nils-herrmann/clean_scirepeval huggingface dataset:
Automated classification of malaria parasite species on thick blood film using support vector machine

Malaria is a serious global health problem. It requires fast and effective diagnosis for detecting and classifying the type of infection. Proper treatment should be administered in a timely fashion to prevent an outbreak. Microscopic examination of thick blood films is one of the current standards for malaria diagnosis. However, inspecting a thick blood film is time-consuming and requires experienced technicians. Hence, for developing countries where most cases of malaria occur but microscopy expertise may not be available, a computerized system to aid such diagnosis is desirable. In this paper, an automated classification system operating on digitized images of thick blood film has been developed to classify between Plasmodium falciparum and Plasmodium vivax malaria parasite species. The system is fully automated. It is fast and can be handled by non-experts. We calculate five statistical features - mean, standard deviation, kurtosis, skewness and entropy - from four color channels (green, intensity, saturation, and value) of these images. The features are then projected onto a subspace representing image characteristics from both species. The projected features are used by the support vector machine for classification. It is found that the algorithm has acceptable training error and can classify test images with good accuracy.
Counterfactual domain adversarial training of neural networks

A number of domain adaptation techniques have been developed in classical supervised machine learning to solve the problem of learning under domain shift. Unfortunately, almost no attempt has been made to exploit their potential use for causal inference. In this paper, we present a conceptual framework which leverages on one of these successes by adopting Domain Adversarial of Neural Network (DANN)  and make it work within the context of causal inference. More specifically, we attempt to modify an existing counterfactual prediction framework based on domain adaptation (DA)  with a deep neural network and discrepancy distance measure to enable us instead to perform causal inference in an adversarial way. We achieved this by jointly minimizing the risk in the feature reconstruction and the counterfactual prediction while maximizing the error in discriminating between the distribution of subjects that received intervention and those that received no intervention. This way, a domain invariant yet discriminative model that will generalize well on the counterfactual target distribution is produced.
A fuzzy-AHP multi-criteria decision making model for procurement process

The purpose of this study is to develop a fuzzy-AHP multi-criteria decision making model for procurement process. It aims to measure the procurement performance in the automotive industry. As such measurement of procurement will enable competitive advantage and provide a model for continuous improvement. The rapid growth in the market and the level of competition in the global economy transformed procurement as a strategic issue; which is broader in scope and responsibilities as compared to purchasing. This study reviews the existing literature in procurement performance measurement to identify the key areas of measurement and a hierarchical model is developed with a set of generic measures. In addition, a questionnaire is developed for pair-wise comparison and to collect opinion from practitioners, researchers, managers etc. The relative importance of the measurement criteria are assessed using Analytical Hierarchy Process (AHP) and fuzzy-AHP. The validity of the model is c onfirmed with the results obtained.
Blue light dose–responses of leaf photosynthesis, morphology, and chemical composition of Cucumis sativus grown under different combinations of red and blue light

The blue part of the light spectrum has been associated with leaf characteristics which also develop under high irradiances. In this study blue light dose-response curves were made for the photosynthetic properties and related developmental characteristics of cucumber leaves that were grown at an equal irradiance under seven different combinations of red and blue light provided by light-emitting diodes. Only the leaves developed under red light alone (0% blue) displayed dysfunctional photosynthetic operation, characterized by a suboptimal and heterogeneously distributed dark-adapted F(v)/F(m), a stomatal conductance unresponsive to irradiance, and a relatively low light-limited quantum yield for CO(2) fixation. Only 7% blue light was sufficient to prevent any overt dysfunctional photosynthesis, which can be considered a qualitatively blue light effect. The photosynthetic capacity (A(max)) was twice as high for leaves grown at 7% blue compared with 0% blue, and continued to increase with increasing blue percentage during growth measured up to 50% blue. At 100% blue, A(max) was lower but photosynthetic functioning was normal. The increase in A(max) with blue percentage (0-50%) was associated with an increase in leaf mass per unit leaf area (LMA), nitrogen (N) content per area, chlorophyll (Chl) content per area, and stomatal conductance. Above 15% blue, the parameters A(max), LMA, Chl content, photosynthetic N use efficiency, and the Chl:N ratio had a comparable relationship as reported for leaf responses to irradiance intensity. It is concluded that blue light during growth is qualitatively required for normal photosynthetic functioning and quantitatively mediates leaf responses resembling those to irradiance intensity.
Effects of hydromorphological impacts on river ecosystem functioning: a review and suggestions for assessing ecological impacts

