nils-herrmann/clean_scirepeval huggingface dataset:
Benefits of genetically modified crops for the poor : household income , nutrition , and health

The potential impacts of genetically modified (GM) crops on income, poverty and nutrition in developing countries continue to be the subject of public controversy. Here, a review of the evidence is given. As an example of a first-generation GM technology, the effects of insect-resistant Bt cotton are analysed. Bt cotton has already been adopted by millions of small-scale farmers, in India, China, and South Africa among others. On average, farmers benefit from insecticide savings, higher effective yields and sizeable income gains. Insights from India suggest that Bt cotton is employment generating and poverty reducing. As an example of a second-generation technology, the likely impacts of beta-carotenerich Golden Rice are analysed from an ex ante perspective. Vitamin A deficiency is a serious nutritional problem, causing multiple adverse health outcomes. Simulations for India show that Golden Rice could reduce related health problems significantly, preventing up to 40,000 child deaths every year. These examples clearly demonstrate that GM crops can contribute to poverty reduction and food security in developing countries. To realise such social benefits on a larger scale requires more public support for research targeted to the poor, as well as more efficient regulatory and technology delivery systems.
Identification of Move Method Refactoring Opportunities

Placement of attributes/methods within classes in an object-oriented system is usually guided by conceptual criteria and aided by appropriate metrics. Moving state and behavior between classes can help reduce coupling and increase cohesion, but it is nontrivial to identify where such refactorings should be applied. In this paper, we propose a methodology for the identification of Move Method refactoring opportunities that constitute a way for solving many common feature envy bad smells. An algorithm that employs the notion of distance between system entities (attributes/methods) and classes extracts a list of behavior-preserving refactorings based on the examination of a set of preconditions. In practice, a software system may exhibit such problems in many different places. Therefore, our approach measures the effect of all refactoring suggestions based on a novel entity placement metric that quantifies how well entities have been placed in system classes. The proposed methodology can be regarded as a semi-automatic approach since the designer will eventually decide whether a suggested refactoring should be applied or not based on conceptual or other design quality criteria. The evaluation of the proposed approach has been performed considering qualitative, metric, conceptual, and efficiency aspects of the suggested refactorings in a number of open-source projects.
Human-Centric Automation and Optimization for Smart Homes

A smart home needs to be human-centric, where it tries to fulfill human needs given the devices it has. Various works are developed to provide homes with reasoning and planning capability to fulfill goals, but most do not support complex sequence of plans or require significant manual effort in devising subplans. This is further aggravated by the need to optimize conflicting personal goals. A solution is to solve the planning problem represented as constraint satisfaction problem (CSP). But CSP uses hard constraints and, thus, cannot handle optimization and partial goal fulfillment efficiently. This paper aims to extend this approach to weighted CSP. Knowledge representation to help in generating planning rules is also proposed, as well as methods to improve performances. Case studies show that the system can provide intelligent and complex plans from activities generated from semantic annotations of the devices, as well as optimization to maximize personal constraints’ fulfillment. Note to Practitioners—Smart home should maximize the fulfillment of personal goals that are often conflicting. For example, it should try to fulfill as much as possible the requests made by both the mother and daughter who wants to watch TV but both having different channel preferences. That said, every person has a set of goals or constraints that they hope the smart home can fulfill. Therefore, human-centric system that automates the loosely coupled devices of the smart home to optimize the goals or constraints of individuals in the home is developed. Automated planning is done using converted services extracted from devices, where conversion is done using existing tools and concepts from Web technologies. Weighted constraint satisfaction that provides the declarative approach to cover large problem domain to realize the automated planner with optimization capability is proposed. Details to speed up planning through search space reduction are also given. Real-time case studies are run in a prototype smart home to demonstrate its applicability and intelligence, where every planning is performed under a maximum of 10 s. The vision of this paper is to be able to implement such system in a community, where devices everywhere can cooperate to ensure the well-being of the community.
Automated Pre-processing Method for Dermoscopic Images and its Application to Pigmented Skin Lesion Segmentation

