nils-herrmann/clean_scirepeval huggingface dataset:
"show-me": Water Consumption at a Glance to Promote Water Conservation in the Shower

Water is a scarce resource worldwide. Yet, we have many opportunities to conserve it. One particular opportunity for water conservation is the shower, because depending on the shower head and shower habits, an individual can save many liters of fresh water each day. Feedback proved to be an effective method to promote sustainable behavior. Therefore, in this paper we suggest to promote water conservation by providing feedback in form of an ambient display that can easily be integrated in current shower types. We built a prototype to study the potential of such a feedback device. These shower water meter (show-me) display the amount of water, that is used during one shower in form of LEDs assembled on a stick. Thus, an increasing water level is visualized. The user study revealed two groups. The subjects who considered themselves as ecologically conscious changed their behavior and turned the water down or off while soaping. Also, they are willing to pursue this behavior. Other subjects who did not have the goal to act more sustainable, were surprised about their water consumption and tried to reduce it. However, after the removal of the show-me device they did not maintain their behavior and fell back into their previous habit.
Measurement of capacitive sensor with Arduino

This paper presents a measurement system with capacitive sensor connected to an open-source electronic platform Arduino Uno. A simple code was modified in the project, which ensures that the platform works as interface for the sensor. The code can be modified and upgraded at any time to fulfill other specific applications. The simulations were carried out in the platform's own environment and the collected data are represented in graphical form. Accuracy of developed measurement platform is 0.1 pF.
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions

Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be learned without access to supervised data. To address this problem we present a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion. It incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently. On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches that do not incorporate such prior knowledge. We demonstrate its ability to handle occlusion and show that it can extrapolate learned knowledge to scenes with different numbers of objects.
A Server-Assigned Spatial Crowdsourcing Framework

With the popularity of mobile devices, spatial crowdsourcing is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing, the goal is to crowdsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to physically travel to those locations in order to perform the tasks. In this article, we focus on one class of spatial crowdsourcing, in which the workers send their locations to the server and thereafter the server assigns to every worker tasks in proximity to the worker’s location with the aim of maximizing the overall number of assigned tasks. We formally define this maximum task assignment (MTA) problem in spatial crowdsourcing, and identify its challenges. We propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space, including the spatial distribution and the travel cost of the workers. MTA is based on the assumptions that all tasks are of the same type and all workers are equally qualified in performing the tasks. Meanwhile, different types of tasks may require workers with various skill sets or expertise. Subsequently, we extend MTA by taking the expertise of the workers into consideration. We refer to this problem as the maximum score assignment (MSA) problem and show its practicality and generality. Extensive experiments with various synthetic and two real-world datasets show the applicability of our proposed framework.
Pathways to language: fiber tracts in the human brain

The human language function is not only based on the grey matter of circumscribed brain regions in the frontal and the temporal cortex but moreover on the white matter fiber tracts connecting these regions. Different pathways connecting frontal and temporal cortex have been identified. The dorsal pathway projecting from the posterior portion of Broca's area to the superior temporal region seems to be of particular importance for higher-order language functions. This pathway is particularly weak in non-human compared to human primates and in children compared to adults. It is therefore considered to be crucial for the evolution of human language, which is characterized by the ability to process syntactically complex sentences.
Learning Graph Representations with Recurrent Neural Network Autoencoders

Representing and comparing graphs is a central problem in many fields. We present an approach to learn representations of graphs using recurrent neural network autoencoders. Recurrent neural networks require sequential data, so we begin with several methods to generate sequences from graphs, including random walks, breadth-first search, and shortest paths. We train long short-term memory (LSTM) autoencoders to embed these graph sequences into a continuous vector space. We then represent a graph by averaging its graph sequence representations. The graph representations are then used for graph classification and comparison tasks. We demonstrate the effectiveness of our approach by showing improvements over the existing state-of-the-art on several graph classification tasks, including both labeled and unlabeled graphs.
Autonomous Soft Robotic Fish Capable of Escape Maneuvers Using Fluidic Elastomer Actuators.

