nils-herrmann/clean_scirepeval huggingface dataset:
Support vector machine active learning for image retrieval

Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user's desired output or query concept by asking the user whether certain proposed images are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval. The algorithm selects the most informative images to query a user and quickly learns a boundary that separates the images that satisfy the user's query concept from the rest of the dataset. Experimental results show that our algorithm achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
A very large Brazilian pedigree with 11778 Leber's hereditary optic neuropathy.

We conducted extensive epidemiological, neuro-ophthalmological, psychophysical, and blood examinations on a newly discovered, very large pedigree with molecular analysis showing mtDNA mutation for Leber's hereditary optic neuropathy (LHON).



Four patients representing four index cases from a remote area of Brazil were sent to Sao Paulo, where complete ophthalmological examinations strongly suggested LHON. Molecular analysis of their blood demonstrated that they were LHON, homoplasmic 11778, J-haplogroup. They had an extensive family that all lived in one rural area in Brazil. To investigate this family, we drew on a number of international experts to form a team that traveled to Brazil. This field team also included several members of the Federal University of Sao Paulo, and together we evaluated 273 of the 295 family members that were still alive. We conducted epidemiological interviews emphasizing possible environmental risk factors, comprehensive neuro-ophthalmological examinations, psychophysical tests, Humphrey visual field studies, fundus photography, and blood testing for both mitochondrial genetic analysis and nuclear gene linkage analysis.



The person representing the first-generation case immigrated from Verona, Italy, to Colatina. Subsequent generations demonstrated penetrance rates of 71%, 60%, 34%, 15%, and 9%. The percentages of males were 60%, 50%, 64%, 100%, and 100%. Age at onset varied from 10 to 64 years, and current visual acuities varied from LP to 20/400.



Almost 95% of a nearly 300-member pedigree with LHON 11778 were comprehensively studied. Analysis of environmental risk factors and a nuclear modifying factor from this group may help address the perplexing mystery of LHON: Why do only some of the genetically affected individuals manifest the disease? This fully described database may also provide an excellent opportunity for future clinical trials of any purported neuroprotective agent.
A 5–6 GHz 1-V CMOS Direct-Conversion Receiver With an Integrated Quadrature Coupler

This paper describes a novel monolithic low voltage (1-V) CMOS RF front-end architecture with an integrated quadrature coupler (QC) and two subharmonic mixers for direct-down conversion. The LC-folded-cascode technique is adopted to achieve low-voltage operation while the subharmonic mixers in conjunction with the QC are used to eliminate LO self-mixing. In addition, the inherent bandpass characteristic of the LC tanks helps suppression of LO leakage at RF port. The circuit was fabricated in a standard 0.18-mum CMOS process for 5-6 GHz applications. At 5.4 GHz, the RF front-end exhibits a voltage gain of 26.2 dB and a noise figure of 5.2 dB while dissipating 45.5 mW from a 1.0-V supply. The achieved input-referred DC-offset due to LO self-mixing is below -110.7 dBm.
Chemotypical variation in Vanilla planifolia Jack. (Orchidaceae) from the Puebla-Veracruz Totonacapan region

One of the threats in the diversity loss of the primary gene pool of Vanilla planifolia is the lack of information on existing level of polymorphism in cultivated germplasm, and the different expressions of this polymorphism. For this reason, it is proposed to study the chemical polymorphism of the four phytochemicals that define the vanilla aroma quality in fruits (vanillin, vanillic acid, p-hydroxybenzaldehyde, p-hydroxybenzoic acid) by HPLC analysis (High Performance Liquid Chromatography) of 25 collections of unknown genotype, grown in the region Totonacapan Puebla-Veracruz, Mexico. The results identified a selection process, domestication in fruit aroma of vanilla, during which increased the participation of vanillin and reduced the presence of three minor compounds (vanillic acid, p-hydroxybenzaldehyde and p-hydroxybenzoic acid) in the global aroma. We distinguished a total of six chemotypes of V. planifolia in the Totonacapan region, some chemotypes with wild aromatic characteristics (low participation of vanillin) related to the material less cultivated in the region and domesticated chemotypes with high participation of vanillin, for the most cultivated material. The results show that the diversification of the chemotypes of V. planifolia is not related to environmental variation. The data indicate that in the possible center of origin of vanilla, there is phytochemical polymorphism, which indirectly suggests the existence of genetic polymorphism, essential for the design of a breeding program for optimizing the use and conservation of diversity of the primary gene pool of Vanilla planifolia.
Digital Nations – Smart Cities, Innovation, and Sustainability