Because of the serious effects of pollution on water supply much closer attention has been paid to water quality than to other aspects of river integrity. However, channel form and water flow are relevant components of river health, and recent evidences show that their impairment threatens the services derived from them. In this article, we review the literature on the effects of common hydromorphological impacts (channel modification and flow modification) on the functioning of river ecosystems. There are evidences that even light hydromorphological impacts can have deep effects on ecosystem functioning, and that different functional variables differ in their responses. Three criteria (relevance, scale and sensitivity) in the selection of functional variables are suggested as a guide for the river scientists and managers to assess the ecological impacts of hydromorphological modifications.
A weight and a meta-analysis on mobile banking acceptance research

Following the globalization of business and systems, there is a pressing need to understand the main factors affecting mobile banking user acceptance. The increasing number of mobile banking studies and articles published in the last years, as well as conferences and workshops, has made the research process on this important subject more complex and time-consuming. Therefore, it is necessary to synthesize findings from existing research, seeking an update of the current state-of-the-art knowledge. A combination of weight and meta-analysis was chosen, in order to identify the frequency and relevance of the most used constructs and their most important relationships. A total of 57 articles were found in the literature, having the necessary quantitative statistical data to be considered. The best predictors of the intention to use the mobile banking services identified, simultaneously significant in the weight and in the meta-analysis, are: (i) attitude, (ii) initial trust, (iii) perceived risk, and (iv) performance expectancy. In terms of use of mobile banking, considering the same assumptions, the best predictors are: (i) intention, and (ii) performance expectancy. Facilitating conditions on attitude, task technology fit on performance expectancy, and performance expectancy on initial trust have the potential to be added to the list of the most important predictors, but they still need additional research. A theoretical model based on our results is presented, providing a means to support future mobile banking acceptance studies.
Towards the High-quality Anime Characters Generation with Generative Adversarial Networks

The automatic generation of anime characters offers an opportunity to bring a custom character into existence without professional skill. Besides, professionals may also take advantages of the automatic generation for inspiration on animation and game character design. however results from existing models [15, 18, 8, 22, 12] on anime image generation are blurred and distorted on an non-trivial frequency, thus generating industry-standard facial images for anime characters remains a challenge. In this paper, we propose a model that produces anime faces at high quality with promising rate of success with three-fold contributions: A clean dataset from Getchu, a suitable DRAGAN[10]-based SRResNet[11]like GAN model, and our general approach to training conditional model from image with estimated tags as conditions. We also make available a public accessible web interface.
Novel LMNA mutations in patients with Emery-Dreifuss muscular dystrophy and functional characterization of four LMNA mutations.

Mutations in LMNA cause a variety of diseases affecting striated muscle including autosomal Emery-Dreifuss muscular dystrophy (EDMD), LMNA-associated congenital muscular dystrophy (L-CMD), and limb-girdle muscular dystrophy type 1B (LGMD1B). Here, we describe novel and recurrent LMNA mutations identified in 50 patients from the United States and Canada, which is the first report of the distribution of LMNA mutations from a large cohort outside Europe. This augments the number of LMNA mutations known to cause EDMD by 16.5%, equating to an increase of 5.9% in the total known LMNA mutations. Eight patients presented with either p.R249W/Q or p.E358K mutations and an early onset EDMD phenotype: two mutations recently associated with L-CMD. Importantly, 15 mutations are novel and include eight missense mutations (p.R189P, p.F206L, p.S268P, p.S295P, p.E361K, p.G449D, p.L454P, and p.W467R), three splice site mutations (c.IVS4 + 1G>A, c.IVS6 - 2A>G, and c.IVS8 + 1G>A), one duplication/in frame insertion (p.R190dup), one deletion (p.Q355del), and two silent mutations (p.R119R and p.K270K). Analysis of 4 of our lamin A mutations showed that some caused nuclear deformations and lamin B redistribution in a mutation specific manner. Together, this study significantly augments the number of EDMD patients on the database and describes 15 novel mutations that underlie EDMD, which will contribute to establishing genotype-phenotype correlations.
Representing Specialized Events with FrameBase