In this work we describe an autonomous soft-bodied robot that is both self-contained and capable of rapid, continuum-body motion. We detail the design, modeling, fabrication, and control of the soft fish, focusing on enabling the robot to perform rapid escape responses. The robot employs a compliant body with embedded actuators emulating the slender anatomical form of a fish. In addition, the robot has a novel fluidic actuation system that drives body motion and has all the subsystems of a traditional robot onboard: power, actuation, processing, and control. At the core of the fish's soft body is an array of fluidic elastomer actuators. We design the fish to emulate escape responses in addition to forward swimming because such maneuvers require rapid body accelerations and continuum-body motion. These maneuvers showcase the performance capabilities of this self-contained robot. The kinematics and controllability of the robot during simulated escape response maneuvers are analyzed and compared with studies on biological fish. We show that during escape responses, the soft-bodied robot has similar input-output relationships to those observed in biological fish. The major implication of this work is that we show soft robots can be both self-contained and capable of rapid body motion.
A Reliability Coefficient for Maximum Likelihood Factor Analysis*

Maximum likelihood factor analysis provides an effective method for estimation of factor matrices and a useful test statistic in the likelihood ratio for rejection of overly simple factor models. A reliability coefficient is proposed to indicate quality of representation of interrelations among attributes in a battery by a maximum likelihood factor analysis. Usually, for a large sample of individuals or objects, the likelihood ratio statistic could indicate that an otherwise acceptable factor model does not exactly represent the interrelations among the attributes for a population. The reliability coefficient could indicate a very close representation in this case and be a better indication as to whether to accept or reject the factor solution.
IP-Based IoT Device Detection

Recent IoT-based DDoS attacks have exposed how vulnerable the Internet can be to millions of insufficiently secured IoT devices. To understand the risks of these attacks requires learning about these IoT devices---where are they, how many are there, how are they changing? In this paper, we propose a new method to find IoT devices in Internet to begin to assess this threat. Our approach requires observations of flow-level network traffic and knowledge of servers run by the manufacturers of the IoT devices. We have developed our approach with 10 device models by 7 vendors and controlled experiments. We apply our algorithm to observations from 6 days of Internet traffic at a college campus and partial traffic from an IXP to detect IoT devices.
SOAR: An Architecture for General Intelligence

The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem-solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them. In this article we present Soar, an implemented proposal for such an architecture. We describe its organizational principles, the system as currently implemented, and demonstrations of its capabilities. This research was sponsored by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 3597, monitored by the Air Force Avionics Laboratory under contracts F33615-81-K-1539 and N00039-83C-0136, and by the Personnel and Training Research Programs,  number NR667-477. Additional partial support was provided by the Sloan Foundation and some computing support was supplied by the SUMEX-AIM facility (NIH grant number RR-00785). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency, the Office of Naval Research, the Sloan Foundation, the National Institute of Health, or the US Government.
The Internet of Things: A Survey from the Data-Centric Perspective

Advances in sensor data collection technology, such as pervasive and embedded devices, and RFID Technology have lead to a large number of smart devices which are connected to the net and continuously transmit their data over time. It has been estimated that the number of internet connected devices has overtaken the number of humans on the planet, since 2008. The collection and processing of such data leads to unprecedented challenges in mining and processing such data. Such data needs to be processed in real-time and the processing may be highly distributed in nature. Even in cases, where the data is stored offline, the size of the data is often so large and distributed, that it requires the use of big data analytical tools for processing. In addition, such data is often sensitive, and brings a number of privacy challenges associated 384 MANAGING AND MINING SENSOR DATA with it. This chapter will discuss a data analytics perspective about mining and managing data associated with this phenomenon, which is now known as the internet of things.
Role of the venous return in critical illness and shock: part II-shock and mechanical ventilation.

To provide a conceptual and clinical review of the physiology of the venous system as it is related to cardiac function in health and disease.



An integration of venous and cardiac physiology under normal conditions, critical illness, and resuscitation.



The usual clinical teaching of cardiac physiology focuses on left ventricular pathophysiology and pathology. Due to the wide array of shock states dealt with by intensivists, an integrated approach that takes into account the function of the venous system and its interaction with the right heart may be more useful. In part II of this two-part review, we describe the physiology of venous return and its interaction with the right heart function as it relates to mechanical ventilation and various shock states including hypovolemic, cardiogenic, obstructive, and septic shock. In particular, we demonstrate how these shock states perturb venous return/right heart interactions. We also show how compensatory mechanisms and therapeutic interventions can tend to return venous return and cardiac output to appropriate values.



