nils-herrmann/clean_scirepeval huggingface dataset:
DPC-Net: Deep Pose Correction for Visual Localization

We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. In contrast to other methods that completely replace a classical visual estimator with a deep network, we propose an approach that uses a convolutional neural network to learn difficult-to-model corrections to the estimator from ground-truth training data. To this end, we derive a novel loss function for learning $ \mathrm{SE}(3)$  corrections based on a matrix Lie groups approach, with a natural formulation for balancing translation and rotation errors. We use this loss to train a deep pose correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment. Using the KITTI odometry dataset, we demonstrate significant improvements to the accuracy of a computationally-efficient sparse stereo visual odometry pipeline, that render it as accurate as a modern computationally-intensive dense estimator. Further, we show how DPC-Net can be used to mitigate the effect of poorly calibrated lens distortion parameters.
The Effect of Foam Rolling for Three Consecutive Days on Muscular Efficiency and Range of Motion

Foam rolling (FR) has been shown to alleviate some symptoms of exercise-induced muscle damage and has been suggested to increase range of motion (ROM) without negatively impacting strength. However, it is unclear what neuromuscular effects, if any, mediate these changes.



In a randomized, crossover design, 16 healthy active males completed 2 min of rest or FR of the knee extensors on three consecutive days. Mechanical properties of vastus lateralis (VL) and rectus femoris (RF) were assessed via Tensiomyography. Knee extension maximal voluntary contraction (MVC) and knee flexion ROM were also assessed, and surface electromyography amplitude (RMS) was recorded during a submaximal isometric contraction (50% of MVC). Measures were performed before and after (0, 15, and 30 min) FR or rest.



MVC was reduced on subsequent days in the rest condition compared to FR (p = 0.002, pη2 = 0.04); ROM was not different across time or condition (p = 0.193, pη2 = 0.01). Stiffness characteristics of the VL were different on the third day of FR (p = 0.002, pη2 = 0.03). RMS was statistically reduced 0, 15, and 30 min after FR compared to rest (p = 0.006, pη2 = 0.03; p = 0.003, pη2 = 0.04; p = 0.002, pη2 = 0.04).



Following FR, MVC was elevated compared to rest and RMS was transiently reduced during a submaximal task. Excitation efficiency of the involved muscles may have been enhanced by FR, which protected against the decline in MVC which was observed with rest.
Dependency-based Hybrid Trees for Semantic Parsing

We propose a novel dependency-based hybrid tree model for semantic parsing, which converts natural language utterance into machine interpretable meaning representations. Unlike previous state-of-the-art models, the semantic information is interpreted as the latent dependency between the natural language words in our joint representation. Such dependency information can capture the interactions between the semantics and natural language words. We integrate a neural component into our model and propose an efficient dynamicprogramming algorithm to perform tractable inference. Through extensive experiments on the standard multilingual GeoQuery dataset with eight languages, we demonstrate that our proposed approach is able to achieve state-ofthe-art performance across several languages. Analysis also justifies the effectiveness of using our new dependency-based
Predicting Attrition in Financial Data with Machine Learning Algorithms

For most businesses there are costs involved when acquiring new customers and having longer relationships with customers is therefore often more profitable. Predicting if an individual is prone to leave the business is then a useful tool to help any company take actions to mitigate this cost. The event when a person ends their relationship with a business is called attrition or churn. Predicting peoples actions is however hard and many different factors can affect their choices. This paper investigates different machine learning methods for predicting attrition in the customer base of a bank. Four different methods are chosen based on the results they have shown in previous research and these are then tested and compared to find which works best for predicting these events. Four different datasets from two different products and with two different applications are created from real world data from a European bank. All methods are trained and tested on each dataset. The results of the tests are then evaluated and compared to find what works best. The methods found in previous research to most reliably achieve good results in predicting churn in banking customers are the Support Vector Machine, Neural Network, Balanced Random Forest, and the Weighted Random Forest. The results show that the Balanced Random Forest achieves the best results with an average AUC of 0.698 and an average F-score of 0.376. The accuracy and precision of the model are concluded to not be enough to make definite decisions but can be used with other factors such as profitability estimations to improve the effectiveness of any actions taken to prevent the negative effects of churn.
SecureDroid: Enhancing Security of Machine Learning-based Detection against Adversarial Android Malware Attacks

With smart phones being indispensable in people's everyday life, Android malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving Android malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic Android malware detection. In these systems, based on different feature representations, various kinds of classifiers are constructed to detect Android malware. Unfortunately, as classifiers become more widely deployed, the incentive for defeating them increases. In this paper, we explore the security of machine learning in Android malware detection on the basis of a learning-based classifier with the input of a set of features extracted from the Android applications (apps). We consider different importances of the features associated with their contributions to the classification problem as well as their manipulation costs, and present a novel feature selection method (named SecCLS) to make the classifier harder to be evaded. To improve the system security while not compromising the detection accuracy, we further propose an ensemble learning approach (named SecENS) by aggregating the individual classifiers that are constructed using our proposed feature selection method SecCLS. Accordingly, we develop a system called SecureDroid which integrates our proposed methods (i.e., SecCLS and SecENS) to enhance security of machine learning-based Android malware detection. Comprehensive experiments on the real sample collections from Comodo Cloud Security Center are conducted to validate the effectiveness of SecureDroid against adversarial Android malware attacks by comparisons with other alternative defense methods. Our proposed secure-learning paradigm can also be readily applied to other malware detection tasks.
A deep learning architecture for sentiment analysis

