nils-herrmann/clean_scirepeval huggingface dataset:
A framework to support the evaluation, adoption and improvement of agile methods in practice

Agile methods are often seen as providing ways to avoid overheads typically perceived as being imposed by traditional software development environments. However, few organizations are psychologically or technically able to take on an agile approach rapidly and effectively. Here, we describe a number of approaches to assist in such a transition. The Agile Software Solution Framework (ASSF) provides an overall context for the exploration of agile methods, knowledge and governance and contains an Agile Toolkit for quantifying part of the agile process. These link to the business aspects of software development so that the business value and agile process are well aligned. Finally, we describe how these theories are applied in practice with two industry case studies using the Agile Adoption and Improvement Model (AAIM).
SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis

Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.
An Exploration of Unreliable News Classification in Brazil and The U.S

The propagation of unreliable information is on the rise in many places around the world. This expansion is facilitated by the rapid spread of information and anonymity granted by the Internet. The spread of unreliable information is a well-studied issue and it is associated with negative social impacts. In a previous work, we have identified significant differences in the structure of news articles from reliable and unreliable sources in the US media. Our goal in this work was to explore such differences in the Brazilian media. We found significant features in two data sets: one with Brazilian news in Portuguese and another one with US news in English. Our results show that features related to the writing style were prominent in both data sets and, despite the language difference, some features have a universal behavior, being significant to both US and Brazilian news articles. Finally, we combined both data sets and used the universal features to build a machine learning classifier to predict the source type of a news article as reliable or unreliable.
Surface electromyographic analysis of exercises for the trapezius and serratus anterior muscles.

This study used a prospective, single-group repeated-measures design to analyze differences between the electromyographic (EMG) amplitudes produced by exercises for the trapezius and serratus anterior muscles.



To identify high-intensity exercises that elicit the greatest level of EMG activity in the trapezius and serratus anterior muscles.



The trapezius and serratus anterior muscles are considered to be the only upward rotators of the scapula and are important for normal shoulder function. Electromyographic studies have been performed for these muscles during active and low-intensity exercises, but they have not been analyzed during high intensity exercises.



Surface electrodes recorded EMG activity of the upper, middle, and lower trapezius and serratus anterior muscles during 10 exercises in 30 healthy subjects.



The unilateral shoulder shrug exercise was found to produce the greatest EMG activity in the upper trapezius. For the middle trapezius, the greatest EMG amplitudes were generated with 2 exercises: shoulder horizontal extension with external rotation and the overhead arm raise in line with the lower trapezius muscle in the prone position. The arm raise overhead exercise in the prone position produced the maximum EMG activity in the lower trapezius. The serratus anterior was activated maximally with exercises requiring a great amount of upward rotation of the scapula. The exercises were shoulder abduction in the plane of the scapula above 120 degrees and a diagonal exercise with a combination of shoulder flexion, horizontal flexion, and external rotation.



This study identified exercises that maximally activate the trapezius and serratus anterior muscles. This information may be helpful for clinicians in developing exercise programs for these muscles.
Real-Time Posture Reconstruction for Microsoft Kinect

The recent advancement of motion recognition using Microsoft Kinect stimulates many new ideas in motion capture and virtual reality applications. Utilizing a pattern recognition algorithm, Kinect can determine the positions of different body parts from the user. However, due to the use of a single-depth camera, recognition accuracy drops significantly when the parts are occluded. This hugely limits the usability of applications that involve interaction with external objects, such as sport training or exercising systems. The problem becomes more critical when Kinect incorrectly perceives body parts. This is because applications have limited information about the recognition correctness, and using those parts to synthesize body postures would result in serious visual artifacts. In this paper, we propose a new method to reconstruct valid movement from incomplete and noisy postures captured by Kinect. We first design a set of measurements that objectively evaluates the degree of reliability on each tracked body part. By incorporating the reliability estimation into a motion database query during run time, we obtain a set of similar postures that are kinematically valid. These postures are used to construct a latent space, which is known as the natural posture space in our system, with local principle component analysis. We finally apply frame-based optimization in the space to synthesize a new posture that closely resembles the true user posture while satisfying kinematic constraints. Experimental results show that our method can significantly improve the quality of the recognized posture under severely occluded environments, such as a person exercising with a basketball or moving in a small room.
Multi-Agent Systems for Power Engineering Applications—Part II: Technologies, Standards, and Tools for Building Multi-agent Systems

This is the second part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examined the potential value of MAS technology to the power industry, described fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications, and presented a comprehensive review of the power engineering applications for which MAS are being investigated. It also defined the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented. Given the significant and growing interest in this field, it is imperative that the power engineering community considers the standards, tools, supporting technologies, and design methodologies available to those wishing to implement a MAS solution for a power engineering problem. This paper describes the various options available and makes recommendations on best practice. It also describes the problem of interoperability between different multi-agent systems and proposes how this may be tackled.
Design and MinION testing of a nanopore targeted gene sequencing panel for chronic lymphocytic leukemia