In this paper, we put forward a new pre–processing scheme for automatic analysis of dermoscopic images. Our contribu tions are two-fold. First, we present a procedure, an extens ion of previous approaches, which succeeds in removing confoun ding factors from dermoscopic images: these include shading ind uce by imaging non-flat skin surfaces and the effect of light-int ensity falloff toward the edges of the dermoscopic image. This proc edure is shown to facilitate the detection and removal of arti f cts such as hairs as well. Second, we present a novel simple yet ef fective greyscale conversion approach that is based on phys ics and biology of human skin. Our proposed greyscale image provides high separability between a pigmented lesion and norm al skin surrounding it. Finally, using our pre–processing sch eme, we perform segmentation based on simple grey-level thresho lding, with results outperforming the state of the art.
Sensory dominance in combinations of audio, visual and haptic stimuli

Participants presented with auditory, visual, or bi-sensory audio–visual stimuli in a speeded discrimination task, fail to respond to the auditory component of the bi-sensory trials significantly more often than they fail to respond to the visual component—a ‘visual dominance’ effect. The current study investigated further the sensory dominance phenomenon in all combinations of auditory, visual and haptic stimuli. We found a similar visual dominance effect also in bi-sensory trials of combined haptic–visual stimuli, but no bias towards either sensory modality in bi-sensory trials of haptic–auditory stimuli. When presented with tri-sensory trials of combined auditory–visual–haptic stimuli, participants made more errors of responding only to two corresponding sensory signals than errors of responding only to a single sensory modality, however, there were no biases towards either sensory modality (or sensory pairs) in the distribution of both types of errors (i.e. responding only to a single stimulus or to pairs of stimuli). These results suggest that while vision can dominate both the auditory and the haptic sensory modalities, it is limited to bi-sensory combinations in which the visual signal is combined with another single stimulus. However, in a tri-sensory combination when a visual signal is presented simultaneously with both the auditory and the haptic signals, the probability of missing two signals is much smaller than of missing only one signal and therefore the visual dominance disappears.
Unusual Vasospastic Angina: A Documented Asymptomatic Spasm with Normal ECG—A Case Report and a Review of the Literature

We report the case of a 53-years-old patient, known to have coronary artery disease, presenting with typical angina at rest with normal ECG and laboratory findings. His angina is relieved by sublingual nitroglycerin. He had undergone a cardiac catheterisation two weeks prior to his presentation for the same complaints. It showed nonsignificant coronary lesions. Another catheterisation was performed during his current admission. He developed coronary spasm during the procedure, still with no ECG changes. The spasm was reversed by administration of 2 mg of intracoronary isosorbide dinitrate. Variant (Prinzmetal's) angina was diagnosed in the absence of electrical ECG changes during pain episodes.
Functional neuroimaging studies of sexual arousal and orgasm in healthy men and women: A review and meta-analysis

In the last fifteen years, functional neuroimaging techniques have been used to investigate the neuroanatomical correlates of sexual arousal in healthy human subjects. In most studies, subjects have been requested to watch visual sexual stimuli and control stimuli. Our review and meta-analysis found that in heterosexual men, sites of cortical activation consistently reported across studies are the lateral occipitotemporal, inferotemporal, parietal, orbitofrontal, medial prefrontal, insular, anterior cingulate, and frontal premotor cortices as well as, for subcortical regions, the amygdalas, claustrum, hypothalamus, caudate nucleus, thalami, cerebellum, and substantia nigra. Heterosexual and gay men show a similar pattern of activation. Visual sexual stimuli activate the amygdalas and thalami more in men than in women. Ejaculation is associated with decreased activation throughout the prefrontal cortex. We present a neurophenomenological model to understand how these multiple regional brain responses could account for the varied facets of the subjective experience of sexual arousal. Further research should shift from passive to active paradigms, focus on functional connectivity and use subliminal presentation of stimuli.
Impact of single layer, double layer and four layer windings on the performance of AFPMSMs

