nils-herrmann/clean_scirepeval huggingface dataset:
The Use of System Dynamics Simulation in Integrated Water Resources Management

In this paper we discuss the use of system dynamics as a methodology with which to address dynamically complex problems in water resources management. Problems in regional planning and river basin management, urban water management, flooding and irrigation exhibit important short-term and long-term effects, and are often contentious issues with high potential for conflict. We argue that system dynamics combined with stakeholder involvement provides an appropriate methodology to address these issues effectively. We trace the theoretical and practical evolution of system dynamics in these areas over the past 40 years. From this review of the literature we identify and discuss a number of best practices and common pitfalls in applications of system dynamics.
Identifying effective influencers based on trust for electronic word-of-mouth marketing: A domain-aware approach

Because effective influencers in an online social network (OSN) can significantly affect consumers’ purchasing decisions via trust among users in electronic word-of-mouth (eWOM) marketing, identifying these influencers with respect to user trust relationships has become increasingly important. However, many existing studies overlook the domain attribute of trust and the time-varying nature of social networks and only analyze a static snapshot of a user trust network (UTN). To address these issues and investigate this topic in the e-commerce context, this study proposes a research framework that takes into account the dimensions of trust, domain, and time. A time-varying hypergraph is developed to model the OSN using the time-varying features of multi-type relationships, and an algorithm is developed to extract a domain-aware UTN based on the time-varying hypergraph and user trust relationships. Reinforced by the dimensions of trust, domain, and time, a novel product review domain-aware (PRDA) approach is conceived that identifies effective influencers and categorizes them into three types, i.e., emerging influencers, holding influencers, and vanishing influencers, based on their popularity status across the life cycle. The experimental results from the Epinions dataset show that the PRDA approach outperforms both the social network-based influence-evaluating approach and the ‘‘popular author’’ approach.
Explaining Black Boxes on Sequential Data using Weighted Automata

Understanding how a learned black box works is of crucial interest for the future of Machine Learning. In this paper, we pioneer the question of the global interpretability of learned black box models that assign numerical values to symbolic sequential data. To tackle that task, we propose a spectral algorithm for the extraction of weighted automata (WA) from such black boxes. This algorithm does not require the access to a dataset or to the inner representation of the black box: the inferred model can be obtained solely by querying the black box, feeding it with inputs and analyzing its outputs. Experiments using Recurrent Neural Networks (RNN) trained on a wide collection of 48 synthetic datasets and 2 real datasets show that the obtained approximation is of great quality.
Deep Multi-Task Network for Learning Person Identity and Attributes

Person re-identification (re-ID) has been gaining in popularity in the research community owing to its numerous applications and growing importance in the surveillance industry. Recent methods often employ partial features for person re-ID and offer fine-grained information beneficial for person retrieval. In this paper, we focus on learning improved partial discriminative features using a deep convolutional neural architecture, which includes a pyramid spatial pooling module for efficient person feature representation. Furthermore, we propose a multi-task convolutional network that learns both personal attributes and identities in an end-to-end framework. Our approach incorporates partial features and global features for identity and attribute prediction, respectively. Experiments on several large-scale person re-ID benchmark data sets demonstrate the accuracy of our approach. For example, we report rank-1 accuracies of 85.37% (+3.47 %) and 92.81% (+0.51 %) on the DukeMTMC re-ID and Market-1501 data sets, respectively. The proposed method shows encouraging improvements compared with the state-of-the-art methods.
Virtual machines vs. containers in cloud gaming systems

In cloud gaming the game is rendered on a distant cloud server and the resulting video stream is sent back to the user who controls the game via a thin client. The high resource usage of cloud gaming servers is a challenge. Expensive hardware including GPUs have to be efficiently shared among multiple simultaneous users. The cloud servers use virtualization techniques to isolate users and share resources among dedicated servers. The traditional virtualization techniques can however inflict notable performance overhead limiting the user count for a single server. Operating-system-level virtualization instances known as containers are an emerging trend in cloud computing. Containers don't need to virtualize the entire operating system still providing most of the benefits of virtualization. In this paper, we evaluate the container-based alternative to traditional virtualization in cloud gaming systems through extensive experiments. We also discuss the differences needed in system implementation using the container approach and identify the existing limitations.
A Descriptive/Comparative Study of the Evolution of Process Models of Software Development Life Cycles (PM-SDLCs)

