nils-herrmann/clean_scirepeval huggingface dataset:
Computer based summative assessment of clinical reasoning in clerkships: A mixed method comparison of key feature problems with case based multiple choice questions
Year: 2015

It is yet unclear if there are differences between using electronic key feature problems (KFPs) or electronic case based multiple choice questions (cbMCQ) for the assessment of clinical decision making.  Fifth year medical students were exposed to clerkships which ended with a summative exam. Assessment of knowledge per exam was done by 6 9 KFPs, 9 20 cbMCQ and 9 28 MC questions. Each KFP consisted of a case vignette and three key features (KF) using "long menu" as question format. We sought students' perceptions of the KFPs and cbMCQs in focus groups (n of students=39) Furthermore statistical data of 11 exams (n of students=377) concerning the KFPs and (cb)MCQs were compared.  The analysis of the focus groups resulted in four themes reflecting students' perceptions of KFPs and their comparison with (cb)MCQ: KFPs were perceived as (i) more realistic, (ii) more difficult, (iii) more motivating for the intense study of clinical reasoning than (cb)MCQ and (iv) showed an overall good acceptance when some preconditions are taken into account. The statistical analysis revealed that there was no difference in difficulty; however KFP showed a higher discrimination and reliability (G coefficient) even when corrected for testing times. Correlation of the different exam parts was intermediate.  Students perceived the KFPs as more motivating for the study of clinical reasoning. Statistically KFPs showed a higher discrimination and higher reliability than cbMCQs. Take home messages: Including KFPs with long menu questions into summative clerkship exams seems to offer positive educational effects.
Spatial Action Maps for Mobile Manipulation
Venue: Robotics: Science and Systems
Year: 2020

Typical end to end formulations for learning robotic navigation involve predicting a small set of steering command actions (e.g. step forward, turn left, turn right, etc. from images of the current state (e.g. a bird's eye view of a SLAM reconstruction) Instead, we show that it can be advantageous to learn with dense action representations defined in the same domain as the state. In this work, we present "spatial action maps," in which the set of possible actions is represented by a pixel map (aligned with the input image of the current state) where each pixel represents a local navigational endpoint at the corresponding scene location. Using ConvNets to infer spatial action maps from state images, action predictions are thereby spatially anchored on local visual features in the scene, enabling significantly faster learning of complex behaviors for mobile manipulation tasks with reinforcement learning. In our experiments, we task a robot with pushing objects to a goal location, and find that policies learned with spatial action maps achieve much better performance than traditional alternatives.
A comparative study of Feature Selection techniques for Intrusion Detection
Venue: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom)
Year: 2015

Feature Selection plays an important role in Intrusion Detection, where a large number of features extracted from whole data needs to be analyzed. Feature relevance is the basic measurement in feature selection techniques. In this paper, different feature selection techniques are analyzed. By using pre processed data set, various feature selection techniques are compared. The NSL KDD dataset is used for the evaluation purpose. Various Feature Selection techniques are applied to NSL KDD data set for reduced training test data sets. Naive Bayes Classifier is used to classify in this. We have compared all the experimented results by using different performance metrics like TP rate, FP rate, Precision, ROC area, Kappa Statistic and Classification Accuracy.
Dynamic Object Removal and Spatio Temporal RGB D Inpainting via Geometry Aware Adversarial Learning
Venue: ArXiv
Year: 2020

Dynamic objects have a significant impact on the robot's perception of the environment which degrades the performance of essential tasks such as localization and mapping. In this work, we address this problem by synthesizing plausible color, texture and geometry in regions occluded by dynamic objects. We propose the novel geometry aware DynaFill architecture that follows a coarse to fine topology and incorporates our gated recurrent feedback mechanism to adaptively fuse information from previous timesteps. We optimize our architecture using adversarial training to synthesize fine realistic textures which enables it to hallucinate color and depth structure in occluded regions online in a spatially and temporally coherent manner, without relying on future frame information. Casting our inpainting problem as an image to image translation task, our model also corrects regions correlated with the presence of dynamic objects in the scene, such as shadows or reflections. We introduce a large scale hyperrealistic dataset with RGB D images, semantic segmentation labels, camera poses as well as ground truth RGB D information of occluded regions. Extensive quantitative and qualitative evaluations show that our approach achieves state of the art performance, even in challenging weather conditions. Furthermore, we present results for retrieval based visual localization with the synthesized images that demonstrate the utility of our approach.
Finding top k local users in geo tagged social media data
Venue: 2015 IEEE 31st International Conference on Data Engineering
Year: 2015