An improved understanding of the role of the venous system in pathophysiologic conditions will allow intensivists to better appreciate the complex circulatory physiology of shock and related therapies. This should enable improved hemodynamic management of this disorder.
Patellofemoral pain in athletes: clinical perspectives

Patellofemoral pain (PFP) is a very common problem in athletes who participate in jumping, cutting and pivoting sports. Several risk factors may play a part in the pathogenesis of PFP. Overuse, trauma and intrinsic risk factors are particularly important among athletes. Physical examination has a key role in PFP diagnosis. Furthermore, common risk factors should be investigated, such as hip muscle dysfunction, poor core muscle endurance, muscular tightness, excessive foot pronation and patellar malalignment. Imaging is seldom needed in special cases. Many possible interventions are recommended for PFP management. Due to the multifactorial nature of PFP, the clinical approach should be individualized, and the contribution of different factors should be considered and managed accordingly. In most cases, activity modification and rehabilitation should be tried before any surgical interventions.
The cardiovascular disease continuum validated: clinical evidence of improved patient outcomes: part I: Pathophysiology and clinical trial evidence (risk factors through stable coronary artery disease).

Fifteen years ago, a panel of experts representing the full spectrum of cardiovascular disease (CVD) research and practice assembled at a workshop to examine the state of knowledge about CVD. The leaders of the workshop generated a hypothesis that framed CVD as a chain of events, initiated by a myriad of related and unrelated risk factors and progressing through numerous physiological pathways and processes to the development of end-stage heart disease (Figure 1).1 They further hypothesized that intervention anywhere along the chain of events leading to CVD could disrupt the pathophysiological process and confer cardioprotection. The workshop participants endorsed this paradigm but also identified the unresolved issues relating to the concept of a CVD continuum. There was limited availability of clinical trial data and pathobiological evidence at that time, and the experts recognized that critical studies at both the mechanistic level and the clinical level were needed to validate the concept of a chain of events leading to end-stage CVD. In the intervening 15 years, new evidence for underlying pathophysiological mechanisms, the development of novel therapeutic agents, and the release of additional landmark clinical trial data have confirmed the concept of a CVD continuum and reinforced the notion that intervention at any point along this chain can modify CVD progression. In addition, the accumulated evidence indicates that the events leading to disease progression overlap and intertwine and do not always occur as a sequence of discrete, tandem incidents. Furthermore, although the original concept focused on risk factors for coronary artery disease (CAD) and its sequelae, the CVD continuum has expanded to include other areas such as cerebrovascular disease, peripheral vascular disease, and renal disease. Since its conception 15 years ago, the CVD continuum has become much in need of an update. Accordingly, this 2-part article will present a critical and comprehensive update of the current evidence for a CVD continuum based on the results of pathophysiological studies and the outcome of a broad range of clinical trials that have been performed in the past 15 years. It is not the intent of the article to include a comprehensive listing of all trials performed as part of the CVD continuum; instead, we have sought to include only those trials that have had the greatest impact. Part I briefly reviews the current understanding of the pathophysiology of CVD and discusses clinical trial data from risk factors for disease through stable CAD. Part II continues the review of clinical trial data beginning with acute coronary syndromes and continuing through extension of the CVD continuum to stroke and renal disease. The article concludes with a discussion of areas in which future research might further clarify our understanding of the CVD continuum.
Ethical Hacking : The Security Justification

The state of security on the Internet is poor and progress toward increased protection is slow. This has given rise to a class of action referred to as “Ethical Hacking”. Companies are releasing software with little or no testing and no formal verification and expecting consumers to debug their product for them. For dot.com companies time-to-market is vital, security is not perceived as a marketing advantage, and implementing a secure design process an expensive sunk expense such that there is no economic incentive to produce bug-free software. There are even legislative initiatives to release software manufacturers from legal responsibility to their defective software.
Component-based software engineering - new challenges in software development

The primary role of component-based software engineering is to address the development of systems as an assembly of parts components , the development of parts as reusable entities, and the maintenance and upgrading of systems by customising and replacing such parts. This requires established methodologies and tool support covering the entire component and system lifecycle including technological, organisational, marketing, legal, and other aspects. The traditional disciplines from software engineering need new methodologies to support component-based development.
Fiber Optic Sensors