A relevant knowledge [24] (and consequently research area) is the study of software lifecycle process models (PM-SDLCs). Such process models have been defined in three abstraction levels: (i) full organizational software lifecycles process models (e.g. ISO 12207, ISO 15504, CMMI/SW); (ii) lifecycles frameworks models (e.g. waterfall, spiral, RAD, and others) and (iii) detailed software development life cycles process (e.g. unified process, TSP, MBASE, and others). This paper focuses on (ii) and (iii) levels and reports the results of a descriptive/comparative study of 13 PM-SDLCs that permits a plausible explanation of their evolution in terms of common, distinctive, and unique elements as well as of the specification rigor and agility attributes. For it, a conceptual research approach and a software process lifecycle meta-model are used. Findings from the conceptual analysis are reported. Paper ends with the description of research limitations and recommendations for further research.
Developing Mathematical Thinking with Scratch - An Experiment with 6th Grade Students

One of the latest trends in the educational landscape is the introduction of computer programming in the K-12 classroom to develop computational thinking in students. As computational thinking is not a skill exclusively related to computer science, it is assumed – but not yet scientifically proven – that the problem solving process may be generalized and transferred to a wide variety of problems. This paper presents a research designed to test whether the use of coding in Maths classes could have a positive impact on learning outcomes of students in their mathematical skills. Therefore, the questions we want to investigate in this paper are if the use of programming in Maths classes improves (a) modeling process and reality phenomena, (b) reasoning, (c) problem formulation and problem solving, and (d) comparison and execution of procedures and algorithms. We have therefore designed a quantitative, quasi-experimental experiment with 42 participating 6th grade (11 and 12 years old) students. Results show that there is a statistically significant increase in the understanding of mathematical processes in the experimental group, which received training in Scratch.
3-D Floating-Gate Synapse Array With Spike-Time-Dependent Plasticity

This paper proposes a 3-D floating-gate (FG) synapse array for neuromorphic applications. The designed device has certain advantages over previous planar FG synapse devices: a smaller cell size due to the stacked structure and smaller operation voltage by the gate-all-around geometry. In addition, the operation method to implement spike time-dependent plasticity is proposed and demonstrated. The proposed array based on commercialized flash memory technology is expected be one of the most promising candidate architecture for neuromorphic applications.
An Introduction to Sensor Data Analytics

The increasing advances in hardware technology for sensor processing and mobile technology has resulted in greater access and availability of sensor data from a wide variety of applications. For example, the commodity mobile devices contain a wide variety of sensors such as GPS, accelerometers, and other kinds of data. Many other kinds of technology such as RFID-enabled sensors also produce large volumes of data over time. This has lead to a need for principled methods for efficient sensor data processing. This chapter will provide an overview of the challenges of sensor data analytics and the different areas of research in this context. We will also present the organization of the chapters in this book in this context.
Standardizing Ethical Design for Artificial Intelligence and Autonomous Systems

For decades—even prior to its inception—AI has aroused both fear and excitement as humanity has contemplated creating machines like ourselves. Unfortunately, the misconception that “intelligent” artifacts should necessarily be human-like has largely blinded society to the fact that we have been achieving AI for some time. Although AI that surpasses human ability grabs headlines (think of Watson, Deep Mind, or alphaGo), AI has been a standard part of the industrial repertoire since at least the 1980s, with expert systems checking circuit boards and credit card transactions. Machine learning (ML) strategies for generating AI have also long been used, such as genetic algorithms for nding solutions to intractable computational problems like scheduling, and neural networks not only to model and understand human learning but also for basic industrial control, monitoring, and classi cation. In the 1990s, probabilistic and Bayesian methods revolutionized ML and opened the door to one of the most pervasive AI abilities now available: searching through massive troves of data. Innovations in AI and ML algorithms have extended our capacity to nd information in texts, allowing us to search photographs as well as both recorded and live video and audio. We can translate, transcribe, read lips, read emotions (including lying), forge signatures and other handwriting, and forge video. Yet, the downside of these bene ts is ever present. As we write this, allegations are circulating that the Standardizing Ethical Design for Artifi cial Intelligence and Autonomous Systems
Evaluation of Retrieval Algorithms for Expertise Search

Evaluation of expertise search systems is a non-trivial task. While in a typical search engine the responses to user queries are documents, the search results for an expertise retrieval system are people. The relevancy scores indicate how knowledgeable they are on a given topic. Within an organisation, such a ranking of employees could potentially be difficult as well as controversial. We introduce an in-house capability search system built for an organisation with a diverse range of disciplines. We report on two attempts of evaluating six different ranking algorithms implemented for this system. Evaluating the system using relevance judgements produced in each of the two attempts leads to an understanding of how different methods of collecting judgements on people's expertise can lead to different effectiveness of algorithms.
Hopfield neural networks for timetabling: formulations, methods, and comparative results

This paper considers the use of discrete Hopfield neural networks for solving school timetabling problems. Two alternative formulations are provided for the problem: a standard Hopfield-Tank approach, and a more compact formulation which allows the Hopfield network to be competitive with swapping heuristics. It is demonstrated how these formulations can lead to different results. The Hopfield network dynamics are also modified to allow it to be competitive with other metaheuristics by incorporating controlled stochasticities. These modifications do not complicate the algorithm, making it possible to implement our Hopfield network in hardware. The neural network results are evaluated on benchmark data sets and are compared to results obtained using greedy search, simulated annealing and tabu search.
Objectively measured physical activity has a negative but weak association with academic performance in children and adolescents.