Social network platforms and location based services are increasingly popular in people's daily lives. The combination of them results in location based social media where people are connected not only through the friendship in the social network but also by their geographical locations in reality. This duality makes it possible to query and make use of social media data in novel ways. In this work, we formulate a novel and useful problem called top k local user search (TkLUS for short) from tweets with geo tags. Given a location q, a distance r, and a set of keywords W, the TkLUS query finds the top k users who have posted tweets relevant to the desired keywords in W at a place within the distance r from q. TkLUS queries are useful in many application scenarios such as friend recommendation, spatial decision, etc. We design a set of techniques to answer such queries efficiently. First, we propose two local user ranking methods that integrate text relevance and location proximity in a TkLUS query. Second, we construct a hybrid index under a scalable framework, which is aware of keywords as well as locations, to organize high volume geo tagged tweets. Furthermore, we devise two algorithms for processing TkLUS queries. Finally, we conduct an experimental study using real tweet data sets to evaluate the proposed techniques. The experimental results demonstrate the efficiency, effectiveness and scalability of our proposals.
Review of Various Intrusion Detection Methods for Training Data sets
Year: 2017

In the field of Information technology security plays a vital role. Unauthorized entries or any anomalies in system are known as intrusion and detection of these anomalies are known as Intrusion Detection System (IDS) As the attacks have increased in huge numbers over the past few years, IDS is increasingly becoming a critical component to secure the network. Designing of an efficient Intrusion detection system is always challenging tasks for the researchers. IDS performs monitoring and analyzing of network traffic for detecting security violations many researcher suggested data mining technique such as classification, clustering ,pattern matching and rule induction for developing an effective intrusion detection system. In this review paper we are presenting comparative analysis of various IDS by using decision tree based classifiers for data sets such ad Kyoto data sets, KDD Cup 99 data sets. This study will help to develop an efficient IDS system for security system. KeywordsIntrusion detection system, Intruder, Decision tree, and KDD Cup 99, Kyoto datasets
Relationship between diabetes mellitus and the risk of pancreatic cancer
Year: 2018

Patients with pancreatic cancer (PaC) are typically not diagnosed until the disease is at a surgically unresectable advanced stage, making it one of the most lethal malignancies worldwide. Patients with diabetes mellitus (DM) a disease that is increasing in prevalence worldwide, are two to three times more likely to develop PaC. Here, we review the clinical links between DM and PaC and the biological mechanisms that may contribute to these links. In particular, we examine molecular pathways that potentially account for development of PaC in patients who have had DM for various amounts of time. In addition, we also briefly discuss the potential effects of other factors such as different anti diabetic medicines on PaC risk. Observational studies indicate that newly developed DM may be a manifestation of PaC and that the risk for this cancer is increased in long term cases. Notably, development of PaC may be inhibited by using anti diabetic medications, such as metformin. However, DM in long term patients is more likely to be managed with insulin therapy, which is associated with an increased risk for developing PaC. How incretin based drugs and thiazolidinediones impact the incidence of PaC remains unclear. Thus, further research is needed to elucidate these relationships and to determine the causality and impact of DM severity and disease duration as well as residual biases and misclassifications.
From Design for Assembly to Design for Collaborative Assembly Product Design Principles for Enhancing Safety, Ergonomics and Efficiency in Human Robot Collaboration
Year: 2020

Over the past decades, product design has been very much concerned with the topics of Design for Manufacturing and Design for Assembly. The aim was to design products in such a way that they could be manufactured easily and cost effectively using existing production processes. In consequence of the introduction of Industry 4.0 and collaborative robotics in production, production system and process designers are facing new challenges in designing safe, ergonomic and efficient assembly processes. Cost effective collaborative robots enable the automation of production processes even with small quantities and are therefore now of great interest also for small and medium sized companies. In this work, we present the hypothesis, that in addition to manufacturing system design, a corresponding product design can also positively or negatively influence the feasibility of collaborative assembly and workcells. Based on the aforementioned "Design for X" methods many product designers already have certain traditional assembly technologies in mind. However, if these assembly technologies are to be substituted by more innovative approaches to assemble products using human robot collaboration, this often leads to product design being not or only conditionally suitable for collaborative assembly. In this work we show what kind of changes occur in assembly due to the introduction of collaborative robotics and what influence product design has on these changes. As a result of this research, we present design principles and design guidelines for products to enhance safety, ergonomics and efficiency in collaborative assembly.
Simulation Platform for Optimal Pricing of a Distribution Utility with Distributed Energy Resources Operating in a Residential Community
Venue: 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE)
Year: 2020

