nils-herrmann/clean_scirepeval huggingface dataset:
MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach

The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper is to develop a standalone novel technique to suppress motion artefacts from MR images using a data-driven deep learning approach.  A deep learning convolutional neural network (CNN) was developed to remove motion artefacts in brain MR images. A CNN was trained on simulated motion corrupted images to identify and suppress artefacts due to the motion. The network was an encoderdecoder CNN architecture where the encoder decomposed the motion corrupted images into a set of feature maps. The feature maps were then combined by the decoder network to generate a motion-corrected image. The network was tested on an unseen simulated dataset and an experimental, motion corrupted in vivo brain dataset.  The trained network was able to suppress the motion artefacts in the simulated motion corrupted images, and the mean percentage error in the motion corrected images was 2.69 % with a standard deviation of 0.95 %. The network was able to effectively suppress the motion artefacts from the experimental dataset, demonstrating the generalisation capability of the trained network.  A novel and generic motion correction technique has been developed that can suppress motion artefacts from motion corrupted MR images. The proposed technique is a standalone post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for a multitude of MR sequences.
Assessment of jerk performance s-curve and trapezoidal velocity profiles

nowadays, there are several techniques for the acceleration and deceleration of computer numerical control (CNC) machine tools and industrial robots (Robot manipulators) in order to plan smoother trajectories avoiding the jerk and another kind of stress. The aim of this article is to show a comparison between the trapezoidal and s-curve velocity profile used in robotics and CNC machinery. After doing that, the reader will be able to select a velocity profile in order to design their trajectories.
TopoToolbox: A set of Matlab functions for topographic analysis

TopoToolbox contains a set of Matlab functions that provide utilities for relief analysis in a nonGeographical Information System (GIS) environment. The tools have been developed to support the work flow in combined spatial and non-spatial numerical analysis. They offer flexible and user-friendly software for hydrological and geomorphological research that involves digital elevation model analysis and focuses on material fluxes and spatial variability of water, sediment, chemicals and nutrients. The objective of this paper is to give an introduction to the linear algebraic concept behind the software that employs sparse matrix computations for digital elevation model analysis. Moreover, we outline the functionality of the toolbox. The source codes are freely available in Matlab language on the authors’ webpage (physiogeo.unibas.ch/topotoolbox).  TopoToolbox  MATLAB Requirements: MATLAB R2009a, Image Processing Toolbox Availability: TopoToolbox is available free of charge and can be downloaded on http://physiogeo.unibas.ch/topotoolbox.
Reconditioned merchandise: extended structured report formats in usability inspection

Structured Problem Report Formats have been key to improving the assessment of usability methods. Once extended to record analysts' rationales, they not only reveal analyst behaviour but also change it. We report on two versions of an Extended Structured Report Format for usability problems, briefly noting their impact on analyst behaviour, but more extensively presenting insights into decision making during usability inspection, thus validating and refining a model of evaluation performance.
Machine learning approaches to lung cancer prediction from mass spectra.

We addressed the problem of discriminating between 24 diseased and 17 healthy specimens on the basis of protein mass spectra. To prepare the data, we performed mass to charge ratio (m/z) normalization, baseline elimination, and conversion of absolute peak height measures to height ratios. After preprocessing, the major difficulty encountered was the extremely large number of variables (1676 m/z values) versus the number of examples (41). Dimensionality reduction was treated as an integral part of the classification process; variable selection was coupled with model construction in a single ten-fold cross-validation loop. We explored different experimental setups involving two peak height representations, two variable selection methods, and six induction algorithms, all on both the original 1676-mass data set and on a prescreened 124-mass data set. Highest predictive accuracies (1-2 off-sample misclassifications) were achieved by a multilayer perceptron and Naïve Bayes, with the latter displaying more consistent performance (hence greater reliability) over varying experimental conditions. We attempted to identify the most discriminant peaks (proteins) on the basis of scores assigned by the two variable selection methods and by neural network based sensitivity analysis. These three scoring schemes consistently ranked four peaks as the most relevant discriminators: 11683, 1403, 17350 and 66107.
Decryption of Frequent Password Hashes in Rainbow Tables

Time-memory trade-off methods provide means to invert one way functions. Such attacks offer a flexible trade-off between running time and memory cost in accordance to users' computational resources. In particular, they can be applied to hash values of passwords in order to recover the plaintext. They were introduced by Martin Hellman and later improved by Philippe Oechslin with the introduction of rainbow tables. The drawbacks of rainbow tables are that they do not always guarantee a successful inversion. We address this issue in this paper. In the context of passwords, it is pertinent that frequently used passwords are incorporated in the rainbow table. It has been known that up to 4 given passwords can be incorporated into a chain but it is an open problem if more than 4 passwords can be achieved. We solve this problem by showing that it is possible to incorporate more of such passwords along a chain. Furthermore, we prove that this results in faster recovery of such passwords during the online running phase as opposed to assigning them at the beginning of the chains. For large chain lengths, the average improvement translates to 3 times the speed increase during the online recovery time.
Aircraft Ground Routing and Scheduling Optimization