Indian Railway Catering and Tourism Corporation Ltd. (IRCTC) launched “IRCTC Connect” mobile application (app) for different mobile platforms for booking/cancellation tickets, but the app usage rate is very low in comparison to IRCTC website and Passenger Reservation System (PRS). This indicates a gap between implementation and adaption of IRCTC Connect. This paper explores the factors influencing the consumer’s behavioral intention to use IRCTC Connect by adapting Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Regression analysis is used to analyze total 159 valid responses, collected through survey at MNNIT campus Allahabad, India. The findings of the study illustrate that only three factors Social Influence, Price Value and Habit of UTAUT2 model are significantly influencing the adoption of IRCTC Connect with adjusted R-Square value 0.699. This study will facilitate IRCTC Connect developers to encompass better understanding on consumers’ desires and intention and encourages researchers in this area for longitudinal observation in different backgrounds.
Efficient multitasking: parallel versus serial processing of multiple tasks

In the context of performance optimizations in multitasking, a central debate has unfolded in multitasking research around whether cognitive processes related to different tasks proceed only sequentially (one at a time), or can operate in parallel (simultaneously). This review features a discussion of theoretical considerations and empirical evidence regarding parallel versus serial task processing in multitasking. In addition, we highlight how methodological differences and theoretical conceptions determine the extent to which parallel processing in multitasking can be detected, to guide their employment in future research. Parallel and serial processing of multiple tasks are not mutually exclusive. Therefore, questions focusing exclusively on either task-processing mode are too simplified. We review empirical evidence and demonstrate that shifting between more parallel and more serial task processing critically depends on the conditions under which multiple tasks are performed. We conclude that efficient multitasking is reflected by the ability of individuals to adjust multitasking performance to environmental demands by flexibly shifting between different processing strategies of multiple task-component scheduling.
Conditional Variational Autoencoder for Neural Machine Translation

We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). Similar to (Zhang et al., 2016), we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. We extend this model with a co-attention mechanism motivated by (Parikh et al., 2016) in the inference network. Compared to the vision domain, latent variable models for text face additional challenges due to the discrete nature of language, namely posterior collapse (Bowman et al., 2015). We experiment with different approaches to mitigate this issue. We show that our conditional variational model improves upon both discriminative attention-based translation and the variational baseline presented in (Zhang et al., 2016). Finally, we present some exploration of the learned latent space to illustrate what the latent variable is capable of capturing. This is the first reported conditional variational model for text that meaningfully utilizes the latent variable without weakening the translation model.
Color Constancy by Deep Learning

Computational color constancy aims to estimate the color of the light source. The performance of many vision tasks, such as object detection and scene understanding, may benefit from color constancy by using the corrected object colors. Since traditional color constancy methods are based on specific assumptions, none of those methods can be used as a universal predictor. Further, shallow learning schemes are used for trainingbased color constancy, possibly suffering from limited learning capabilities. In this paper, we propose a new framework using Deep Neural Networks (DNNs) to obtain accurate light source estimation. We reformulate color constancy as a DNN-based regression approach to estimate the color of the light source. The model is trained using datasets of more than a million images. Experiments show that the proposed algorithm outperforms the state-of-the-art by 9%. Especially in cross dataset validation, our approach reduces the median angular error by 35%. Our algorithm operates at more than 100 fps during testing.
Components, Frameworks, Patterns

Frameworks are an object-oriented reuse technique that are widely used in industry but not discussed much by the software engineering research community. They are a way of reusing design that is part of the reason that some object-oriented developers are so productive. This paper compares and contrasts frameworks with other reuse techniques, and describes how to use them, how to evaluate them, and how to develop them. It describe the tradeo s involved in using frameworks, including the costs and pitfalls, and when frameworks are appropriate.
Multi-label classification search space in the MEKA software

This technical report describes the multi-label classification (MLC) search space in the MEKA software, including the traditional/meta MLC algorithms, and the traditional/meta/preprocessing single-label classification (SLC) algorithms. The SLC search space is also studied because is part of MLC search space as several methods use problem transformation methods to create a solution (i.e., a classifier) for a MLC problem. This was done in order to understand better the MLC algorithms. Finally, we propose a grammar that formally expresses this understatement.
Three Perspectives for Evaluating Human-Robot Interaction