Events of various sorts make up an important subset of the entities relevant not only in knowledge representation but also in natural language processing and numerous other fields and tasks. How to represent these in a homogeneous yet expressive, extensive, and extensible way remains a challenge. In this paper, we propose an approach based on FrameBase, a broad RDFS-based schema consisting of frames and roles. The concept of a frame, which is a very general one, can be considered as subsuming existing definitions of events. This ensures a broad coverage and a uniform representation of various kinds of events, thus bearing the potential to serve as a unified event model. We show how FrameBase can represent events from several different sources and domains. These include events from a specific taxonomy related to organized crime, events captured using schema.org, and events from DBpedia.
Avoiding Latent Variable Collapse With Generative Skip Models

Variational autoencoders (vaes) learn distributions of high-dimensional data. They model data with a deep latent-variable model and then fit the model by maximizing a lower bound of the log marginal likelihood. vaes can capture complex distributions, but they can also suffer from an issue known as "latent variable collapse," especially if the likelihood model is powerful. Specifically, the lower bound involves an approximate posterior of the latent variables; this posterior "collapses" when it is set equal to the prior, i.e., when the approximate posterior is independent of the data.Whilevaes learn good generativemodels, latent variable collapse prevents them from learning useful representations. In this paper, we propose a simple new way to avoid latent variable collapse by including skip connections in our generative model; these connections enforce strong links between the latent variables and the likelihood function. We study generative skip models both theoretically and empirically. Theoretically, we prove that skip models increase the mutual information between the observations and the inferred latent variables. Empirically, we study images (MNIST and Omniglot) and text (Yahoo). Compared to existing VAE architectures, we show that generative skip models maintain similar predictive performance but lead to less collapse and provide more meaningful representations of the data.
Rough Clustering Based Unsupervised Image Change Detection

This paper introduces an unsupervised technique to detect the changed region of multitemporal images on a same reference plane with the help of rough clustering. The proposed technique is a soft-computing approach, based on the concept of rough set with rough clustering and Pawlak’s accuracy. It is less noisy and avoids pre-deterministic knowledge about the distribution of the changed and unchanged regions. To show the effectiveness, the proposed technique is compared with some other approaches. change detection; clustering; rough clustering; rough set
Stability, robustness, vulnerability and resilience of agricultural systems. A review

Global warming and price volatility are increasing uncertainty for the future of agriculture. Therefore, agricultural systems must be sustainable not only under average conditions, but also under extreme changes of productivity, economy, environment and social context. Here, we review four concepts: stability, robustness, vulnerability and resilience. Those concepts are commonly used but are sometimes difficult to distinguish due to the lack of clear boundaries. Here, we clarify the role of these concepts in addressing agronomic issues. Our main findings are as follows: (1) agricultural systems face different types of perturbations, from small and usual perturbations to extreme and unpredictable changes; (2) stability, robustness, vulnerability and resilience have been increasingly applied to analyze the agricultural context in order to predict the system response under changing conditions; (3) the four concepts are distinguished by the nature of the system components and by the type of perturbation studied; (4) assessment methods must be tested under contrasted situations; and (5) the major options allowing system adaptation under extreme and unpredictable changes are the increase of diversity and the increase of the adaptive capacity.
A profound case of linear epidermal nevus in a patient with epidermal nevus syndrome.

Epidermal nevus syndrome is a multi-system disease with a wide spectrum of clinical presentation. Numerous specialists may be required to address its extra cutaneous manifestations.



We report a severe case of epidermal nevus syndrome involving the oral cavity, pharynx, and central nervous system in addition to disfiguring skin lesions.



Dermatologists are in a unique position to first render the diagnosis of epidermal nevus syndrome for young patients and ensure appropriate follow-up.
Secure and authentic communication on existing in-vehicle networks

Because of the increasing number of electronic components, the automotive manufacturers introduced data bus systems to decrease the number of discrete lines. Inside modern vehicles there are several bus systems that are used for communication to provide many safety-relevant functions with direct impact to the vehicle's behaviour. Due to missing security services, these in-car networks are unprotected against malicious attacks. Exemplarily focussing CAN, this article explains that the missing of authenticity and confidentiality are the most important issues concerning security risks for in-car communication. A flexible and adaptive solution using trusted communication groups is presented that enables confidential communication between components of a vehicle and guarantees that only authentic controllers - holding a certificate signed by the manufacturer - are able to be part of these closed communication groups.
Word embeddings for idiolect identification

