine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.

The general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in machine learning. The goal of this paper is to take a broad look into coarsening techniques that have been successfully deployed in scientific computing and see how similar principles are finding their way in more recent applications related to machine learning. In scientific computing, coarsening plays a central role in algebraic multigrid methods as well as the related class of multilevel incomplete LU factorizations. In machine learning, graph coarsening goes under various names, e.g., graph downsampling or graph reduction. Its goal in most cases is to replace some original graph by one which has fewer nodes, but whose structure and characteristics are similar to those of the original graph. As will be seen, a common strategy in these methods is to rely on spectral properties to define the coarse graph.

This paper presents a sampling-based planning algorithm for in-hand manipulation of a grasped object using a series of external pushes. A high-level sampling-based planning framework, in tandem with a low-level inverse contact dynamics solver, effectively explores the space of continuous pushes with discrete pusher contact switch-overs. We model the frictional interaction between gripper, grasped object, and pusher, by discretizing complex surface/line contacts into arrays of hard frictional point contacts. The inverse dynamics problem of finding an instantaneous pusher motion that yields a desired instantaneous object motion takes the form of a mixed nonlinear complementarity problem. Building upon this dynamics solver, our planner generates a sequence of pushes that steers the object to a goal grasp. We evaluate the performance of the planner for the case of a parallel-jaw gripper manipulating different objects, both in simulation and with real experiments. Through these examples, we highlight the important properties of the planner: respecting and exploiting the hybrid dynamics of contact sticking/sliding/rolling and a sense of efficiency with respect to discrete contact switch-overs.