In this paper we deal with the ground optimization problem, that is the problem of routing and scheduling airplanes surface maneuvering operations. We consider the specific case study of Malpensa Terminal Maneuvering Area (Italy). Our objective function is the minimization of total tardiness. At first a routing problem is solved to assign a path to each aircraft in the terminal, then the scheduling problem of minimizing the average tardiness is addressed. We model the scheduling problem as a job-shop scheduling problem. We develop heuristic procedures based on the alternative graph formulation of the problem to construct and improve feasible solutions. Experimental results based on real data and analysis are reported.
Ischiofemoral impingement.

Femoroacetabular impingement is a well-documented cause of hip pain. There is, however, increasing evidence for the presence of a previously unrecognised impingement-type condition around the hip - ischiofemoral impingement. This is caused by abnormal contact between the lesser trochanter of the femur and the ischium, and presents as atypical groin and/or posterior buttock pain. The symptoms are gradual in onset and may be similar to those of iliopsoas tendonitis, hamstring injury or bursitis. The presence of ischiofemoral impingement may be indicated by pain caused by a combination of hip extension, adduction and external rotation. Magnetic resonance imaging demonstrates inflammation and oedema in the ischiofemoral space and quadratus femoris, and is distinct from an acute tear. To date this has only appeared in the specialist orthopaedic literature as a problem that has developed after total hip replacement, not in the unreplaced joint.
Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition

The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition. Separately, connectionist temporal classification (CTC) has matured as an alignment-free, non-autoregressive approach to sequence transduction, either by itself or in various multitask and decoding frameworks. We propose SAN-CTC, a deep, fully self-attentional network for CTC, and show it is tractable and competitive for end-toend speech recognition. SAN-CTC trains quickly and outperforms existing CTC models and most encoder-decoder models, with character error rates (CERs) of 4.7% in 1 day on WSJ eval92 and 2.8% in 1 week on LibriSpeech test-clean, with a fixed architecture and one GPU. Similar improvements hold for WERs after LM decoding. We motivate the architecture for speech, evaluate position and downsampling approaches, and explore how label alphabets (character, phoneme, subword) affect attention heads and performance.
Automatic Smart Parking System using Internet of Things ( IOT )

Internet of Things (IOT) plays a vital role in connecting the surrounding environmental things to the network and made easy to access those un-internet things from any remote location. It’s inevitable for the people to update with the growing technology. And generally people are facing problems on parking vehicles in parking slots in a city. In this study we design a Smart Parking System (SPS) which enables the user to find the nearest parking area and gives availability of parking slots in that respective parking area. And it mainly focus on reducing the time in finding the parking lots and also it avoids the unnecessary travelling through filled parking lots in a parking area. Thus it reduces the fuel consumption which in turn reduces carbon footprints in an atmosphere.
Performance evaluation of energy detector over generalized non-linear and shadowed composite fading channels using a Mixture Gamma Distribution

The performance of energy detection (ED) for independent and identically distribution (i.i.d.) signal models is analyzed over generalized composite non-linear line-of-sight (LOS) and non-line-ofsight (NLOS) shadowed fading scenarios. The novel expressions for ακ-μ/Gamma and α-η-μ/Gamma fading channels have been derived to approximate by using the mixture gamma (MG) distribution under low instantaneous signal-to-noise (SNR) condition. On the basis of the deduced fading distributions, novel, exact and close-form detective models are derived to evaluate the sensing performance with different key fading parameters over generalized non-linear and shadowed composite fading channels.
Hybrid Security RSA Algorithm in Application of Web Service

A new hybrid security algorithm is presented for RSA cryptosystem named as Hybrid RSA. The system works on the concept of using two different keys- a private and a public for decryption and encryption processes. The value of public key (P) and private key (Q) depends on value of M, where M is the product of four prime numbers which increases the factorizing of variable M. moreover, the computation of P and Q involves computation of some more factors which makes it complex. This states that the variable x or M is transferred during encryption and decryption process, where x represents the multiplication of two prime numbers A and B. thus, it provides more secure path for encryption and decryption process. The proposed system is compared with the RSA and enhanced RSA (ERSA) algorithms to measure the key generation time, encryption and decryption time which is proved to be more efficient than RSA and ERSA.
A combination of low-level light stemming and support vector machines for the classification of Arabic opinions