We report a customized gene panel assay based on multiplex long-PCR followed by third generation sequencing on nanopore technology (MinION), designed to analyze five frequently mutated genes in chronic lymphocytic leukemia (CLL): TP53, NOTCH1, BIRC3, SF3B1 and MYD88. For this purpose, 12 patients were selected according to specific cytogenetic and molecular features significantly associated with their mutational status. In addition, simultaneous analysis of the targets genes was performed by molecular assays or Sanger Sequencing. Data analysis included mapping to the GRCh37 human reference genome, variant calling and annotation, and average sequencing depth/error rate analysis. The sequencing depth resulted on average higher for smaller amplicons, and the final breadth of coverage of the panel was 94.1%. The error rate was about 6% and 2% for insertions/deletions and single nucleotide variants, respectively. Our gene panel allows analysis of the prognostically relevant genes in CLL, with two PCRs per patient. This strategy offers an easy and affordable workflow, although further advances are required to improve the accuracy of the technology and its use in the clinical field. Nevertheless, the rapid and constant development of nanopore technology, in terms of chemistry advances, more accurate basecallers and analysis software, offers promise for a wide use of MinION in the future.
The Association between Health Behaviours and Academic Performance in Canadian Elementary School Students: A Cross-Sectional Study.

Establishing early healthy eating and physical activity behaviours is critical in supporting children's long-term health and well-being. The objective of the current paper was to examine the association between health behaviours and academic performance in elementary school students in a school board in Nova Scotia, Canada.



Our population-based study included students in grades 4-6 across 18 schools in a rural school board. Diet and physical activity were assessed through validated instruments. Academic performance measures were obtained from the school board for Mathematics and English Language Arts (ELA). Associations between health behaviours and academic performance were assessed using multilevel logistic regression.



Students with unhealthy lifestyle behaviours were more likely to have poor academic performance for both ELA and Mathematics compared to students with healthy lifestyle behaviours; associations were statistically significant for diet quality, physical activity, sugar-sweetened beverage consumption for ELA; and breakfast skipping, not being physically active at morning recess, and not being physically active after school for Mathematics. The effects of diet and physical activity were independent of each other and there was no interaction between the two exposures.



Our findings suggest that support for healthy behaviours may help to improve academic outcomes of students.
Gate driving of high power IGBT through a Double Galvanic Insulation Transformer

The reliability of the transmission of the switching signal of IGBT in a static converter is crucial. In fact, if the switching signals are badly transmitted, the power converter can be short-circuited with dramatic consequences. Thus, the operating of such a system can be stopped with heavy economic consequences, as it is the case for an electric train. Many techniques have been developed to achieve solutions for a safe transmission of switching signals with a good galvanic insulation. In very high-voltage, over 10 kV, an optimal solution is to use optic fibres. This technology is limited by the fibre degradation in high temperature atmosphere. Actually, this problem exists in trains. The common use of the radio frequency transmission (RFT) can be exploited to achieve an original IGBT wireless driver. This solution seems to be interesting because high temperature do not interfere with radio frequency transmission. However, radiated electromagnetic interferences (EMI) are drastically important in such an electrical environment, EMI can disturb the RFT. In order to optimise the transmission of switching signals, we have decided to transmit the signals through the energy supplying link. This last device is constituted by a double galvanic insulation transformer (DGIT). The difficulty is to transmit the energy, which is used for the IGBT driver supply and the switching signals in the same loop wire. The paper will highlight this aspect
Overview of Algorithms for Swarm Intelligence

Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.
Comparing treatment outcome of infliximab and adalimumab in patients with severe hidradenitis suppurativa.

Hidradenitis suppurativa (HS) is a chronic relapsing skin disease. Recent studies have shown promising results of anti-tumor necrosis factor-alpha treatment.



To compare the efficacy and safety of infliximab and adalimumab in the treatment of HS.



A retrospective study was performed to compare 2 cohorts of 10 adult patients suffering from severe, recalcitrant HS. In 2005, 10 patients were treated with infliximab intravenous (i.v.) (3 infusions of 5 mg/kg at weeks 0, 2, and 6). In 2009, 10 other patients were treated in the same hospital with adalimumab subcutaneous (s.c.) 40 mg every other week. Both cohorts were followed up for 1 year using identical evaluation methods [Sartorius score, quality of life index, reduction of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), patient and doctor global assessment, and duration of efficacy].



Nineteen patients completed the study. In both groups, the severity of the HS diminished. Infliximab performed better in all aspects. The average Sartorius score was reduced to 54% of baseline for the infliximab group and 66% of baseline for the adalimumab group.