The current power grid system in the Philippines allows the consumers to utilize any amount of electrical energy as long as they can pay for it. As a response, the utilities need to meet the demand at all times by purchasing energy from the generating plants whatever the cost may be. Today, these distribution utilities started to explore the usage of distributed energy resources in order to cater these varying demands, especially the peak demand. Moreover, the number of consumers which implemented transactive energy schemes also continues to increase. If this interaction between the utility and the consumers will be well coordinated, reduction in the electricity price and energy consumption would be possible. This paper describes a computing tool and was able to (1) solve for the electricity tariff, (2) produce schedule of energy dispatch from distributed generators and storage systems and (3) perform proper demand side management through scheduling of appliance operations, in order to reduce the cost of customer energy consumption at the same time maximize the allowable utility profit.
Soil geochemical anomly characteristics and the geological significance of soil geochemical survey at Kavanga area in Kigoma,Tanzania
Year: 2013

Kavanga area of Kigoma in Tanzania is located in tropic geochemical landscape area with poor geological survey.In certain extent soil geochemical survey is carried out at scale 1..50 000then optimum anomlies are screen out to be working areas for soil geochemical survey at scale 1..10 000and integrated Cu Pb Zn anolies with good morphology and sequential elements overprinting are lineated.The integrated anomlies are checked with field survey and trenching.Malachite is discovered in muddy shale with stable attitude and the anolies are controlled by NNE faults.The metal mineralization is controlled by both of the stratum and faults thus the area is payable for further exploration.
Drug treatments for covid 19: living systematic review and network meta analysis
Venue: BMJ
Year: 2020

To compare the effects of treatments for coronavirus disease 2019 (covid 19) Design Living systematic review and network meta analysis.  US Centers for Disease Control and Prevention COVID 19 Research Articles Downloadable Database, which includes 25 electronic databases and six additional Chinese databases to 20 July 2020.  Randomised clinical trials in which people with suspected, probable, or confirmed covid 19 were randomised to drug treatment or to standard care or placebo. Pairs of reviewers independently screened potentially eligible articles.  After duplicate data abstraction, a bayesian random effects network meta analysis was conducted. Risk of bias of the included studies was assessed using a modification of the Cochrane risk of bias 2.0 tool, and the certainty of the evidence using the grading of recommendations assessment, development and evaluation (GRADE) approach. For each outcome, interventions were classified in groups from the most to the least beneficial or harmful following GRADE guidance.  23 randomised controlled trials were included in the analysis performed on 26 June 2020. The certainty of the evidence for most comparisons was very low because of risk of bias (lack of blinding) and serious imprecision. Glucocorticoids were the only intervention with evidence for a reduction in death compared with standard care (risk difference 37 fewer per 1000 patients, 95% credible interval 63 fewer to 11 fewer, moderate certainty) and mechanical ventilation (31 fewer per 1000 patients, 47 fewer to 9 fewer, moderate certainty) These estimates are based on direct evidence; network estimates for glucocorticoids compared with standard care were less precise because of network heterogeneity. Three drugs might reduce symptom duration compared with standard care: hydroxychloroquine (mean difference 4.5 days, low certainty) remdesivir 2.6 days, moderate certainty) and lopinavir ritonavir 1.2 days, low certainty) Hydroxychloroquine might increase the risk of adverse events compared with the other interventions, and remdesivir probably does not substantially increase the risk of adverse effects leading to drug discontinuation. No other interventions included enough patients to meaningfully interpret adverse effects leading to drug discontinuation.  Glucocorticoids probably reduce mortality and mechanical ventilation in patients with covid 19 compared with standard care. The effectiveness of most interventions is uncertain because most of the randomised controlled trials so far have been small and have important study limitations.  This review was not registered. The protocol is included as a supplement. Readers' note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication.
Deepening Discipleship through Creating a Public Forum for Biblical Storytelling in a Multi Ethnic Community
Year: 2014

Deepening Discipleship through Creating a Public Forum for Biblical Storytelling in a Multi Ethnic Community Virginia Hertenstein Doctor of Ministry School of Theology, Fuller Theological Seminary 2014 The goal of this doctoral project is to pilot a dramatic Scripture reading and narrative storytelling approach to discipleship and evangelism at First Presbyterian Church designed to bridge the cultural divisions in the multi ethnic community of Duncanville, Texas. The pastoral challenge was to strengthen spiritual foundations and deepen participation in evangelistic outreach to help the church become a vibrant reflection of disciples on mission in their changing neighborhood. The goal for the church is to learn to reach out to all ethnicities so that potential church growth reflects the community in which it resides. This project presents an approach to evangelism which strengthens discipleship while developing a method for lay outreach which unleashes the narratives of the Bible through storytelling and dramatic reading. The premise of this project is twofold. The power of the Holy Spirit uniquely speaks through the Bible to bring conversion and deepen discipleship, and the Holy Spirit empowers believers as they fulfill their Christgiven mission to share his message. Congregants are equipped through the spiritual discipline of lectio divina small groups, internalizing the stories, and then telling them in a public community outreach festival designed to draw the diverse cultures in Duncanville together. The outreach festival was held in a park. Biblical stories were about care for the poor, the foreigner, and a concern for justice. The goal was to provide a feel good atmosphere with service oriented booths, food vendors, games, a silent auction and raffle. A combined choir from the church and Duncanville High School performed with storytellers. Approximately one thousand people attended and $51,811 was raised for local charity. The outreach exceeded expectations and has led to a church initiated, public Community Conversation series, with participants including culturally diverse community leaders, opening new doors of opportunity for First Presbyterian Church to further reach out to the community.  Kurt Fredrickson, PhD
Flexible and Stretchable Microwave Microelectronic Devices and Circuits
Venue: IEEE Transactions on Electron Devices
Year: 2017