Recent years have brought the burst of volume of shared opinionated texts across the internet. Every day, a tremendous number of comments and reviews towards different aspects of our lives is generated through social networks and other websites. A large portion of these data is written in Arabic which is the fifth most used language on internet and is one of the six official languages of the United Nations. However, the templatic morphology of Arabic language makes it a difficult language in the fields of information retrieval. In light of the scarcity of Arabic opinion mining systems, this paper introduces a complete approach to prepare, model and classify Arabic comments and reviews. It is conducted using a dataset consisting of 500 comments collected from TripAdvisor Website which fall into four scaled classes of opinions. For this, a new stemming technique has been defined. Stemmed opinions are then quantified according to a set of mathematical functions, and classified by different machine learning techniques. Support vector machines have yielded the highest f-measure. Finally, the impact of each step on the overall performance has been evaluated.
Colorimetric protein assay techniques.

There has been an increase in the number of colorimetric assay techniques for the determination of protein concentration over the past 20 years. This has resulted in a perceived increase in sensitivity and accuracy with the advent of new techniques. The present review considers these advances with emphasis on the potential use of such technologies in the assay of biopharmaceuticals. The techniques reviewed include Coomassie Blue G-250 dye binding (the Bradford assay), the Lowry assay, the bicinchoninic acid assay and the biuret assay. It is shown that each assay has advantages and disadvantages relative to sensitivity, ease of performance, acceptance in the literature, accuracy and reproducibility/coefficient of variation/laboratory-to-laboratory variation. A comparison of the use of several assays with the same sample population is presented. It is suggested that the most critical issue in the use of a chromogenic protein assay for the characterization of a biopharmaceutical is the selection of a standard for the calibration of the assay; it is crucial that the standard be representative of the sample. If it is not possible to match the standard with the sample from the perspective of protein composition, then it is preferable to use an assay that is not sensitive to the composition of the protein such as a micro-Kjeldahl technique, quantitative amino acid analysis or the biuret assay. In a complex mixture it might be inappropriate to focus on a general method of protein determination and much more informative to use specific methods relating to the protein(s) of particular interest, using either specific assays or antibody-based methods. The key point is that whatever method is adopted as the 'gold standard' for a given protein, this method needs to be used routinely for calibration.
Is ICT Adoption for Agriculture Still an Important Issue ?

Attempts to understand and improve the effectiveness of ICT Adoption for Agriculture have been at the forefront of national and regional policies ever since computers became available for agricultural management and production. EFITA* has been sharing these efforts via it’s conferences and the collection of data sets to identify ICT Adoption constraints and trends over time. The 5th EFITA/WCCA conference hosted in Villa Real, Portugal addressed the results emanating from these data sets and the ongoing Adoption efforts. It evaluated them via a plenary discussion of the question: Is ICT Adoption for Agriculture Still an Important Issue? The overwhelming response from conference participants asserted that ICT Adoption for Agriculture and Rural Development remains a major national and international concern. It was suggested that if cost is eliminated as a constraint the other ICT adoption impediments, in both developed and developing countries, are basically the same. Training, as one example was identified as a common, dominant alleviating adoption factor. Public funding to promote ICT Adoption and services was justified with qualifications. It was observed that ICT Adoption for Agriculture should be considered as an issue of public concern beyond farmer’s interests. The EFITA sponsored data sets support some of the conference conclusions. EFITA European Federation for Information Technology in Agriculture, Food and Environment The support of the Samuel Neaman Institute for Advanced Studies in Science and Technology for preparing the theme background material is gratefully acknowledged.  Attempts to understand and explain the mechanism and constraints of Technological Innovation Adoption are not new – e.g. Grilliches (1957). Adoption of ICT (Information and Communication Technologies) in rural areas as a unique challenge has long been a specific public concern with regional, national and international strategic significance. Gelb, Schmidt and Rauschkolb (1985) focused as a case study on Extension as did an EU sponsored workshop Gelb, Bonati (1997). Harsh (1986) generalized ICT Adoption trends in Agriculture. They were later evaluated via an EFITA sponsored questionnaire since the 1999 EFITA Conference in Bonn Gelb, et al (2004). The USDA Computer Ownership and Use Surveys NASS (2005) highlight as a comparative baseline the significance of ICT Adoption constraints since 1997 for a very large sample of US farmers. With this indicative, intensive history of effort and studying ICT Adoption issues in mind the EFITA/WCCA 2005 conference deliberated a specific question: Is ICT Adoption in Agriculture and Rural Development still an Important Issue? The conference’s two dedicated sessions and plenary discussion participants had access to additional background material. It included the conference papers, the ongoing analysis of the previous EFITA Questionnaire data sets, several ICT Adoption in Agriculture perspective reviews (currently freely available at http://departments.agri.huji.ac.il/economics/gelb-
Rapid analysis of bile acids in different biological matrices using LC-ESI-MS/MS for the investigation of bile acid transformation by mammalian gut bacteria.