The experience of interacting with a robot has been shown to be very different in comparison to people’s interaction experience with other technologies and artifacts, and often has a strong social or emotional component – a fact that raises concerns related to evaluation. In this paper we outline how this difference is due in part to the general complexity of robots’ overall context of interaction, related to their dynamic presence in the real world and their tendency to invoke a sense of agency. A growing body of work in Human-Robot Interaction (HRI) focuses on exploring this overall context and tries to unpack what exactly is unique about interaction with robots, often through leveraging evaluation methods and frameworks designed for more-traditional HCI. We raise the concern that, due to these differences, HCI evaluation methods should be applied to HRI with care, and we present a survey of HCI evaluation techJames E. Young University of Calgary, Canada, The University of Tokyo, Japan E-mail: jim.young@ucalgary.ca JaYoung Sung Georgia Institute of Technology, GA, U.S.A. E-mail: jsung@cc.gatech.edu Amy Voida University of Calgary, Canada E-mail: avoida@ucalgary.ca Ehud Sharlin University of Calgary, Canada E-mail: ehud@cpsc.ucalgary.ca Takeo Igarashi The University of Tokyo, Japan, JST ERATO, Japan E-mail: takeo@acm.org Henrik I. Christensen Georgia Institute of Technology, GA, U.S.A.  hic@cc.gatech.edu Rebecca E. Grinter Georgia Institute of Technology, GA, U.S.A. E-mail: beki@cc.gatech.edu niques from the perspective of the unique challenges of robots. Further, we have developed a new set of tools to aid evaluators in targeting and unpacking the holistic human-robot interaction experience. Our technique surrounds the development of a map of interaction experience possibilities and, as part of this, we present a set of three perspectives for targeting specific components of interaction experience, and demonstrate how these tools can be practically used in evaluation. Models and principles]: user/machine systems–software psychology
A ball tracking framework for broadcast soccer video

It is challenging to detect and track the ball from the broadcast soccer video. The feature-based tracking methods to judge if a sole object is a target are inadequate because the features of the balls change fast over frames and we cannot differ the ball from other objects by them. This paper proposes a new framework to find the ball position by creating and analyzing the trajectory. The ball trajectory is obtained from the candidate collection by use of the heuristic false candidate reduction, the Kalman filterbased trajectory mining, and the trajectory evaluation. The ball trajectory is extended via a localized Kalman filter-based model matching procedure. The experimental results on two consecutive 1000-frame sequences illustrate that the proposed framework is very effective and can obtain a very high accuracy that is much better than existing methods.
‘Primitive intelligence’ in the auditory cortex

The everyday auditory environment consists of multiple simultaneously active sources with overlapping temporal and spectral acoustic properties. Despite the seemingly chaotic composite signal impinging on our ears, the resulting perception is of an orderly "auditory scene" that is organized according to sources and auditory events, allowing us to select messages easily, recognize familiar sound patterns, and distinguish deviant or novel ones. Recent data suggest that these perceptual achievements are mainly based on processes of a cognitive nature ("sensory intelligence") in the auditory cortex. Even higher cognitive processes than previously thought, such as those that organize the auditory input, extract the common invariant patterns shared by a number of acoustically varying sounds, or anticipate the auditory events of the immediate future, occur at the level of sensory cortex (even when attention is not directed towards the sensory input).
Simple QPSK Modulator Implemented in Virtex 6 FPGA Board for Satellite Ground Station

Modulation is a key feature commonly used in wireless communication for data transmission and to minimize antenna design. QPSK (Quadrature Phase Shift Keying) is one type of digital modulation technique used to transfer the baseband data wirelessly in much efficient way compare to other modulation techniques. Conventional QPSK modulator operates by separation of baseband data into i and q phases and then add them to produce QPSK signal. The process of generating sine and cosine carrier wave to produce the i and q phases consume high power. For better efficiency in power consumption and area utilization, 2 new types of QPSK modulator proposed. The proposed method will eliminate the generation of 2 phases and will produce the QPSK output based on stored data in RAM. Verilog HDL used to implement the proposed QPSK modulators and it has been successfully simulated on Xilinx ISE 12.4 software platform. a comparision has been made with existing modulator and significant improvement can be seen in term of area and power consumption.
Ultra-wideband multi-section power divider on suspended stripline

In this paper, a design method of an ultra-wideband multi-section power divider on suspended stripline (SSL) is presented. A clear design guideline for ultra-wideband power dividers is provided. As a design example, a 10-section SSL power divider is implemented. The fabricated divider exhibits the minimum insertion loss of 0.3 dB, the maximum insertion loss of 1.5 dB from 1 to 19 GHz. The measured VSWR is typically 1.40:1, and the isolation between output-port is typically 20 dB.
A virtual EXV mass flow sensor for applications with two-phase flow inlet conditions

