nils-herrmann/clean_scirepeval huggingface dataset:
Retinal Disease Screening Through Local Binary Patterns

This paper investigates discrimination capabilities in the texture of fundus images to differentiate between pathological and healthy images. For this purpose, the performance of local binary patterns (LBP) as a texture descriptor for retinal images has been explored and compared with other descriptors such as LBP filtering and local phase quantization. The goal is to distinguish between diabetic retinopathy (DR), age-related macular degeneration (AMD), and normal fundus images analyzing the texture of the retina background and avoiding a previous lesion segmentation stage. Five experiments (separating DR from normal, AMD from normal, pathological from normal, DR from AMD, and the three different classes) were designed and validated with the proposed procedure obtaining promising results. For each experiment, several classifiers were tested. An average sensitivity and specificity higher than 0.86 in all the cases and almost of 1 and 0.99, respectively, for AMD detection were achieved. These results suggest that the method presented in this paper is a robust algorithm for describing retina texture and can be useful in a diagnosis aid system for retinal disease screening.
Unsupervised change detection between multi-sensor high resolution satellite images

In this paper, we present a novel unsupervised framework for change detection between two high resolution remote sensing images. Thanks to the use of local descriptors, the method does not need any image co-registration and is able to identify changes even with images acquired from different incidence angles and by different sensors. Local descriptors are used to both locally align images and identify changes. The setting of thresholds as well as the final grouping of isolated changes are performed thanks to a contrario statistical procedures. This provides a complete and automatic pipeline, whose efficiency is shown through several challenging pairs of high resolution urban images, acquired through different satellites.
A 14-bit Hybrid Incremental Sigma-Delta / Cyclic ADC for X-ray Linear Array Sensor

Yuze Niu 1,2, Yacong Zhang 1,2,*, Zhuo Zhang 1,2, Miaomiao Fan 1, Wengao Lu 1,2 and Zhongjian Chen 1,2 1 Key Laboratory of Microelectronic Devices and Circuits, Department of Microelectronics, Peking University, Beijing 100871, China; yzniu@pku.edu.cn (Y.N.); zhangzhuo1658@163.com (Z.Z.); mayunfmm@163.com (M.F.); wglu@pku.edu.cn (W.L.); chenzj@pku.edu.cn (Z.C.) 2 Peking University Information Technology Institute (Tianjin Binhai), Tianjin 300452, China * Correspondence: zhangyc@pku.edu.cn
A Systematic Review of the Use of Requirements Engineering Techniques in Model-Driven Development

Model-Driven Development (MDD) emphasizes the use of models at a higher abstraction level in the software development process and argues in favor of automation via model execution, transformation, and code generation. However, one current challenge is how to manage requirements during this process whilst simultaneously stressing the benefits of automation. This paper presents a systematic review of the current use of requirements engineering techniques in MDD processes and their actual automation level. 72 papers from the last decade have been reviewed from an initial set of 884 papers. The results show that although MDD techniques are used to a great extent in platform-independent models, platform-specific models, and at code level, at the requirements level most MDD approaches use only partially defined requirements models or even natural language. We additionally identify several research gaps such as a need for more efforts to explicitly deal with requirements traceability and the provision of better tool support.
Design and implementation of a street parking system using wireless sensor networks

Recently, with the explosive increase of automobiles in China, people park their cars on streets without following the rules and police has hard time to conduct law enforcement without introducing effective street parking system. To solve the problem, we propose a SPS (street parking system) based on wireless sensor networks. For accurately detecting a parking car, a parking algorithm based on state machine is also proposed. As the vehicle detection in SPS is absolutely critical, we use a Honeywell 3-axis magnetic sensor to detect vehicle. However, the magnetic sensor may be affected by the fluctuation of the outside temperature. To solve the problem, we introduce a moving drift factor. On the parking lot in Shenzhen Institutes of Advanced Technology (SIAT), 62 sensor devices are deployed to evaluate the performance of SPS. By running the system for several months, we observe the vehicle detection accurate rate of the SPS is nearly 99%. The proposed SPS is energy efficient. The end device can run about 7 years with one 2400mAh AA battery.
Gated Recurrent Unit Based Acoustic Modeling with Future Context

