nils-herrmann/clean_scirepeval huggingface dataset:
Introducao ao Direito Digital
Year: 2018

O Direito Digital representa a evolucao do proprio Direito, abrangendo todos os seus ramos. Trata, especialmente, de dilemas da denominada "Sociedade da Informacao" e das novas formas de criminalidade surgidas da evolucao tecnologica e da expansao da internet. O artigo aborda questoes relacionadas a tais temas e indica como vem sendo tratadas em normas internacionais e brasileiras.
Beta glucan or rice bran enriched foods: a comparative crossover clinical trial on lipidic pattern in mildly hypercholesterolemic men
Venue: European Journal of Clinical Nutrition
Year: 2011

There has been growing interest in using dietary intervention to improve the lipid profile. This work aims at analyzing the effects and the comparison of the enrichment of a diet with beta glucans or rice bran in mildly hypercholesterolemic men.The subjects initially consumed a 3 week Step 1 American Heart Association diet with rice bran enriched foods. After this adaptation period, volunteers were randomly assigned to follow a crossover, controlled trial that consisted of two treatment with beta glucan or rice bran enriched foods, each of 4 weeks, with a 3 week wash out, like the adaptation period, between periods. Fasted blood samples were collected on days 0, 21, 49, 70 and 98 in both study arms for measuring low density lipoprotein (LDL) cholesterol (primary outcome) total cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides, apolipoprotein (apo) A I, apo B and glucose levels.Twenty four men (mean age: 50.3+ 5.3, mean body mass index: 24.9+ 1.9) completed the 14 week trial. Subjects in the 3 week adaptation period experienced significant reductions in the mean level of LDL cholesterol, total cholesterol, total cholesterol/HDL cholesterol, LDL cholesterol/HDL cholesterol, apo A I, apo A I/apo B and glucose. During the intervention diet periods, a difference was found between treatment groups for the mean change in LDL (0.21 (95% confidence interval (CI) 0.02 0.40) P=0.033) and total cholesterol (0.34 (95% CI: 0.20 0.47) P<0.001) Other parameters evaluated were not significantly affected by the diet consumed.The results of the present crossover clinical trial showed that beta glucan enriched foods are more effective in lowering serum LDL levels, compared with rice bran enriched foods.
Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018)
Venue: Machine Learning
Year: 2019

We welcome you to this special issue of Machine Learning Journal (MLJ) comprising of papers accepted to the journal track of the 10th Asian Conference on Machine Learning (ACML 2018) held in Beijing, China, from 14 to 16November 2018. TheACML conference is running a dedicated journal track alongside the usual conference proceedings track. We are delighted to share the contributions with you. This year's ACML journal track received 35 submissions and 7 papers have been accepted for this special issue. The reviewing process was handled by the ACML journal track committee, consisting of Wray Buntine (Monash University, Australia) Steven Hoi (Singapore Management University, Singapore) Kee Eung Kim (Korea Advanced Institute of Science and Technology, Korea) Wee Sun Lee (National University of Singapore, Singapore) Hang Li (Toutiao, China) Yu Feng Li (Nanjing University, China) and Ivor Tsang (University of Technology Sydney, Australia) with Co chairs and Editors of this special issue, Masashi Sugiyama (RIKEN/The University of Tokyo, Japan) and Yung Kyun Noh (Seoul National University, Korea) Submitted papers were assigned to committee members by the Co chairs according to the committee members' areas of expertise, while ensuring that there were no conflicts of interest. The committee members recommended reviewers and acted as metareviewers for the papers. Promising papers that did not quite meet the expected standard were allowed to be resubmitted after improvement, following the reviewing process of this journal. The paper Good Arm Identification via Bandit Feedback by Hideaki Kano, Junya Honda, Kentaro Sakamaki, Kentaro Matsuura, Atsuyoshi Nakamura, and Masashi Sugiyama introduces a new concept of selecting a good arm which is not the best but useful. The Hybrid algorithm for the Dilemma of Confidence (HDoC) for this problem is proposed with theoretical bounds on the sample complexity. For many applications that not necessarily need the best choice, the proposed algorithm in this paper can be used for choosing good enough choices after looking into a small number of samples.
Privacy Preserving Private Frequent Itemset Mining via Smart Splitting
Year: 2016