Adalimumab s.c. 40 mg every other week is less effective than infliximab i.v. 5 mg/kg at weeks 0, 2, and 6.
Multiple Features With Extreme Learning Machines For Clothing Image Recognition

Clothing image recognition has recently received considerable attention from many communities, such as multimedia information processing and computer vision, due to its commercial and social applications. However, the large variations in clothing images’ appearances and styles and their complicated formation conditions make the problem challenging. In addition, a generic treatment with convolutional neural networks (CNNs) cannot provide a satisfactory solution considering the training time and recognition performance. Therefore, how to balance those two factors for clothing image recognition is an interesting problem. Motivated by the fast training and straightforward solutions exhibited by extreme learning machines (ELMs), in this paper, we propose a recognition framework that is based on multiple sources of features and ELM neural networks. In this framework, three types of features are first extracted, including CNN features with pre-trained networks, histograms of oriented gradients and color histograms. Second, those low-level features are concatenated and taken as the inputs to an autoencoder version of the ELM for deep feature-level fusion. Third, we propose an ensemble of adaptive ELMs for decision-level fusion using the previously obtained feature-level fusion representations. Extensive experiments are conducted on an up-to-date large-scale clothing image data set. Those experimental results show that the proposed framework is competitive and efficient.
Circularly Polarized Square Slot Antenna With a Pair of Inverted-L Grounded Strips

This paper presents a new wideband circularly polarized square slot antenna (CPSSA) with a coplanar waveguide (CPW) feed. The proposed antenna features two inverted-L grounded strips around two opposite corners of the slot and a widened tuning stub protruded into the slot from the signal strip of the CPW. Broadside circular-polarization (CP) radiation can be easily obtained using a simple design procedure. For the optimized antenna prototype, the measured bandwidth with an axial ratio (AR) of less than 3 dB is larger than 25% and the measured VSWR les 2 impedance bandwidth is as large as 52%.
Retinal prosthesis for the blind.

Most of current concepts for a visual prosthesis are based on neuronal electrical stimulation at different locations along the visual pathways within the central nervous system. The different designs of visual prostheses are named according to their locations (i.e., cortical, optic nerve, subretinal, and epiretinal). Visual loss caused by outer retinal degeneration in diseases such as retinitis pigmentosa or age-related macular degeneration can be reversed by electrical stimulation of the retina or the optic nerve (retinal or optic nerve prostheses, respectively). On the other hand, visual loss caused by inner or whole thickness retinal diseases, eye loss, optic nerve diseases (tumors, ischemia, inflammatory processes etc.), or diseases of the central nervous system (not including diseases of the primary and secondary visual cortices) can be reversed by a cortical visual prosthesis. The intent of this article is to provide an overview of current and future concepts of retinal and optic nerve prostheses. This article will begin with general considerations that are related to all or most of visual prostheses and then concentrate on the retinal and optic nerve designs. The authors believe that the field has grown beyond the scope of a single article so cortical prostheses will be described only because of their direct effect on the concept and technical development of the other prostheses, and this will be done in a more general and historic perspective.
Distributed mininet with symbiosis

Mininet is a container-based emulation environment that can study networks with virtual hosts and OpenFlow-enabled virtual switches on Linux. However, it is well-known that experiments using Mininet may lose fidelity for large-scale networks and heavy traffic load. One solution is to use a distributed setup where an experiment constitutes multiple instances of Mininet running on a cluster, each handling a subset of virtual hosts and switches. Such arrangement, however, is still constrained by bandwidth and latency limitations in the physical connection between the instances. In this paper, we propose a novel method of integrating distributed Mininet instances using a symbiotic approach, which extends an existing method for combining real-time simulation and emulation. We use an abstract network model to coordinate the distributed instances, which are superimposed to represent the target network. In this case, one can more effectively study the behavior of real implementation of network applications on large-scale networks, since the interaction between the Mininet instances is only capturing the effect of contentions among network flows in shared queues, as opposed to having to exchange individual network packets, which can be limited by bandwidth or sensitive to latency. We provide a prototype implementation of the new approach and present validation studies to show it can achieve accurate results. We also present a case study that successfully replicates the behavior of a denial-of-service (DoS) attack protocol.
A Vision System for Horizon Tracking and Object Recognition for Micro Air Vehicles

In this paper, we develop a unified vision system for small-scale aircraft, known broadly as Micro Air Vehicl es (MAVs), that not only addresses basic flight stability and control, but also enables more intelligent missions, such as ground o bject recognition and moving-object tracking. The proposed syst em defines a framework for real-time image feature extraction, horizon detection and sky/ground segmentation, and contex tual ground object detection. Multiscale Linear Discriminant Analysis (MLDA) defines the first stage of the vision system, and generates a multiscale description of images, incorporati ng both color and texture through a dynamic representation of image details. This representation is ideally suited for horizondetection and sky/ground segmentation of images, which we accomplish through the probabilistic representation of tree-structured belief networks (TSBN). Specifically, we propose incomplete meta TSBNs (IMTSBN) to accommodate the properties of our MLDA representation and to enhance the descriptive component of these statistical models. In the last stage of the vision processi ng, we seamlessly extend this probabilistic framework to perfo rm computationally efficient detection and recognition of obj ects in the segmented ground region, through the idea of visual contexts. By exploiting the concept of visual contexts, we c an quickly focus on candidate regions, where objects of intere st may be found, and then compute additional features through the Complex Wavelet Transform (CWT) and HSI color space for those regions, only. These additional features, while n ot necessary for global regions, are useful in accurate detect ion and recognition of smaller objects. Throughout, our approach is heavily influenced by real-time constraints and robustne ss to transient video noise.