The use of future contextual information is typically shown to be helpful for acoustic modeling. However, for the recurrent neural network (RNN), it’s not so easy to model the future temporal context effectively, meanwhile keep lower model latency. In this paper, we attempt to design a RNN acoustic model that being capable of utilizing the future context effectively and directly, with the model latency and computation cost as low as possible. The proposed model is based on the minimal gated recurrent unit (mGRU) with an input projection layer inserted in it. Two context modules, temporal encoding and temporal convolution, are specifically designed for this architecture to model the future context. Experimental results on the Switchboard task and an internal Mandarin ASR task show that, the proposed model performs much better than long short-term memory (LSTM) and mGRU models, whereas enables online decoding with a maximum latency of 170 ms. This model even outperforms a very strong baseline, TDNN-LSTM, with smaller model latency and almost half less parameters.
Mechanism Principle and Dynamics Simulation on Variable Diameter Walking Wheel

Starting from the principle and strategy of changing the diameter of walking wheel, applying ANSYS "C FEMBS "C SIMPACK to construct model of flexible connectors with geometric stiffness. Based on the theory of continuous colLision, we introduce mobile marker to define the nonLinear contact between the wheel's caster and ground. The rigid-flexible simulation analysis was carried out to wheel body system which consisting of six flexible spring connectors, to get the dynamic response in the mode of changing the diameter in situ and to get the minimum size of torque needing for changing diameter. The simulation's results provides theoretical basis for subsequent prototype development, and can be used as the references for further experiment's studying.
Metrics for the evaluation of data quality of signal data in industrial processes

For Industry 4.0 technology as well as Cyber-Physical Production Systems analysis of data gained more and more importance. But the disparity of data, often make the efficient use of data mining methods difficult due to data with poor quality. To evaluate data quality and further adopt appropriate measures, the proposal develops a data quality model fitted to the specific properties of signal data of industrial processes. Relevant data quality characteristics are identified and a classification of these characteristics is conducted to ascertain important factors. Furthermore, a measurement for the characteristic Completeness, aggregated of its sub-dimensions, is defined. The data quality model is applied to two different use cases showing its effectiveness and validity of the defined measures. The efficient use of real industrial signal data e.g. appropriateness of the data for the specific data mining purpose, is supported by a comprehensive measurement for data quality and the detailed discussion of the influencing factors.
Adaptive KF-SVM Classification for Single Trial EEG in BCI

Single trial electroencephalogram classification is indispensable in online brain–computer interfaces (BCIs) A classification method called adaptive Kernel Fisher Support Vector Machine (KF-SVM) is designed and applied to single trial EEG classification in BCIs. The adaptive KF-SVM algorithm combines adaptive idea, SVM and within-class scatter inspired from kernel fisher. Firstly, the within-class scatter matrix of a feature vector is calculated. And to construct a new kernel, this scatter is incorporated into the kernel function of SVM. Ultimately, the recognition result is calculated by the SVM whose kernel has been changed. The proposed algorithm simultaneously maximizes the discrimination between classes and also considers the within-class dissimilarities, which avoids some disadvantages of traditional SVM. In addition, the within-class scatter matrix of adaptive KF-SVM is updated trial by trail, which enhances the online adaptation of BCIs. Based on the EEG data recorded from seven subjects, the new approach achieved higher classification accuracies than the standard SVM, KF-SVM and adaptive linear classifier. The proposed scheme achieves the average performance improvement of 5.8%,5.2% and 3.7% respectively compared to other three schemes.
Adaptive LQR stabilization control of reaction wheel for satellite systems

Spacecrafts, which are used for stereoscopic mapping, imaging and telecommunication applications, require fine attitude and stabilization control which has an important role in high precision pointing and accurate stabilization. The conventional techniques for attitude and stabilization control are thrusters, reaction wheels, control moment gyroscopes (CMG) and magnetic torquers. Since reaction wheel can generate relatively smaller torques, they provide very fine stabilization and attitude control. Although conventional PID framework solves many stabilization problems, it is reported that many PID feedback loops are poorly tuned. In this paper, a model reference adaptive LQR control for reaction wheel stabilization problem is implemented. The tracking performance and disturbance rejection capability of proposed controller is found to give smooth motion after abnormal disruptions.
Understanding the relationship between institutions and economic development – Some key theoretical issues

* Texto original publicado na obra Instituições e Desenvolvimento Econômico (Teixeira; Braga, 2007, p. 03-23).  Economista sul-coreano especialista em assuntos sobre desenvolvimento econômico. Professor da University of Cambridge, onde atualmente trabalha como redator da Revista Political Economy of Development. Autor da obra Kicking Away the Ladder: Development Strategy in Historical Perspective (2002a). Também é consultor do Banco Mundial, do Banco de Desenvolvimento Asiático e do Banco Europeu de Desenvolvimento. É conhecido por ser uma das grandes influências acadêmicas do economista Rafael Corrêa, atual Presidente do Equador (2007).  Respectivamente, mestranda pelo curso de Ciências Políticas da Universidade Federal do Rio Grande do Sul, mestranda em Sociologia pela Universidade de São Paulo, mestrando em Sociologia pela Universidade de São Paulo e mestre em Sociologia pela Universidade de São Paulo. Understanding the relationship between institutions and economic development – Some key theoretical issues Compreendendo a relação entre instituições e desenvolvimento econômico – Alguns assuntos teóricoschave*
On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation

