nils-herrmann/clean_scirepeval huggingface dataset:
A smart hydroponics farming system using exact inference in Bayesian network

Smart farming is seen to be the future of agriculture as it produces higher quality of crops by making farms more intelligent in sensing its controlling parameters. Analyzing massive amount of data can be done by accessing and connecting various devices with the help of Internet of Things (IoT). However, it is not enough to have an Internet support and self-updating readings from the sensors but also to have a self-sustainable agricultural production with the use of analytics for the data to be useful. This study developed a smart hydroponics system that is used in automating the growing process of the crops using exact inference in Bayesian Network (BN). Sensors and actuators are installed in order to monitor and control the physical events such as light intensity, pH, electrical conductivity, water temperature, and relative humidity. The sensor values gathered were used to build the Bayesian Network in order to infer the optimum value for each parameter. A web interface is developed wherein the user can monitor and control the farm remotely via the Internet. Results have shown that the fluctuations in terms of the sensor values were minimized in the automatic control using BN as compared to the manual control. The yielded crop on the automatic control was 66.67% higher than the manual control which implies that the use of exact inference in BN aids in producing high-quality crops. In the future, the system can use higher data analytics and longer data gathering to improve the accuracy of inference.
The Role of Immunotherapy in Multiple Myeloma

Multiple myeloma is the second most common hematologic malignancy. The treatment of this disease has changed considerably over the last two decades with the introduction to the clinical practice of novel agents such as proteasome inhibitors and immunomodulatory drugs. Basic research efforts towards better understanding of normal and missing immune surveillence in myeloma have led to development of new strategies and therapies that require the engagement of the immune system. Many of these treatments are under clinical development and have already started providing encouraging results. We, for the second time in the last two decades, are about to witness another shift of the paradigm in the management of this ailment. This review will summarize the major approaches in myeloma immunotherapies.
Image-based air quality analysis using deep convolutional neural network

Air pollution may cause many severe diseases. An efficient air quality monitoring system is of great benefit for human health and air pollution control. In this paper, we study image-based air quality analysis, in particular, the concentration estimation of particulate matter with diameters less than 2.5 micrometers (PM2.5). The proposed method uses a deep Convolutional Neural Network (CNN) to classify natural images into different categories based on their PM2.5 concentrations. In order to evaluate the proposed method, we created a dataset that contains total 591 images taken in Beijing with corresponding PM2.5 concentrations. The experimental results demonstrate that our method are valid for image-based PM2.5 concentration estimation.
The use and effect of video game design theory in the creation of game-based systems for upper limb stroke rehabilitation

Upper limb exercise is often neglected during post-stroke rehabilitation. Video games have been shown to be useful in providing environments in which patients can practise repetitive, functionally meaningful movements, and in inducing neuroplasticity. The design of video games is often focused upon a number of fundamental principles, such as reward, goals, challenge and the concept of meaningful play, and these same principles are important in the design of games for rehabilitation. Further to this, there have been several attempts for the strengthening of the relationship between commercial game design and rehabilitative game design, the former providing insight into factors that can increase motivation and engagement with the latter. In this article, we present an overview of various game design principles and the theoretical grounding behind their presence, in addition to attempts made to utilise these principles in the creation of upper limb stroke rehabilitation systems and the outcomes of their use. We also present research aiming to move the collaborative efforts of designers and therapists towards a model for the structured design of these games and the various steps taken concerning the theoretical classification and mapping of game design concepts with intended cognitive and motor outcomes.
A review of methods for automatic understanding of natural language mathematical problems

This article addresses the problem of understanding mathematics described in natural language. Research in this area dates back to early 1960s. Several systems have so far been proposed to involve machines to solve mathematical problems of various domains like algebra, geometry, physics, mechanics, etc. This correspondence provides a state of the art technical review of these systems and approaches proposed by different research groups. A unified architecture that has been used in most of these approaches is identified and differences among the systems are highlighted. Significant achievements of each method are pointed out. Major strengths and weaknesses of the approaches are also discussed. Finally, present efforts and future trends in this research area are presented.
Change-driven model transformations