In conventional vapor compression systems, electronic expansion valves (EXVs) are used for refrigerant flow control. Subcooled refrigerant enters the expansion device and is expanded to the evaporation pressure while the valve opening is modified to achieve the desired mass flowrate. The relationship between the inlet and outlet conditions, the opening, and the mass flowrate has been extensively studied, e.g. by Park et al. (2007) and appropriate empirical correlations have been developed. However, for certain operating conditions (e.g. low refrigerant charge) or applications that generally have two-phase inlet conditions (e.g. balancing valves used in a hybrid control scheme as proposed by Kim et al. (2008)), these correlations are not applicable, since even low inlet vapor fractions lead to a significant reduction of the valve mass flowrate at a given opening. This paper proposes a continuous correlation that can be used for both two-phase and subcooled valve inlet conditions. The benefit of the continuity is that there is a smooth transition between subcooled and two-phase inlet conditions, which is essential for control and simulation purposes. The new correlation employs the Buckingham-Pi theorem as proposed by Buckingham (1914). The selected dimensionless Pi-groups describe opening of the valve, subcooling, inlet and outlet pressures, driving pressure difference across the valve, inlet density, surface tension, and viscosity. The data that was used to determine the coefficients of the correlation was taken on a dedicated valve test stand, which was sized for the per-circuit capacity of a typical 5-ton R410A heat pump and a 3-ton R404A large room cooling system. The purpose of these tests was mainly to map the valves for the low pressure drops, high inlet qualities and large valve openings that occur when they are used as balancing valves in a hybrid control approach. Two commercially available valves of different rated capacity were tested. Due to the much higher valve capacity for subcooled inlet conditions, valve openings of less than 5% occurred in that case. This led to an accuracy of the correlation for these points that is less than what typically can be found for correlations with subcooled inlet conditions in the open literature. However, for two-phase flow inlet conditions, the resulting RMS of 1.0 g/s for the 8-PI correlation is sufficiently small to use the approach for estimating the refrigerant mass flow and using the EXV as a virtual flow sensor. The limitations of this approach in practical applications, as well as possible applications in fault detection and diagnostics are shown for application as balancing valves within a 5-ton R410A heat pump and a 3-ton R404A large room cooling system.
Treatment, Therapy and Management of Metabolic Epilepsy: A Systematic Review

Metabolic epilepsy is a metabolic abnormality which is associated with an increased risk of epilepsy development in affected individuals. Commonly used antiepileptic drugs are typically ineffective against metabolic epilepsy as they do not address its root cause. Presently, there is no review available which summarizes all the treatment options for metabolic epilepsy. Thus, we systematically reviewed literature which reported on the treatment, therapy and management of metabolic epilepsy from four databases, namely PubMed, Springer, Scopus and ScienceDirect. After applying our inclusion and exclusion criteria as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we reviewed a total of 43 articles. Based on the reviewed articles, we summarized the methods used for the treatment, therapy and management of metabolic epilepsy. These methods were tailored to address the root causes of the metabolic disturbances rather than targeting the epilepsy phenotype alone. Diet modification and dietary supplementation, alone or in combination with antiepileptic drugs, are used in tackling the different types of metabolic epilepsy. Identification, treatment, therapy and management of the underlying metabolic derangements can improve behavior, cognitive function and reduce seizure frequency and/or severity in patients.
Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes

Motivated by the recent success in applying deep learning for natural image analysis, we designed an image segmentation system based on deep Convolutional Neural Network (CNN) to detect the presence of soft tissue sarcoma from multi-modality medical images, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Positron Emission Tomography (PET). Multi-modality imaging analysis using deep learning has been increasingly applied in the field of biomedical imaging and brought unique value to medical applications. However, it is still challenging to perform the multi-modal analysis owing to a major difficulty that is how to fuse the information derived from different modalities. There exist varies of possible schemes which are application-dependent and lack of a unified framework to guide their designs. Aiming at lesion segmentation with multi-modality images, we innovatively propose a conceptual image fusion architecture for supervised biomedical image analysis. The architecture has been optimized by testing different fusion schemes within the CNN structure, including fusing at the feature learning level, fusing at the classifier level, and the fusing at the decision-making level. It is found from the results that while all the fusion schemes outperform the single-modality schemes, fusing at the feature level can generally achieve the best performance in terms of both accuracy and computational cost, but can also suffer from the decreased robustness due to the presence of large errors in one or more image modalities.
Shadow Suppression using RGB and HSV Color Space in Moving Object Detection

Video-surveillance and traffic analysis systems can be heavily improved using vision-based techniques to extract, manage and track objects in the scene. However, problems arise due to shadows. In particular, moving shadows can affect the correct localization, measurements and detection of moving objects. This work aims to present a technique for shadow detection and suppression used in a system for moving visual object detection and tracking. The major novelty of the shadow detection technique is the analysis carried out in the HSV color space to improve the accuracy in detecting shadows. This paper exploits comparison of shadow suppression using RGB and HSV color space in moving object detection and results in this paper are more encouraging using HSV colour space over RGB colour space.  Shadow detection; HSV color space; RGB color space.