This paper proposes a novel method for multivehicle detection and tracking using a vehicle-mounted monocular camera. In the proposed method, the features of vehicles are learned as a deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOGs). The detection algorithm combines both global and local features of the vehicle as a deformable object model. Detected vehicles are tracked through a particle filter, which estimates the particles' likelihood by using a detection scores map and template compatibility for both root and parts of the vehicle while considering the deformation cost caused by the movement of vehicle parts. Tracking likelihoods are iteratively used as a priori probability to generate vehicle hypothesis regions and update the detection threshold to reduce false negatives of the algorithm presented before. Extensive experiments in urban scenarios showed that the proposed method can achieve an average vehicle detection rate of 97% and an average vehicle-tracking rate of 86% with a false positive rate of less than 0.26%.
Disintegration and Bayesian Inversion via String Diagrams

The notions of disintegration and Bayesian inversion are fundamental in conditional probability theory. They produce channels, as conditional probabilities, from a joint state, or from an already given channel (in opposite direction). These notions exist in the literature, in concrete situations, but are presented here in abstract graphical formulations. The resulting abstract descriptions are used for proving basic results in conditional probability theory. The existence of disintegration and Bayesian inversion is discussed for discrete probability, and also for measure-theoretic probability — via standard Borel spaces and via likelihoods. Finally, the usefulness of disintegration and Bayesian inversion is illustrated in several examples.
Mushrooms: A Potential Natural Source of Anti-Inflammatory Compounds for Medical Applications

For centuries, macrofungi have been used as food and medicine in different parts of the world. This is mainly attributed to their nutritional value as a potential source of carbohydrates, proteins, amino acids, and minerals. In addition, they also include many bioactive metabolites which make mushrooms and truffles common components in folk medicine, especially in Africa, the Middle East, China, and Japan. The reported medicinal effects of mushrooms include anti-inflammatory effects, with anti-inflammatory compounds of mushrooms comprising a highly diversified group in terms of their chemical structure. They include polysaccharides, terpenoids, phenolic compounds, and many other low molecular weight molecules. The aims of this review are to report the different types of bioactive metabolites and their relevant producers, as well as the different mechanisms of action of mushroom compounds as potent anti-inflammatory agents.
Label-Based Automatic Alignment of Video with Narrative Sentences

In this paper we consider videos (e.g. Hollywood movies) and their accompanying natural language descriptions in the form of narrative sentences (e.g. movie scripts without timestamps). We propose a method for temporally aligning the video frames with the sentences using both visual and textual information, which provides automatic timestamps for each narrative sentence. We compute the similarity between both types of information using vectorial descriptors and propose to cast this alignment task as a matching problem that we solve via dynamic programming. Our approach is simple to implement, highly efficient and does not require the presence of frequent dialogues, subtitles, and character face recognition. Experiments on various movies demonstrate that our method can successfully align the movie script sentences with the video frames of movies.
Computer science: The learning machines

hree years ago, researchers at the secretive Google X lab in Mountain View, California, extracted some 10 million still images from YouTube videos and fed them into Google Brain — a network of 1,000 computers programmed to soak up the world much as a human toddler does. After three days looking for recurring patterns, Google Brain decided, all on its own, that there were certain repeating categories it could identify: human faces, human bodies and … cats 1. Google Brain's discovery that the Inter-net is full of cat videos provoked a flurry of jokes from journalists. But it was also a landmark in the resurgence of deep learning: a three-decade-old technique in which massive amounts of data and processing power help computers to crack messy problems that humans solve almost intuitively, from recognizing faces to understanding language. Deep learning itself is a revival of an even older idea for computing: neural networks. These systems, loosely inspired by the densely interconnected neurons of the brain, mimic human learning by changing the strength of simulated neural connections on the basis of experience. Google Brain, with about 1 million simulated neurons and 1 billion simulated connections, was ten times larger than any deep neural network before it. Project founder Andrew Ng, now director of the Artificial Intelligence Laboratory at Stanford University in California, has gone on to make deep-learning systems ten times larger again. Such advances make for exciting times in THE LEARNING MACHINES Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence.
Quantitative determination of fatty acid compositions in micro-encapsulated fish-oil supplements using Fourier transform infrared (FTIR) spectroscopy.