There is an emerging body of evidence on the potential effects of regular physical activity on academic performance. The aim of this study was to add to the debate, by examining the association between objectively measured physical activity and academic performance in a relatively large sample of children and adolescents.



The Spanish UP & DOWN study is a 3-year longitudinal study designed to assess the impact, overtime, of physical activity and sedentary behaviours on health indicators. This present analysis was conducted with 1778 children and adolescents aged 6-18 years. Physical activity was objectively measured by accelerometry. Academic performance was assessed using school grades.



Physical activity was inversely associated with all academic performance indicators after adjustment for potential confounders, including neonatal variables, fatness and fitness (all p < 0.05). This association became nonsignificant among quartiles of physical activity. There were only slight differences in academic performance between the lowest and the second quartile of physical activity, compared to the highest quartile, with very small effect size (d < 0.20).



Objectively measured physical activity may influence academic performance during both childhood and adolescence, but this association was negative and very weak. Longitudinal and intervention studies are necessary to further our understanding.
Entrepreneurial Development and Interventionist Agencies in Nigeria

The focus of this paper is on entrepreneurial development and analysis of Interventionist Agencies in Nigeria. It examines the critical stages or sphere of development required of the entrepreneur in order to enable him perform his strategic functions in the organization and in the context of organizational strategic management in Nigeria. In pursuit of the focus of this paper, it treats numerous issues (an overview inclusive). It also examines the entrepreneurial roles and factors affecting its strategic management importance. Furthermore it x-rays in detail the three-skill approach to entrepreneurial development. These include technical, human and conceptual skills. It analyzes some government interventionist institutions and agencies established to encourage entrepreneurial development in Nigeria. The paper posits that though there is a widespread knowledge of the efficacy of entrepreneurial development mix, integrated entrepreneurial development efforts indicates that several of the institutions established by government concentrated on a partial approach to entrepreneurial development programme. Finally, it concludes and recommends four priorities agenda to enhance the entrepreneurial development in Nigeria.
Current status and key issues in image steganography: A survey

Steganography and steganalysis are the prominent research fields in information hiding paradigm. Steganography is the science of invisible communication while steganalysis is the detection of steganography. Steganography means “covered writing” that hides the existence of the message itself. Digital steganography provides potential for private and secure communication that has become the necessity of most of the applications in today’s world. Various multimedia carriers such as audio, text, video, image can act as cover media to carry secret information. In this paper, we have focused only on image steganography. This article provides a review of fundamental concepts, evaluation measures and security aspects of steganography system, various spatial and transform domain embedding schemes. In addition, image quality metrics that can be used for evaluation of stego images and cover selection measures that provide additional security to embedding scheme are also highlighted. Current research trends and directions to improve on existing methods are suggested.
Observations related to pathogenesis of dengue hemorrhagic fever. V. Examination of agspecific sequential infection rates using a mathematical model.

We have previously shown that disease differing widely in severity and prognosis is included in the entity, dengue hemorrhagic fever (DHF) in Thailand."2 Nearly 40 percent of 523 children hospitalized with DHF had a syndrome characterized by shock following a fever of several days duration. The rest had less severe febrile illnesses with various mild hemorrhagic manifestations. When sera from these children were studied, a significant correlation between shocked patients and a secondary-type antibody response to dengue virus was found.8 This report considers in greater detail the various manifestations of dengue disease and the antibody response in the host. Observations are included that suggest that severity of host response to dengue infection is influenced by an interaction between immune status and the age and sex of the patient. Associations between severity of illness and the rate of virus recovery, the quantity of antibody produced and the type of dengue virus recovered are described. A synthesis of these data and their relevance to the pathogenesis of human dengue infection are presented in the final paper in this series.  Patient selection, serologic methods, virus isolation and identification techniques and definition of primary and secondary dengue antibody responses have been described .',',' This report is based upon experience with 604 dengue hemorrhagic fever patients: 400 admitted to Children's 81 fatal hemorrhagic fever cases admitted to other Bangkok hospitals in 1962-1964. All DHF patients who survived their infection had serologic evidence of recent dengue infection according to established criteria' and a discharge diagnosis of hemorrhagic fever. Included for some evaluations are 35 out
Optimizing sleep to maximize performance: implications and recommendations for elite athletes.