Recently there has been a growing interest in designing differentially private data mining algorithms. A variety of algorithms have been proposed for mining frequent itemsets. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. It has practical importance in a wide range of application areas such as decision support, web usage mining, bioinformatics, etc. In this paper, It explore the possibility of designing a differentially private FIM algorithm which can not only achieve high data utility and a high degree of privacy, but also offer high time efficiency. To this end, a differentially private FIM algorithm based on the FP growth algorithm, which is referred to as PFP growth. The PFP growth consist of a preprocessing phase and a mining phase. In the preprocessing phase, to improve the utility and privacy tradeoff, a novel smart splitting method is proposed to transform the database. For a given database, the preprocessing phase needs to be performed only once. In the mining phase, to offset the information loss caused by transaction splitting,we devise a run time estimation method to estimate the actual support of itemsets in the original database. In addition, by leveraging the downward closure property, we put forward a dynamic reduction method to dynamically reduce the amount of noise added to guarantee privacy during the mining process. Through formal privacy analysis, PFP growth algorithm is differentially private. Extensive experiments on real datasets illustrate that in PFP growth algorithm substantially outperforms the state of the art technique.
Extracting news text from web pages: an application for the visually impaired
Venue: PETRA
Year: 2015

Apart from the actual content, web pages contain several other components (referred to as boilerplate text) that describes how, and in what context the content should be displayed. We show how content bearing text can be efficiently separated from boilerplate text using a random forest classifier. We compare the performance with another state of the art method for boilerplate detection that uses a decision tree classifier and shallow features extracted from the text. The result is a general improvement using the random forest classifier for both classifying problems analyzed, significantly so for the more complex problem. We also show that a small increase in feature set range can lead to even further improved accuracy. The conclusion is that random forest classification can achieve significantly higher accuracy rates than at least one of the current state of the art methods for content extraction. The results can improve content extraction methods for a variety of applications, including search engine optimization and making the web more accessible for the blind or visually impaired.
Identification, expression, and physiological functions of Siberian hamster gonadotropin inhibitory hormone.
Venue: Endocrinology
Year: 2012

Gonadotropin inhibitory hormone (GnIH) is a hypothalamic neuropeptide that inhibits gonadotropin secretion in birds and mammals. To further understand its physiological roles in mammalian reproduction, we identified its precursor cDNA and endogenous mature peptides in the Siberian hamster brain. The Siberian hamster GnIH precursor cDNA encoded two RFamide related peptide (RFRP) sequences. SPAPANKVPHSAANLPLRF NH(2) (Siberian hamster RFRP 1) and TLSRVPSLPQRF NH(2) (Siberian hamster RFRP 3) were confirmed as mature endogenous peptides by mass spectrometry from brain samples purified by immunoaffinity chromatography. GnIH mRNA expression was higher in long days (LD) compared with short days (SD) GnIH mRNA was also highly expressed in SD plus pinealectomized animals, whereas expression was suppressed by melatonin, a nocturnal pineal hormone, administration. GnIH immunoreactive ir) neurons were localized to the dorsomedial region of the hypothalamus, and GnIH ir fibers projected to hypothalamic and limbic structures. The density of GnIH ir perikarya and fibers were higher in LD and SD plus pinealectomized hamsters than in LD plus melatonin or SD animals. The percentage of GnRH neurons receiving close appositions from GnIH ir fiber terminals was also higher in LD than SD, and GnIH receptor was expressed in GnRH ir neurons. Finally, central administration of hamster RFRP 1 or RFRP 3 inhibited LH release 5 and 30 min after administration in LD. In sharp contrast, both peptides stimulated LH release 30 min after administration in SD. These results suggest that GnIH peptides fine tune LH levels via its receptor expressed in GnRH ir neurons in an opposing fashion across the seasons in Siberian hamsters.
Optimization of minimally invasive radio guided parathyroidectomy: the importance of neck ultrasonography and intraoperative parathyroid hormone assay.
Venue: Endocrine practice official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
Year: 2008

To determine whether close collaboration between a neck ultrasound certified endocrinologist and a skilled endocrine surgeon can optimize minimally invasive radio guided parathyroidectomy (MIRP) surgical outcomes.  Outcome data were collected on patients with primary hyperparathyroidism whom we intended to treat with MIRP at the induction of anesthesia between October 1, 2005, and December 31, 2007. Patients underwent preoperative gamma camera sestamibi scanning (GCSS) intraoperative gamma probe sestamibi scanning (IOSS) and preoperative neck ultrasonography. Intraoperative parathyroid hormone (PTH) monitoring was performed. Postoperative surgical success was defined as a serum calcium concentration between 8.0 and 10.4 mg/dL within 4 weeks of surgery.  During the study period, MIRP was planned for 46 patients. Of the 46 patients, 39 had preoperative neck ultrasonography; 7 underwent evaluation by an endocrinologist or internist who was not ultrasound certified and they therefore did not undergo preoperative ultrasonography. IOSS correctly identified 1 adenomatous gland in 38 of 46 patients (83% while GCSS correctly localized 1 adenomatous gland in 30 of 46 patients (65% In 11 GCSS negative patients, IOSS identified the abnormal gland in 7 (64% while ultrasonography identified the abnormal gland in 8 (73% The surgical approach was converted to traditional parathyroidectomy in 3 patients. Every patient exhibited at least a 51% drop in intraoperative PTH levels with resection of the final adenoma; average decrement for the entire group was 79 8% from the highest baseline level. Forty five patients (98% demonstrated sustained normalization of serum calcium within several days of surgery.  A collaborative endocrinology and surgical endocrine oncology practice arrangement, emphasizing careful preoperative physician supervised neck ultrasonography and the use of intraoperative PTH measurement, optimizes MIRP outcomes.
Everyday emotional dynamics in major depression.
Venue: Emotion
Year: 2018