In this paper, we investigate change-driven model transformations, a novel class of transformations, which are directly triggered by complex model changes carried out by arbitrary transactions on the model (e.g. editing operation, transformation, etc). After a classification of relevant change scenarios, we identify challenges for change-driven transformations. As the main technical contribution of the current paper, we define an expressive, high-level language for specifying change-driven transformations as an extension of graph patterns and graph transformation rules. This language generalizes previous results on live model transformations by offering trigger events for arbitrarily complex model changes, and dedicated reactions for specific kinds of changes, making this way the concept of change to be a first-class citizen of the transformation language. We discuss how the underlying transformation engine needs to be adapted in order to use the same language uniformly for different change scenarios. The technicalities of our approach will be discussed on a (1) model synchronization case study with non-materialized target models and (2) a case study on detecting the violation of evolutionary (temporal) constraints in the security requirements engineering domain.
Predicting the functions of proteins in Protein-Protein Interaction networks from global information

In this work we present a novel approach to predict the function of proteins in protein-protein interaction (PPI) networks. We classify existing approaches into inductive and transductive approaches, and into local and global approaches. As of yet, among the group of inductive approaches, only local ones have been proposed for protein function prediction. We here introduce a protein description formalism that also includes global information, namely information that locates a protein relative to specific important proteins in the network. We analyze the effect on function prediction accuracy of selecting a different number of important proteins. With around 70 important proteins, even in large graphs, our method makes good and stable predictions. Furthermore, we investigate whether our method also classifies proteins accurately on more detailed function levels. We examined up to five different function levels. The method is benchmarked on four datasets where we found classification performance according to F-measure values indeed improves by 9 percent over the benchmark methods employed.
The genetic epidemiology of phobias in women. The interrelationship of agoraphobia, social phobia, situational phobia, and simple phobia.

In 2163 personally interviewed female twins from a population-based registry, the pattern of age at onset and comorbidity of the simple phobias (animal and situational)--early onset and low rates of comorbidity--differed significantly from that of agoraphobia--later onset and high rates of comorbidity. Consistent with an inherited "phobia proneness" but not a "social learning" model of phobias, the familial aggregation of any phobia, agoraphobia, social phobia, and animal phobia appeared to result from genetic and not from familial-environmental factors, with estimates of heritability of liability ranging from 30% to 40%. The best-fitting multivariate genetic model indicated the existence of genetic and individual-specific environmental etiologic factors common to all four phobia subtypes and others specific for each of the individual subtypes. This model suggested that (1) environmental experiences that predisposed to all phobias were most important for agoraphobia and social phobia and relatively unimportant for the simple phobias, (2) environmental experiences that uniquely predisposed to only one phobia subtype had a major impact on simple phobias, had a modest impact on social phobia, and were unimportant for agoraphobia, and (3) genetic factors that predisposed to all phobias were most important for animal phobia and least important for agoraphobia. Simple phobias appear to arise from the joint effect of a modest genetic vulnerability and phobia-specific traumatic events in childhood, while agoraphobia and, to a somewhat lesser extent, social phobia result from the combined effect of a slightly stronger genetic influence and nonspecific environmental experiences.
Experimental Validation of a Wideband Metasurface for Wide-Angle Scanning Leaky-Wave Antennas

Beam scanning arrays typically suffer from scan loss; an increasing degradation in gain as the beam is scanned from broadside toward the horizon in any given scan plane. Here, a metasurface is presented that reduces the effects of scan loss for a leaky-wave antenna (LWA). The metasurface is simple, being composed of an ultrathin sheet of subwavelength split-ring resonators. The leaky-wave structure is balanced, scanning from the forward region, through broadside, and into the backward region, and designed to scan in the magnetic plane. The metasurface is effectively invisible at broadside, where balanced LWAs are most sensitive to external loading. It is shown that the introduction of the metasurface results in increased directivity, and hence, gain, as the beam is scanned off broadside, having an increasing effect as the beam is scanned to the horizon. Simulations show that the metasurface improves the effective aperture distribution at higher scan angles, resulting in a more directive main beam, while having a negligible impact on cross-polarization gain. Experimental validation results show that the scan range of the antenna is increased from  $-39 {^{\circ }} \leq \theta \leq +32 {^{\circ }}$  to  $-64 {^{\circ }} \leq \theta \leq +70 {^{\circ }}$ , when loaded with the metasurface, demonstrating a flattened gain profile over a 135° range centered about broadside. Moreover, this scan range occurs over a frequency band spanning from 9 to 15.5 GHz, demonstrating a relative bandwidth of 53% for the metasurface.
Model-based safety assessment: Review of the discipline and its challenges