The research describes a rapid method for the determination of fatty acid (FA) contents in a micro-encapsulated fish-oil (μEFO) supplement by using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic technique and partial least square regression (PLSR) analysis. Using the ATR-FTIR technique, the μEFO powder samples can be directly analysed without any pre-treatment required, and our developed PLSR strategic approach based on the acquired spectral data led to production of a good linear calibration with R(2)=0.99. In addition, the subsequent predictions acquired from an independent validation set for the target FA compositions (i.e., total oil, total omega-3 fatty acids, EPA and DHA) were highly accurate when compared to the actual values obtained from standard GC-based technique, with plots between predicted versus actual values resulting in excellent linear fitting (R(2)≥0.96) in all cases. The study therefore demonstrated not only the substantial advantage of the ATR-FTIR technique in terms of rapidness and cost effectiveness, but also its potential application as a rapid, potentially automated, online monitoring technique for the routine analysis of FA composition in industrial processes when used together with the multivariate data analysis modelling.
Pregnancy derived from human zygote pronuclear transfer in a patient who had arrested embryos after IVF.

Nuclear transfer of an oocyte into the cytoplasm of another enucleated oocyte has shown that embryogenesis and implantation are influenced by cytoplasmic factors. We report a case of a 30-year-old nulligravida woman who had two failed IVF cycles characterized by all her embryos arresting at the two-cell stage and ultimately had pronuclear transfer using donor oocytes. After her third IVF cycle, eight out of 12 patient oocytes and 12 out of 15 donor oocytes were fertilized. The patient's pronuclei were transferred subzonally into an enucleated donor cytoplasm resulting in seven reconstructed zygotes. Five viable reconstructed embryos were transferred into the patient's uterus resulting in a triplet pregnancy with fetal heartbeats, normal karyotypes and nuclear genetic fingerprinting matching the mother's genetic fingerprinting. Fetal mitochondrial DNA profiles were identical to those from donor cytoplasm with no detection of patient's mitochondrial DNA. This report suggests that a potentially viable pregnancy with normal karyotype can be achieved through pronuclear transfer. Ongoing work to establish the efficacy and safety of pronuclear transfer will result in its use as an aid for human reproduction.
Controlled sink mobility for prolonging wireless sensor networks lifetime

This paper demonstrates the advantages of using controlled mobility in wireless sensor networks (WSNs) for increasing their lifetime, i.e., the period of time the network is able to provide its intended functionalities. More specifically, for WSNs that comprise a large number of statically placed sensor nodes transmitting data to a collection point (the sink), we show that by controlling the sink movements we can obtain remarkable lifetime improvements. In order to determine sink movements, we first define a Mixed Integer Linear Programming (MILP) analytical model whose solution determines those sink routes that maximize network lifetime. Our contribution expands further by defining the first heuristics for controlled sink movements that are fully distributed and localized. Our Greedy Maximum Residual Energy (GMRE) heuristic moves the sink from its current location to a new site as if drawn toward the area where nodes have the highest residual energy. We also introduce a simple distributed mobility scheme (Random Movement or S. Basagni ( ) Department of Electrical and Computer Engineering, Northeastern University e-mail: basagni@ece.neu.edu A. Carosi · C. Petrioli Dipartimento di Informatica, Università di Roma “La Sapienza”  carosi@di.uniroma1.it C. Petrioli e-mail: petrioli@di.uniroma1.it E. Melachrinoudis · Z. M. Wang Department of Mechanical and Industrial Engineering, Northeastern University e-mail: emelas@coe.neu.edu Z. M. Wang e-mail: zmwang@coe.neu.edu RM) according to which the sink moves uncontrolled and randomly throughout the network. The different mobility schemes are compared through extensive ns2-based simulations in networks with different nodes deployment, data routing protocols, and constraints on the sink movements. In all considered scenarios, we observe that moving the sink always increases network lifetime. In particular, our experiments show that controlling the mobility of the sink leads to remarkable improvements, which are as high as sixfold compared to having the sink statically (and optimally) placed, and as high as twofold compared to uncontrolled mobility.
Multilevel Image Thresholding Selection Based on the Firefly Algorithm

The multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the firefly algorithm is proposed. This proposed method is called the maximum entropy based firefly thresholding method. Four different methods are implemented for comparing to this proposed method: the exhaustive search, the particle swarm optimization, the hybrid cooperative-comprehensive learning based PSO algorithm and the honey bee mating optimization. The experimental results demonstrated that the proposed MEFFT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the PSO and HCOCLPSO, the segmentation results of using the MEFFT algorithm is significantly improved and the computation time of the proposed MEFFT algorithm is shortest.