Bile acids are important signaling molecules that regulate cholesterol, glucose, and energy homoeostasis and have thus been implicated in the development of metabolic disorders. Their bioavailability is strongly modulated by the gut microbiota, which contributes to generation of complex individual-specific bile acid profiles. Hence, it is important to have accurate methods at hand for precise measurement of these important metabolites. Here, a rapid and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous identification and quantitation of primary and secondary bile acids as well as their taurine and glycine conjugates was developed and validated. Applicability of the method was demonstrated for mammalian tissues, biofluids, and cell culture media. The analytical approach mainly consists of a simple and rapid liquid-liquid extraction procedure in presence of deuterium-labeled internal standards. Baseline separation of all isobaric bile acid species was achieved and a linear correlation over a broad concentration range was observed. The method showed acceptable accuracy and precision on intra-day (1.42-11.07 %) and inter-day (2.11-12.71 %) analyses and achieved good recovery rates for representative analytes (83.7-107.1 %). As a proof of concept, the analytical method was applied to mouse tissues and biofluids, but especially to samples from in vitro fermentations with gut bacteria of the family Coriobacteriaceae. The developed method revealed that the species Eggerthella lenta and Collinsella aerofaciens possess bile salt hydrolase activity, and for the first time that the species Enterorhabdus mucosicola is able to deconjugate and dehydrogenate primary bile acids in vitro.
Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite

Analytical prediction of oxidative stress biomarkers in ecosystem provides an expressive result for many stressors. These oxidative stress biomarkers including superoxide dismutase, glutathione peroxidase and catalase activity in fish liver tissue were analyzed within feeding different levels of selenium nanoparticles. Se-nanoparticles represent a salient defense mechanism in oxidative stress within certain limits; however, stress can be engendered from toxic levels of these nanoparticles. For instance, prediction of the level of pollution and/or stressors was elucidated to be improved with different levels of selenium nanoparticles using the bio-inspired Sine-Cosine algorithm (SCA). In this paper, we improved the prediction accuracy of liver enzymes of fish fed by nano-selenite by developing a neural network model based on SCA, that can train and update the weights and the biases of the network until reaching the optimum value. The performance of the proposed model is better and achieved more efficient than other models.
In the mood for sharing contents: Emotions, personality and interaction styles in the diffusion of news

In this paper, we analyze the influence of Twitter users in sharing news articles that may affect the readers’ mood. We collected data of more than 2000 Twitter users who shared news articles from Corriere.it, a daily newspaper that provides mood metadata annotated by readers on a voluntary basis. We automatically annotated personality types and communication styles of Twitter users and analyzed the correlations between personality, communication style, Twitter metadata (such as followig and folllowers) and the type of mood associated to the articles they shared. We also run a feature selection task, to find the best predictors of positive and negative mood sharing, and a classification task. We automatically predicted positive and negative mood sharers with 61.7% F1-measure.
An evolutionary life-history framework for understanding sex differences in human mortality rates.

Sex differences in mortality rates stem from genetic, physiological, behavioral, and social causes that are best understood when integrated in an evolutionary life history framework. This paper investigates the Male-to-Female Mortality Ratio (M:F MR) from external and internal causes and across contexts to illustrate how sex differences shaped by sexual selection interact with the environment to yield a pattern with some consistency, but also with expected variations due to socioeconomic and other factors.
An IoT-based appliance control system for smart homes

With the development of the social economy, more and more appliances have been presented in a house. It comes out a problem that how to manage and control these increasing various appliances efficiently and conveniently so as to achieve more comfortable, security and healthy space at home. In this paper, a smart control system base on the technologies of internet of things has been proposed to solve the above problem. The smart home control system uses a smart central controller to set up a radio frequency 433 MHz wireless sensor and actuator network (WSAN). A series of control modules, such as switch modules, radio frequency control modules, have been developed in the WSAN to control directly all kinds of home appliances. Application servers, client computers, tablets or smart phones can communicate with the smart central controller through a wireless router via a Wi-Fi interface. Since it has WSAN as the lower control layer, a appliance can be added into or withdrawn from the control system very easily. The smart control system embraces the functions of appliance monitor, control and management, home security, energy statistics and analysis.