In this paper, the impact of an increased number of layers on the performance of axial flux permanent magnet synchronous machines (AFPMSMs) is studied. The studied parameters are the inductance, terminal voltages, PM losses, iron losses, the mean value of torque, and the ripple torque. It is shown that increasing the number of layers reduces the fundamental winding factor. In consequence, the rated torque for the same current reduces. However, the reduction of harmonics associated with a higher number of layers reduces the ripple torque, PM losses, and iron losses. Besides studying the performance of the AFPMSMs for the rated conditions, the study is broadened for the field weakening (FW) region. During the FW region, the flux of the PMs is weakened by an injection of a reversible d-axis current. This keeps the terminal voltage of the machine fixed at the rated value. The inductance plays an important role in the FW study. A complete study for the FW shows that the two layer winding has the optimum performance compared to machines with an other number of winding layers.
Pareto Rank Learning in Multi-objective Evolutionary Algorithms

In this paper, the interest is on cases where assessing the goodness of a solution for the problem is costly or hazardous to construct or extremely computationally intensive to compute. We label such category of problems as “expensive” in the present study. In the context of multi-objective evolutionary optimizations, the challenge amplifies, since multiple criteria assessments, each defined by an “expensive” objective is necessary and it is desirable to obtain the Pareto-optimal solution set under a limited resource budget. To address this issue, we propose a Pareto Rank Learning scheme that predicts the Pareto front rank of the offspring in MOEAs, in place of the “expensive” objectives when assessing the population of solutions. Experimental study on 19 standard multi-objective benchmark test problems concludes that Pareto rank learning enhanced MOEA led to significant speedup over the state-of-the-art NSGA-II, MOEA/D and SPEA2.
The Berkeley UNIX Consultant Project

UC (UNIX Consultant) is an intelligent, natural-language interface thatallows naive users to learn about the UNIX operating system. UC wasundertaken because the task was thought to be both a fertile domain forArtificial Intelligence research and a useful application of AI work inplanning, reasoning, natural language processing, and knowledgerepresentation. The current implementation of UC comprises the followingcomponents: A language analyzer, called ALANA, that produces arepresentation of the content contained in an utterance; aninference component called a concretion mechanism that furtherrefines this content; a goal analyzer, PAGAN, that hypothesizes theplans and goals under which the user is operating; an agent, calledUCEgo, that decides on UC's goals and proposes plans for them; adomain planner, called KIP, that computes a plan to address the user'srequest; an expression mechanism, UCExpress, that determines thecontent to be communicated to the user, and a language productionmechanism, UCGen, that expresses UC's response in English. UC alsocontains a component called KNOME that builds a model of the user'sknowledge state with respect to UNIX. Another mechanism, UCTeacher,allows a user to add knowledge of both English vocabulary and factsabout UNIX to UC's knowledge base. This is done by interacting with theuser in natural language. All these aspects of UC make use of knowledgerepresented in a knowledge representation system called KODIAK. KODIAKis a relation-oriented system that is intended to have widerepresentational range and a clear semantics, while maintaining acognitive appeal. All of UC's knowledge, ranging from its most generalconcepts to the content of a particular utterance, is represented inKODIAK.
Systematic determination of genetic network architecture

Technologies to measure whole-genome mRNA abundances and methods to organize and display such data are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes. We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo. Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions. The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected cis-regulatory elements. Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast—without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known.
Deep adaptive learning for writer identification based on single handwritten word images

There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author’s identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on singleword images, compared to non-adaptive and simple linear-adaptive approaches.
Pure jump L evy processes for asset price modelling H

The goal of the paper is to show that some types of L evy processes such as the hyperbolic motion and the CGMY are particularly suitable for asset price modelling and option pricing. We wish to review some fundamental mathematic properties of L evy distributions, such as the one of infinite divisibility, and how they translate observed features of asset price returns. We explain how these processes are related to Brownian motion, the central process in finance, through stochastic time changes which can in turn be interpreted as a measure of the economic activity. Lastly, we focus on two particular classes of pure jump L evy processes, the generalized hyperbolic model and the CGMY models, and report on the goodness of fit obtained both on stock prices and option prices.
Neuromorphic speech systems using advanced ReRAM-based synapse

We demonstrate an advanced ReRAM based analog artificial synapse for neuromorphic systems. Nitrogen doped TiN/PCMO based artificial synapse is proposed to improve the performance and reliability of the neuromorphic systems by using simple identical spikes. For the first time, we develop fully unsupervised learning with proposed analog synapses which is illustrated with the help of auditory and electroencephalography (EEG) applications.
A Wireless Magnetoresistive Sensing System for an Intraoral Tongue-Computer Interface