The term idiolect refers to the unique and distinctive use of language of an individual and it is the theoretical foundation of Authorship Attribution. In this paper we are focusing on learning distributed representations (embeddings) of social media users that reflect their writing style. These representations can be considered as stylistic fingerprints of the authors. We are exploring the performance of the two main flavours of distributed representations, namely embeddings produced by Neural Probabilistic Language models (such as word2vec) and matrix factorization (such as GloVe).
Real-time reinforcement learning by sequential Actor-Critics and experience replay

Actor-Critics constitute an important class of reinforcement learning algorithms that can deal with continuous actions and states in an easy and natural way. This paper shows how these algorithms can be augmented by the technique of experience replay without degrading their convergence properties, by appropriately estimating the policy change direction. This is achieved by truncated importance sampling applied to the recorded past experiences. It is formally shown that the resulting estimation bias is bounded and asymptotically vanishes, which allows the experience replay-augmented algorithm to preserve the convergence properties of the original algorithm. The technique of experience replay makes it possible to utilize the available computational power to reduce the required number of interactions with the environment considerably, which is essential for real-world applications. Experimental results are presented that demonstrate that the combination of experience replay and Actor-Critics yields extremely fast learning algorithms that achieve successful policies for non-trivial control tasks in considerably short time. Namely, the policies for the cart-pole swing-up [Doya, K. (2000). Reinforcement learning in continuous time and space. Neural Computation, 12(1), 219-245] are obtained after as little as 20 min of the cart-pole time and the policy for Half-Cheetah (a walking 6-degree-of-freedom robot) is obtained after four hours of Half-Cheetah time.
A Contour-Based Robust Algorithm for Text Detection in Color Images

Text detection in color images has become an active research area in the past few decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. The proposed algorithm is based on the combination of connected component and texture feature analysis of unknown text region contours. First, we utilize an elaborate color image edge detection algorithm to extract all possible text edge pixels. Connected component analysis is performed on these edge pixels to detect the external contour and possible internal contours of potential text regions. The gradient and geometrical characteristics of each region contour are carefully examined to construct candidate text regions and classify part non-text regions. Then each candidate text region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize each text region to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our proposed algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result. key words: text detection, texture analysis, connected component analysis, region contour, edge detection
Magnetic Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we present a generalization of HMC which exploits non-canonical Hamiltonian dynamics. We refer to this algorithm as magnetic HMC, since in 3 dimensions a subset of the dynamics map onto the mechanics of a charged particle coupled to a magnetic field. We establish a theoretical basis for the use of non-canonical Hamiltonian dynamics in MCMC, and construct a symplectic, leapfrog-like integrator allowing for the implementation of magnetic HMC. Finally, we exhibit several examples where these non-canonical dynamics can lead to improved mixing of magnetic HMC relative to ordinary HMC.
Critical periods in speech perception: new directions.

A continuing debate in language acquisition research is whether there are critical periods (CPs) in development during which the system is most responsive to environmental input. Recent advances in neurobiology provide a mechanistic explanation of CPs, with the balance between excitatory and inhibitory processes establishing the onset and molecular brakes establishing the offset of windows of plasticity. In this article, we review the literature on human speech perception development within the context of this CP model, highlighting research that reveals the interplay of maturational and experiential influences at key junctures in development and presenting paradigmatic examples testing CP models in human subjects. We conclude with a discussion of how a mechanistic understanding of CP processes changes the nature of the debate: The question no longer is, "Are there CPs?" but rather what processes open them, keep them open, close them, and allow them to be reopened.
INXS: Bridging the throughput and energy gap for spiking neural networks

In recent years, multiple neuromorphic architectures have been designed to execute cognitive applications that deal with image and speech analysis. These architectures have followed one of two approaches. One class of architectures is based on machine learning with artificial neural networks. A second class is focused on emulating biology with spiking neuron models, in an attempt to eventually approach the brain's accuracy and energy efficiency. A prominent example of the second class is IBM's TrueNorth processor that can execute large spiking networks on a low-power tiled architecture, and achieve high accuracy on a variety of tasks. However, as we show in this work, there are many inefficiencies in the TrueNorth design. We propose a new architecture, INXS, for spiking neural networks that improves upon the computational efficiency and energy efficiency of the TrueNorth design by 3,129× and 10× respectively. The architecture uses memristor crossbars to compute the effects of input spikes on several neurons in parallel. Digital units are then used to update neuron state. We show that the parallelism offered by crossbars is critical in achieving high throughput and energy efficiency.