The present study aimed to investigate the everyday emotional dynamics of depressed individuals, especially the role of emotional inertia, emotional context insensitivity, and emotional variability and instability. Using ecological momentary assessment, 40 currently depressed individuals and 40 healthy controls reported on their current emotional state and current activities 10 times a day for 4 consecutive days. There were no differences in the dynamics of positive affect (PA) between depressed and healthy subjects. Depressed participants' negative affect (NA) however, was found to be more inert than in healthy controls, while at the same time being more variable and more reactive to positive events. There was also an association between emotional instability and depression, but this was rendered nonsignificant when analyses were controlled for emotional variability. Altogether, emotional dynamics of NA appear to be more prominently disturbed in depression compared to PA. Results support earlier findings on NA emotional variability as well as inertia in depressed patients. In addition, there was some evidence for a mood brightening effect in depression.
State of the Art and Emerging Trends in Additive Manufacturing: From Multi Material processes to 3D printed Electronics
Year: 2018

Additive manufacturing is considered a disruptive technology that is expected to revolutionize production technology and affect value chains on a global scale. The scope of accessible materials ranges from polymers to metals and ceramics as well as composites. The keynote will provide an overview of available manufacturing processes for the various material groups and discuss the specific advantages of additive manufacturing e.g. realization of complex geometries, part count and assembly effort reduction, art to part approach in view of selected applications and production scenarios. Further to this, selected emerging trends in additive manufacturing will be presented and discussed, including fabrication of composites, hybrid structures, multi material techniques and associated promises with respect to local tailoring of material properties, and the integration of sensors and/or electronic systems in AM parts.
Biomarker mediated disruption of coffee ring formation as a low resource diagnostic indicator.
Venue: Langmuir the ACS journal of surfaces and colloids
Year: 2012

The ring pattern resulting from the unique microfluidics in an evaporating coffee drop is a well studied mass transport phenomenon generating interest in the research community mostly from a mechanistic perspective. In this report, we describe how biomarker induced particle particle assemblies, magnetic separation, and evaporation driven ring formation can be combined for simple pathogen detection. In this assay design, the presence of biomarkers causes self assembly of a magnetic nanoparticle and a fluorescently labeled micrometer sized particle. A small spherical magnet under the center of the drop prevents these assemblies from migrating to the drop's edge while a nonreactive control particle flows to the edge forming a ring pattern. Thus the presence or absence of biomarker results in distinctly different distributions of particles in the dried drop. Proof of principle studies using poly L histidine, a peptide mimic of the malaria biomarker pfHRPII, show that the predicted particle distributions occur with a limit of detection of approximately 200 300 nM.
Optimization Algorithms for Deep Learning Based Medical Image Segmentations
Year: 2019

Medical image segmentation is one of the fundamental processes to understand and assess the functionality of different organs and tissues as well as quantifying diseases and helping treatment planning. With ever increasing number of medical scans, the automated, accurate, and efficient medical image segmentation is as unmet need for improving healthcare. Recently, deep learning has emerged as one the most powerful methods for almost all image analysis tasks such as segmentation, detection, and classification and so in medical imaging. In this regard, this dissertation introduces new algorithms to perform medical image segmentation for different (a) imaging modalities, (b) number of objects, (c) dimensionality of images, and (d) under varying labeling conditions. First, we study dimensionality problem by introducing a new 2.5D segmentation engine that can be used in single and multi object settings. We propose new fusion strategies and loss functions for deep neural networks to generate improved delineations. Later, we expand the proposed idea into 3D and 4D medical images and develop a "budget (computational) friendly" architecture search algorithm to make this process self contained and fully automated without scarifying accuracy. Instead of manual architecture design, which is often based on plug in and out and expert experience, the new algorithm provides an automated search of successful segmentation architecture within a short period of time. Finally, we study further optimization algorithms on label noise issue and improve overall segmentation problem by incorporating prior information about label noise and object shape information. We conclude the thesis work by studying different network and hyperparameter optimization settings that are fine tuned for varying conditions for medical images. Applications are chosen from cardiac scans (images) and efficacy of the proposed algorithms are demonstrated on several data sets publicly available, and independently validated by blind evaluations.
Smallholder dairy value chain development in India and selected states (Assam and Bihar) Situation analysis and trends
Year: 2014