Tongue drive system (TDS) is a tongue-operated, minimally invasive, unobtrusive, and wireless assistive technology (AT) that infers users' intentions by detecting their voluntary tongue motion and translating them into user-defined commands. Here we present the new intraoral version of the TDS (iTDS), which has been implemented in the form of a dental retainer. The iTDS system-on-a-chip (SoC) features a configurable analog front-end (AFE) that reads the magnetic field variations inside the mouth from four 3-axial magnetoresistive sensors located at four corners of the iTDS printed circuit board (PCB). A dual-band transmitter (Tx) on the same chip operates at 27 and 432 MHz in the Industrial/Scientific/Medical (ISM) band to allow users to switch in the presence of external interference. The Tx streams the digitized samples to a custom-designed TDS universal interface, built from commercial off-the-shelf (COTS) components, which delivers the iTDS data to other devices such as smartphones, personal computers (PC), and powered wheelchairs (PWC). Another key block on the iTDS SoC is the power management integrated circuit (PMIC), which provides individually regulated and duty-cycled 1.8 V supplies for sensors, AFE, Tx, and digital control blocks. The PMIC also charges a 50 mAh Li-ion battery with constant current up to 4.2 V, and recovers data and clock to update its configuration register through a 13.56 MHz inductive link. The iTDS SoC has been implemented in a 0.5-μm standard CMOS process and consumes 3.7 mW on average.
A Study on Power Generation Analysis of Floating PV System Considering Environmental Impact

The floating photovoltaic system is a new concept in energy technology to meet the needs of our time. The system integrates existing land based photovoltaic technology with a newly developed floating photovoltaic technology. Because module temperature of floating PV system is lower than that of overland PV system, the floating PV system has 11% better generation efficiency than overland PV system. In the thesis, superiority of floating PV system is verified through comparison analysis of generation amount by 2.4kW, 100kW and 500kW floating PV system installed by K-water and the cause of such superiority was analyzed. Also, effect of wind speed, and waves on floating PV system structure was measured to analyze the effect of the environment on floating PV system generation efficiency.
A Hybrid Approach to Functional Dependency Discovery

Functional dependencies are structural metadata that can be used for schema normalization, data integration, data cleansing, and many other data management tasks. Despite their importance, the functional dependencies of a specific dataset are usually unknown and almost impossible to discover manually. For this reason, database research has proposed various algorithms for functional dependency discovery. None, however, are able to process datasets of typical real-world size, e.g., datasets with more than 50 attributes and a million records. We present a hybrid discovery algorithm called HyFD, which combines fast approximation techniques with efficient validation techniques in order to find all minimal functional dependencies in a given dataset. While operating on compact data structures, HyFD not only outperforms all existing approaches, it also scales to much larger datasets.
Ingested Foreign Bodies of the Gastrointestinal Tract: Retrospective Analysis of 542 Cases

n = 379) were jail inmates at the time of ingestion, 22.9% ( n = 124) had a history of psychosis, and 7.2% ( n = 39) were alcoholics or denture-wearing elderly subjects. Most foreign bodies passed spontaneously (75.6%; n = 410). Endoscopic removal was possible in 19.5% ( n = 106) and was not associated with any morbidity. Only 4.8% ( n = 26) required surgery. Of the latter, 30.8% ( n = 8) had long gastric FBs with no tendency for distal passage and were removed via gastrotomy; 15.4% ( n = 4) had thin, sharp FBs, causing perforation; and 53.8% ( n = 14) had FBs impacted in the ileocecal region, which were removed via appendicostomy. Conservative approach to FB ingestion is justified, although early endoscopic removal from the stomach is recommended. In cases of failure, surgical removal for gastric FBs longer than 7.0 cm is wise. Thin, sharp FBs require a high index of suspicion because they carry a higher risk for perforation. The ileocecal region is the most common site of impaction. Removal of the FB via appendicostomy is the safest option and should not be delayed more than 48 hours.