Fiber optic sensor technology offers the possibility of sensing different parameters like strain, temperature, pressure in harsh environment and remote locations. these kinds of sensors modulates some features of the light wave in an optical fiber such an intensity and phase or use optical fiber as a medium for transmitting the measurement information. The advantages of fiber optic sensors in contrast to conventional electrical ones make them popular in different applications and now a day they consider as a key component in improving industrial processes, quality control systems, medical diagnostics, and preventing and controlling general process abnormalities. This paper is an introduction to fiber optic sensor technology and some of the applications that make this branch of optic technology, which is still in its early infancy, an interesting field. Fiber optic sensors, distributed sensors, sensor application, crack sensor.
Building information modelling (BIM) implementation and remote construction projects: Issues, Challenges and Critiques

The construction industry has been facing a paradigm shift to (i) increase productivity, efficiency, infrastructure value; quality and sustainability (ii) reduce lifecycle costs, lead times and duplications via effective collaboration and communication of stakeholders in construction projects. This paradigm shift is becoming more critical with remote construction projects, which reveals unique and even more complicated challenging problems in relation to communication and management due to the remoteness of the construction sites. On the other hand, Building Informational Modelling (BIM) is offered by some as the panacea to addressing the interdisciplinary inefficiencies in construction projects. Although in many cases the adoption of BIM has numerous potential benefits, it also raises interesting challenges with regards to how BIM integrates the business processes of individual practices. This paper aims to show how BIM adoption for an architectural company helps to mitigate the management and communication problems in remote construction project. The paper adopts a case study methodology, which is a UK Knowledge Transfer Partnership (KTP) project of BIM adoption between the University of Salford, UK and John McCall Architects (JMA), in which the BIM use between the architectural company and the main contractor for a remote construction project is elaborated and justified. Research showed that the key management and communication problems such as poor quality of construction works, unavailability of materials, and ineffective planning and scheduling can largely be mitigated by adopting BIM at the design stage.
Stability analysis of swarms

Swarming, or aggregations of organisms in groups, can be found in nature in many organisms ranging from simple bacteria to mammals. Such behavior can result from several different mechanisms. For example, individuals may respond directly to local physical cues such as concentration of nutrients or distribution of some chemicals as seen in some bacteria and social insects, or they may respond directly to other individuals as seen in fish, birds, and herds of mammals. In this dissertation, we consider models for aggregating and social foraging swarms and perform rigorous stability analysis of emerging collective behavior. Moreover, we consider formation control of a general class of multi-agent systems in the framework of nonlinear output regulation problem with application on formation control of mobile robots. First, an individual-based continuous time model for swarm aggregation in an n-dimensional space is identified and its stability properties are analyzed. The motion of each individual is determined by two factors: (i) attraction to the other individuals on long distances and (ii) repulsion from the other individuals on short distances. It is shown that the individuals (autonomous agents or biological creatures) will form a cohesive swarm in a finite time. Moreover, explicit bounds on the swarm size and time of convergence are derived. Then, the results are generalized to a more general class of attraction/repulsion functions and extended to handle formation stabilization and uniform swarm density. After that, we consider social foraging swarms. We ii assume that the swarm is moving in an environment with an ”attractant/repellent” profile (i.e., a profile of nutrients or toxic substances) which also affects the motion of each individual by an attraction to the more favorable or nutrient rich regions (or repulsion from the unfavorable or toxic regions) of the profile. The stability properties of the collective behavior of the swarm for different profiles are studied and conditions for collective convergence to more favorable regions are provided. Then, we use the ideas for modeling and analyzing the behavior of honey bee clusters and in-transit swarms, a phenomena seen during the reproduction of the bees. After that, we consider one-dimensional asynchronous swarms with time delays. We prove that, despite the asynchronism and time delays in the motion of the individuals, the swarm will converge to a comfortable position with comfortable intermember spacing. Finally, we consider formation control of a multi-agent system with general nonlinear dynamics. It is assumed that the formation is required to follow a virtual leader whose dynamics are generated by an autonomous neutrally stable system. We develop a decentralized control strategy based on the nonlinear output regulation (servomechanism) theory. We illustrate the procedure with application to formation control of mobile robots.