The International Livestock Research Institute (ILRI) works to improve food security and reduce poverty in developing countries through research for better and more sustainable use of livestock. ILRI is a member of the CGIAR Consortium, a global research partnership of 15 centres working with many partners for a food secure future. ILRI has two main campuses in East Africa and other hubs in East, West and Southern Africa and South, Southeast and East Asia.
High Temperature Hall Effect Sensor Based on Epitaxial Graphene on High Purity Semiinsulating 4H SiC
Venue: IEEE Transactions on Electron Devices
Year: 2019

In this report, we demonstrate a novel high temperature Hall effect sensor that is based on quasi free standing monolayer graphene epitaxially grown on high purity semiinsulating (SI) on axis 4H SiC(0001) substrate in a chemical vapor deposition process. To ensure statistical perspective, characteristics of 23 elements are determined as a function of temperature ranging from 300 to 770 K. Passivated with a 100 nm thick atomic layer deposited aluminum oxide, the sensor offers current mode sensitivity of 80 V/AT with thermal stability of 0.02%/K within the range between 300 and 573 K, and 0.06%/K between 573 and 770 K. The sensor's room temperature output voltage is monitored in the magnetic field from 300 to +300 mT and its offset voltage at 0 T is assessed. Its high temperature electrical properties are explained through a double carrier transport involving spontaneous polarization induced holes in the graphene layer and thermally activated electrons emitted from a deep acceptor level related to silicon vacancy \text{V}_{\textsf {Si}\textsf {1 /2 occupying the {k} site of the 4H SiC lattice. The sensor is compared with a previously reported one on vanadium compensated SI on axis 6H SiC(0001) The new sensor's applicability to magnetic field detection at high temperatures is verified.
Transformable, Freestanding 3D Mesostructures Based on Transient Materials and Mechanical Interlocking
Venue: Advanced Functional Materials
Year: 2019

Areas of application that span almost every class of microsystems technology, from electronics to energy storage devices to chemical/biochemical sensors, can benefit from options in engineering designs that exploit 3D micro/nanostructural layouts. Recently developed methods for forming such systems exploit stress release in prestretched elastomer substrates as a driving force for the assembly of 3D functional microdevices from 2D precursors, including those that rely on the most advanced functional materials and device designs. Here, concepts that expand the options in this class of methods are introduced, to include 1) component parts built with physically transient materials to allow triggered transformation of 3D structures into other shapes and 2) mechanical interlocking elements composed of female type lugs and maletype hooks that activate during the assembly process to irreversibly "lock in" the 3D shapes. Wireless electronic devices demonstrate the utility of these ideas in functional systems.
The Influence of Sport Participation on African American Youth Binge Drinking
Venue: Substance use misuse
Year: 2019

The many negative consequences and high rates of use associated with youth binge drinking make further study into its causes and correlates vital. Sport participation is a relevant factor in predicting drinking behaviors among youth in need of further research.  The current study tests the influence of specific types of sport participation on the binge drinking behaviors of African American youth through binary logistic regression. Study of this particular sample is vital as race and sport participation have been shown to be critical variables associated with variations in drinking behaviors.  Results indicated that various types of sport participation were uniquely associated with drinking behaviors.  Results are framed through social learning, social bond, and the power sport hypothesis in accordance with previous research.
Formative Assessment as an Online Instruction Intervention: Student Engagement, Outcomes, and Perceptions
Venue: Int. J. Distance Educ. Technol.
Year: 2021

Online education has long been suffering from high dropout rate and low achievement However, both asynchronous and synchronous online instructions have to become effective to serve as a quick response to maintain undisrupted learning during the COVID 19 outbreak The purpose of the present study was to examine student engagement, learning outcome, and students' perceptions of an online course featured with frequent tasks, quizzes, and tests as formative assessment Data were collected from the first five weeks of a course that was temporarily converted from blended learning to be fully online in time of school closure Analysis of students' learning records and scores indicated that students engaged themselves actively in all of the online learning activities and had gained high scores in all tasks, quizzes, and tests In addition, students held positive perceptions towards the formative assessment (c) This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License (http:/creativecommons org/licenses/by/4 0/ which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited
A Smart Decision System for Digital Farming
Venue: Agronomy
Year: 2019

New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT) based application development platforms have the potential to run farm management tools capable of monitoring real time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision making systems based on data analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images) and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management.