Electronic systems built on flexible plastic films and stretchable rubber sheets have attracted new applications in many emerging fields. Integration of high speed electronics such as microwave power amplifiers and switches can extend the applications even further with wireless capabilities. As such, flexible and stretchable microwave electronics represent opportunities for future electronics where remote capabilities are desired. Here, we review advances in numerous types of microelectronic devices used for fast, flexible, and stretchable electronic devices, as well as flexible and stretchable passive elements and circuitries. We first introduce the challenges associated with design and fabrication, and the characteristics required for high frequency operation of the devices on foreign substrates. Second, we review the recent efforts that were made utilizing different types of high performance semiconductors, which are ideal for high speed flexible and stretchable electronics, such as silicon, compound semiconductors, and 1 D and 2 D materials. Third, passive electronic components fabricated on such substrates, including inductors, capacitors, and transmission lines, are reviewed. Finally, we discuss the flexible and stretchable microwave electronics at the circuit level and review the recent advances in making numerous types of flexible and stretchable microwave circuits for diverse applications.
A classification approach based on the outranking model for multiple criteria ABC analysis
Year: 2016

The multiple criteria ABC analysis is widely used in inventory management, and it can help organizations to assign inventory items into different classes with respect to several evaluation criteria. Many approaches have been proposed in the literature for addressing such a problem. However, most of these approaches are fully compensatory in multiple criteria aggregation. This means that an item scoring badly on one or more key criteria could be placed in good classes because these bad performances could be compensated by other criteria. Thus, it is necessary to consider the non compensation in the multiple criteria ABC analysis. To the best of our knowledge, the ABC classification problem with non compensation among criteria has not been studied sufficiently. We thus propose a new classification approach based on the outranking model to cope with such a problem in this paper. However, the relational nature of the outranking model makes the search for the optimal classification solution a complex combinatorial optimization problem. It is very time consuming to solve such a problem using mathematical programming techniques when the inventory size is large. Therefore, we combine the clustering analysis and the simulated annealing algorithm to search for the optimal classification. The clustering analysis groups similar inventory items together and builds up the hierarchy of clusters of items. The simulated annealing algorithm searches for the optimal classification on different levels of the hierarchy. The proposed approach is illustrated by a practical example from a Chinese manufacturer. Furthermore, we validate the performance of the approach through experimental investigation on a large set of artificially generated data at the end of the paper.
Parallel basal ganglia circuits for voluntary and automatic behaviour to reach rewards.
Venue: Brain a journal of neurology
Year: 2015

The basal ganglia control body movements, value processing and decision making. Many studies have shown that the inputs and outputs of each basal ganglia structure are topographically organized, which suggests that the basal ganglia consist of separate circuits that serve distinct functions. A notable example is the circuits that originate from the rostral (head) and caudal (tail) regions of the caudate nucleus, both of which target the superior colliculus. These two caudate regions encode the reward values of visual objects differently: flexible (short term) values by the caudate head and stable (long term) values by the caudate tail. These value signals in the caudate guide the orienting of gaze differently: voluntary saccades by the caudate head circuit and automatic saccades by the caudate tail circuit. Moreover, separate groups of dopamine neurons innervate the caudate head and tail and may selectively guide the flexible and stable learning/memory in the caudate regions. Studies focusing on manual handling of objects also suggest that rostrocaudally separated circuits in the basal ganglia control the action differently. These results suggest that the basal ganglia contain parallel circuits for two steps of goal directed behaviour: finding valuable objects and manipulating the valuable objects. These parallel circuits may underlie voluntary behaviour and automatic skills, enabling animals (including humans) to adapt to both volatile and stable environments. This understanding of the functions and mechanisms of the basal ganglia parallel circuits may inform the differential diagnosis and treatment of basal ganglia disorders.