The fabulous results of Deep Convolution Neural Networks in computer vision and image analysis have recently attracted considerable attention from researchers of other application domains as well. In this paper we present NgramCNN, a neural network architecture we designed for sentiment analysis of long text documents. It uses pretrained word embeddings for dense feature representation and a very simple single-layer classifier. The complexity is encapsulated in feature extraction and selection parts that benefit from the effectiveness of convolution and pooling layers. For evaluation we utilized different kinds of emotional text datasets and achieved an accuracy of 91.2 % accuracy on the popular IMDB movie reviews. NgramCNN is more accurate than similar shallow convolution networks or deeper recurrent networks that were used as baselines. In the future, we intent to generalize the architecture for state of the art results in sentiment analysis of variable-length texts.
Lexical Pragmatics

Lexical Pragmatics is a research field that tries to give a systematic and explanatory account of pragmatic phenomena that are connected with the semantic underspecification of lexical items. Cases in point are the pragmatics of adjectives, systematic polysemy, the distribution of lexical and productive causatives, blocking phenomena, the interpretation of compounds, and many phenomena presently discussed within the framework of Cognitive Semantics. The approach combines a constrained-based semantics with a general mechanism of conversational implicature. The basic pragmatic mechanism rests on conditions of updating the common ground and allows to give a precise explication of notions as generalized conversational implicature and pragmatic anomaly. The fruitfulness of the basic account is established by its application to a variety of recalcitrant phenomena among which its precise treatment of Atlas & Levinson's Qand I-principles and the formalization of the balance between informativeness and efficiency in natural language processing (Horn's division of pragmatic labor) deserve particular mention. The basic mechanism is subsequently extended by an abductive reasoning system which is guided by subjective probability. The extended mechanism turned out to be capable of giving a principled account of lexical blocking, the pragmatics of adjectives, and systematic polysemy.
Ambidextrous leadership , ambidextrous employee , and the interaction between ambidextrous leadership and employee innovative performance

Leader opening and closing behaviors are assumed to foster high levels of employee exploration and exploitation behaviors, hence motivating employee innovative performance. Applying the ambidexterity theory of leadership for innovation, results revealed that leader opening and closing behaviors positively predicted employee exploration and exploitation behaviors, respectively, above and beyond the control variables. Moreover, results showed that employee innovative performance was significantly predicted by leader opening behavior, leader closing behavior, and the interaction between leaders’ opening and closing behaviors, above and beyond control variables.
Tele-ophthalmology for diabetic retinopathy screening: 8 years of experience.

To describe the results of a diabetic retinopathy screening program implemented in a primary care area.



A retrospective study was conducted using data automatically collected since the program began on 1 January 2007 until 31 December 2015.



The number of screened diabetic patients has progressively increased, from 7,173 patients in 2007 to 42,339 diabetic patients in 2015. Furthermore, the ability of family doctors to correctly interpret retinographies has improved, with the proportion of retinal images classified as normal having increased from 55% in 2007 to 68% at the end of the study period. The proportion of non-evaluable retinographies decreased to 7% in 2015, having peaked at 15% during the program. This was partly due to a change in the screening program policy that allowed the use of tropicamide. The number of severe cases detected has declined, from 14% with severe non-proliferative and proliferativediabetic retinopathy in the initial phase of the program to 3% in 2015.



Diabetic eye disease screening by tele-ophthalmology has shown to be a valuable method in a growing population of diabetics. It leads to a regular medical examination of patients, helps ease the workload of specialised care services and favours the early detection of treatable cases. However, the results of implementing a program of this type are not immediate, achieving only modest results in the early years of the project that have improved over subsequent years.
Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach Abstract Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences, challenging. At the same time, advances in technology and chan

Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences, challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies and theories such as co-creation processes, interactions and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an “open learning” model. The ability to timely assess customer experience feedback represents a pre-requisite for successful co-creation processes in a service environment.  Ordenes, F. V., Theodoulidis, B., Burton, J., Gruber, T., & Zaki, M. (2014).  Using Text Mining A Linguistics-Based Approach.
CANNABIS IN THE MANAGEMENT AND TREATMENT OF SEIZURES AND EPILEPSY : A SCIENTIFIC REVIEW Author