Despite a growing body of literature demonstrating a positive relationship between sleep and optimal performance, athletes often have low sleep quality and quantity. Insufficient sleep among athletes may be due to scheduling constraints and the low priority of sleep relative to other training demands, as well as a lack of awareness of the role of sleep in optimizing athletic performance. Domains of athletic performance (e.g., speed and endurance), neurocognitive function (e.g., attention and memory), and physical health (e.g., illness and injury risk, and weight maintenance) have all been shown to be negatively affected by insufficient sleep or experimentally modeled sleep restriction. However, healthy adults are notoriously poor at self-assessing the magnitude of the impact of sleep loss, underscoring the need for increased awareness of the importance of sleep among both elite athletes and practitioners managing their care. Strategies to optimize sleep quality and quantity in athletes include approaches for expanding total sleep duration, improving sleep environment, and identifying potential sleep disorders.
Deep learning and the Schrödinger equation

We have trained a deep (convolutional) neural network to predict the ground-state energy of an electron in four classes of confining two-dimensional electrostatic potentials. On randomly generated potentials, for which there is no analytic form for either the potential or the ground-state energy, the neural network model was able to predict the ground-state energy to within chemical accuracy, with a median absolute error of 1.49 mHa. We also investigate the performance of the model in predicting other quantities such as the kinetic energy and the first excited-state energy of random potentials. While we demonstrated this approach on a simple, tractable problem, the transferability and excellent performance of the resulting model suggests further applications of deep neural networks to problems of electronic structure.
Social Media in Disaster Relief Usage Patterns , Data Mining Tools , and Current Research Directions

As social media has become more integrated into peoples’ daily lives, its users have begun turning to it in times of distress. People use Twitter, Facebook, YouTube, and other social media platforms to broadcast their needs, propagate rumors and news, and stay abreast of evolving crisis situations. Disaster relief organizations have begun to craft their efforts around pulling data about where aid is needed from social media and broadcasting their own needs and perceptions of the situation. They have begun deploying new software platforms to better analyze incoming data from social media, as well as to deploy new technologies to specifically harvest messages from disaster situations.
The potential of sustainable algal biofuel production using wastewater resources.

The potential of microalgae as a source of renewable energy has received considerable interest, but if microalgal biofuel production is to be economically viable and sustainable, further optimization of mass culture conditions are needed. Wastewaters derived from municipal, agricultural and industrial activities potentially provide cost-effective and sustainable means of algal growth for biofuels. In addition, there is also potential for combining wastewater treatment by algae, such as nutrient removal, with biofuel production. Here we will review the current research on this topic and discuss the potential benefits and limitations of using wastewaters as resources for cost-effective microalgal biofuel production.
Convolutional Neural Networks combined with Runge-Kutta Methods

A convolutional neural network for image classification can be constructed mathematically since it can be regarded as a multi-period dynamical system. In this paper, a novel approach is proposed to construct network models from the dynamical systems view. Since a pre-activation residual network can be deemed an approximation of a time-dependent dynamical system using the forward Euler method, higher order Runge-Kutta methods (RK methods) can be utilized to build network models in order to achieve higher accuracy. The model constructed in such a way is referred to as the Runge-Kutta Convolutional Neural Network (RKNet). RK methods also provide an interpretation of Dense Convolutional Networks (DenseNets) and Convolutional Neural Networks with Alternately Updated Clique (CliqueNets) from the dynamical systems view. The proposed methods are evaluated on benchmark datasets: CIFAR-10/100, SVHN and ImageNet. The experimental results are consistent with the theoretical properties of RK methods and support the dynamical systems interpretation. Moreover, the experimental results show that the RKNets are superior to the state-of-the-art network models on CIFAR-10 and on par on CIFAR-100, SVHN and ImageNet.
Masked 3D conditional generative adversarial network for rock mesh generation

3D-GAN (Wu et al.  2016) has been introduced as a novel way to generate 3D models. In this paper, we propose a 3D-Masked-CGAN approach to apply in the generation of irregular 3D mesh geometry such as rocks. While there are many ways to generate 3D objects, the generation of irregular 3D models has its own peculiarity. To make a model realistic is extremely time-consuming and in high cost. In order to control the shape of generated 3D models, we extend 3D-GAN by adding conditional information into both the generator and discriminator. It is shown that that this model can generate 3D rock models with effective control over the shapes of generated models.