Since its emergence in 1990s, Model-Based Safety Assessment (MBSA) has enjoyed significant interest from both academia and industry. The last decade has seen not only the development of a number of methods, techniques and tools, but also the gradual adoption of MBSA techniques by industry and its acceptance by regulators. However, the field of MBSA encompasses a large number of fundamentally dissimilar techniques. This paper presents a simple classification schema for MBSA techniques based on two criteria — provenance of the model and engineering semantics of component dependencies captured by the model. The classification organizes the existing techniques into a number of coherent families. Applicability, limitations and challenges of most prominent families of MBSA techniques are presented, and some of the common challenges faced by MBSA discipline are discussed.
Design and Analysis of Meander Microstrip Antenna at Wireless Band

Meander line antenna (MLA) is an electrically small antenna which poses several performance related issues such as narrow bandwidth, high VSWR, low gain and high cross polarization levels. This paper describe the design ,simulation and development of meander line microstrip antenna at wireless band, the antenna was modeled using microstrip lines and S parameter for the antenna was obtained. The properties of the antenna such as bandwidth, beamwidth, gain, directivity, return loss and polarization were obtained.
Face-to-face collaborative learning supported by mobile phones

The use of handheld computers in educational contexts has increased considerably in recent years and their value as a teaching tool has been confirmed by many positive experiences, particular within collaborative learning systems (MCSCL). The cost of the devices has hindered widespread use in schools, however, and cell phones have emerged as an attractive alternative. To test the functionality of cell phones as a platform for collaborative educational activities, the authors adapted an existing PDA application for use on cell phones equipped with Wi-Fi. This article examines the problems of developing applications for this alternative technology and reports on a usability analysis of a collaborative classroom activity for teaching physics. The results confirm the viability of the cell phone platform, with due account taken of the device’s processing, network and interface limitations. With an appropriate design, users quickly master the technology, though a certain decline in efficiency relative to PDAs was observed. This work was partially funded by FONDECYT-CONICYT grant No.
Digitally Designed Stochastic Flash Adc

This paper demonstrates that it is possible to construct the Stochastic flash ADC using standard digital cells. In order to minimize the analog circuit requirements which cost high, it is appropriate to begin the architecture with highly digital. The proposed Stochastic flash ADC uses a random comparator offset to set the trip points. Since the comparator are no longer sized for small offset, they can be shrunk down into digital cells. Using comparators that are implemented as digital cells produces a large variation of comparator offset. Typically, this is considered a disadvantage, but in our case, this large standard deviation of offset is used to set the input signal range. By designing an ADC that is made up entirely of digital cells, it is natural candidate for a synthesizable ADC. The analog comparator which is used in this ADC is constructed from standard digital NAND gates connected with SR latch to minimize the memory effects. A Wallace tree adder is used to sum the total number of comparator output, since the order of comparator output is random. Thus, all the components including the comparator and Wallace tree adder can be implemented using standard digital cells.  As CMOS designs are scaled to smaller technology nodes, many benefits arise, as well as challenges. There are benefits in speed and power due to decreased capacitance and lower supply voltage, yet reduction in intrinsic device gain and lower supply voltage make it difficult to migrate previous analog designs to smaller scaled processes. Moreover, as scaling trends continue, the analog portion of a mixed-signal system tends to consume proportionally more power and area and have a higher design cost than the digital counterpart. This tends to increase the overall design cost of the mixed-signal design. Automatically synthesized digital circuits get all the benefits of scaling, but analog circuits get these benefits at a large cost. The most essential component of ADC is the comparator, which translates from the analog world to digital world. Since comparator defines the boundary between analog and digital realms, the flash ADC architecture will be considered, as it places the comparator as close to the analog input signal. Flash ADCs use a reference ladder to generate the comparator trip points that correspond to each digital code. Typically the references are either generated by a resistor ladder or some form of analog interpolation, but the effect is the same: a …
Putting an “End” to the Motor Cortex Representations of Action Words

Language processing is an example of implicit learning of multiple statistical cues that provide probabilistic information regarding word structure and use. Much of the current debate about language embodiment is devoted to how action words are represented in the brain, with motor cortex activity evoked by these words assumed to selectively reflect conceptual content and/or its simulation. We investigated whether motor cortex activity evoked by manual action words (e.g., caress) might reflect sensitivity to probabilistic orthographic–phonological cues to grammatical category embedded within individual words. We first review neuroimaging data demonstrating that nonwords evoke activity much more reliably than action words along the entire motor strip, encompassing regions proposed to be action category specific. Using fMRI, we found that disyllabic words denoting manual actions evoked increased motor cortex activity compared with non-body-part-related words (e.g., canyon), activity which overlaps that evoked by observing and executing hand movements. This result is typically interpreted in support of language embodiment. Crucially, we also found that disyllabic nonwords containing endings with probabilistic cues predictive of verb status (e.g., -eve) evoked increased activity compared with nonwords with endings predictive of noun status (e.g., -age) in the identical motor area. Thus, motor cortex responses to action words cannot be assumed to selectively reflect conceptual content and/or its simulation. Our results clearly demonstrate motor cortex activity reflects implicit processing of ortho-phonological statistical regularities that help to distinguish a word's grammatical class.
Study of the Spiritual Intelligence Role in Predicting University Students’ Quality of Life

The aim of the study is to investigate the spiritual intelligence role in predicting Quchan University students’ quality of life. In order to collect data, a sample of 143 students of Quechan University was selected randomly enrolled for 89–90 academic year. The instruments of the data collecting are World Health Organization Quality of Life (WHOQOL) and Spiritual Intelligence Questionnaire. For analyzing the data, the standard deviation, and Pearson’s correlation coefficient in descriptive level, and in inferential level, the regression test was used. The results of the study show that the spiritual intelligence has effective role on predicting quality of life.
Generalized Thompson sampling for sequential decision-making and causal inference

Recently, it has been shown how sampling actions from the predictive distribution over the optimal action—sometimes called Thompson sampling—can be applied to solve sequential adaptive control problems, when the optimal policy is known for each possible environment. The predictive distribution can then be constructed by a Bayesian superposition of the optimal policies weighted by their posterior probability that is updated by Bayesian inference and causal calculus. Here we discuss three important features of this approach. First, we discuss in how far such Thompson sampling can be regarded as a natural consequence of the Bayesian modeling of policy uncertainty. Second, we show how Thompson sampling can be used to study interactions between multiple adaptive agents, thus, opening up an avenue of game-theoretic analysis. Third, we show how Thompson sampling can be applied to infer causal relationships when interacting with an environment in a sequential fashion. In summary, our results suggest that Thompson sampling might not merely be a useful heuristic, but a principled method to address problems of adaptive sequential decision-making and causal inference.
Information Security Practices in Latin America: The case of Bolivia

In this paper, we present a social/behavioral study of individual information security practices of internet users in Latin America, specifically presenting the case of Bolivia. The research model uses social cognitive theory in order to explain the individual cognitive factors that influence information security behavior. The model includes individuals’ beliefs about their abilities to competently use computer information security tools and information security awareness in the determination of effective information security practices. The operationalization of constructs that are part of our research model, such as information security practice as the dependent variable, self-efficacy and information security awareness as independent variables , are presented both in Spanish and English. In this study, we offer the analysis of a survey of 255 Internet users from Bolivia who replied to our survey and provided responses about their information security behavior. A discussion about information security awareness and practices is presented.
Metaphor as Structure Mapping : The Relational Shift

GENTNER, DEDRE. Metaphor as Structure Mapping: The Relational Shift. CHILD DEVELOPMENT, 1988, 59, 47-59. The goal of this research is to clarify the development of metaphor by using structure-mapping theory to make distinctions among kinds of metaphors. In particular, it is proposed that children can understand metaphors based on shared object attributes before those based on shared relational structure. This predicts (1) early ability to interpret metaphors based on shared attributes, (2 ) a developmental increase in ability to interpret metaphors based on shared relational structure, and (3) a shift from primarily attributional to primarily relational interpretations for metaphors that can be understood in either way. 2 experiments were performed to test these claims. There were 3 kinds of metaphors, varying in whether the shared information forming the basis for the interpretation was attributional, relational, or both. In Experiment 1, children aged 5-6 and 910 and adults produced interpretations of the 3 types of metaphors. The attributionality and relationality of their interpretations were scored by independent judges. In Experiment 2, children aged 45 and 7-8 and adults chose which of 2 interpretations-relational or attributional-of a metaphor they preferred. In both experiments, relational responding increased significantly with age, but attributional responding did not. These results indicate a developmental shift toward a focus on relational structure in metaphor interpretation.