The American Herbal Pharmacopoeia (AHP) today announced the release of a section of the soon-to-be-completed Cannabis Therapeutic Compendium Cannabis in the Management and Treatment of Seizures and Epilepsy. This scientific review is one of numerous scientific reviews that will encompass the broad range of science regarding the therapeutic effects and safety of cannabis. In recent months there has been considerable attention given to the potential benefit of cannabis for treating intractable seizure disorders including rare forms of epilepsy. For this reason, the author of the section, Dr. Ben Whalley, and AHP felt it important to release this section, in its near-finalized form, into the public domain for free dissemination. The full release of AHP's Therapeutic Compendium is scheduled for early 2014. Dr. Whalley is a Senior Lecturer in Pharmacology and Pharmacy Director of Research at the School of Pharmacy of the University of Reading in the United Kingdom. He is also a member of the UK Epilepsy Research Network. Dr. Whalley's research interests lie in investigating neuronal processes that underlie complex physiological functions such as neuronal hyperexcitability states and their consequential disorders such as epilepsy, ataxia and dystonias, as well as learning and memory. Since 2003, Dr. Whalley has authored and co-authored numerous scientific peer-reviewed papers on the potential effects of cannabis in relieving seizure disorders and investigating the underlying pathophysiological mechanisms of these disorders. The release of this comprehensive review is timely given the growing claims being made for cannabis to relieve even the most severe forms of seizures. According to Dr. Whalley: " Recent announcements of regulated human clinical trials of pure components of cannabis for the treatment of epilepsy have raised hopes among patients with drug-resistant epilepsy, their caregivers, and clinicians. Also, claims in the media of the successful use of cannabis extracts for the treatment of epilepsies, particularly in children, have further highlighted the urgent need for new and effective treatments. " However, Dr. Whalley added, " We must bear in mind that the use of any new treatment, particularly in the critically ill, carries inherent risks. Releasing this section of the monograph into the public domain at this time provides clinicians, patients, and their caregivers with a single document that comprehensively summarizes the scientific knowledge to date regarding cannabis and epilepsy and so fully support informed, evidence-based decision making. " This release also follows recommendations of the Epilepsy Foundation, which has called for increasing medical …
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

Deep Learning has had a transformative impact on Computer Vision, but for all of the success there is also a significant cost. This is that the models and procedures used are so complex and intertwined that it is often impossible to distinguish the impact of the individual design and engineering choices each model embodies. This ambiguity diverts progress in the field, and leads to a situation where developing a state-of-the-art model is as much an art as a science. As a step towards addressing this problem we present a massive exploration of the effects of the myriad architectural and hyperparameter choices that must be made in generating a state-of-the-art model. The model is of particular interest because it won the 2017 Visual Question Answering Challenge. We provide a detailed analysis of the impact of each choice on model performance, in the hope that it will inform others in developing models, but also that it might set a precedent that will accelerate scientific progress in the field.
Game theory applications in wireless networks : A

Game theory is a set of tools developed to model interactions between agents with conflicting interests . It is a field of applied mathematics that defines and evaluates interactive decision situations. It provides analytical tools to predict the outcome of complicated interactions between rational entities, where rationality demands strict adherence to a strategy based on observed or measured results [13]. Originally developed to model problems in the field of economics, game theory has recently been applied to network problems, in most cases to solve the resource allocation problems in a competitive environment. The reason that game theory is an adapted choice for studying cooperative communications is various. Nodes in the network are independent agents, making decisions only for their own interests. Game theory provides us sufficient theoretical tools to analyze the network users’ behaviors and actions. Game theory, also primarily deals with distributed optimization, which often requires local information only. Thus it enables us to design distributed algorithms. . This article surveys the literature on game theory as they apply to wireless networks. First, a brief overview of classifications of games, important definitions used in games (Nash Equilibrium, Pareto efficiency, Pure, Mixed and Fully mixed strategies) and game models are presented. Then, we identified five areas of application of game theory in wireless networks; therefore, we discuss related work to game theory in communication networks, cognitive radio networks, wireless sensor networks, resource allocation and power control. Finally, we discuss the limitations of the application of game theory in wireless networks.
A forward converter topology employing a resonant auxiliary circuit to achieve soft switching and power transformer resetting

This paper presents a forward converter topology that employs a small resonant auxiliary circuit. The advantages of the proposed topology include soft switching in both the main and auxiliary switches, recovery of the leakage inductance energy, simplified power transformer achieving self-reset without using the conventional reset winding, simple gate drive and control circuit, etc. Steady-state analysis is performed herein, and a design procedure is presented for general applications. A 35–75-Vdc to 5 Vdc 100-W prototype converter switched at a frequency of 200 kHz is built to verify the design, and 90% overall efficiency has been obtained experimentally at full load.
Beyond the Factor of Safety : Developing Fragility Curves to Characterize System Reliability

Fragility curves are becoming increasingly common components of flood risk assessments. This report introduces the concept of the fragility curve and shows how fragility curves are related to more familiar reliability concepts, such as the deterministic factor of safety and the relative reliability index. Examples of fragility curves are identified in the literature on structures and risk assessment to identify what methods have been used to develop fragility curves in practice. Four basic approaches are identified: judgmental, empirical, hybrid, and analytical. Analytical approaches are, by far, the most common method encountered in the literature. This group of methods is further decomposed based on whether the limit state equation is an explicit function or an implicit function and on whether the probability of failure is obtained using analytical solution methods or numerical solution methods. Advantages and disadvantages of the various approaches are considered.  The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.
Customer Satisfaction in the Hotel Industry : A Case Study from Sicily

In order to be successful in the market it is not sufficient to attract new customers managers must concentrate on retaining existing customers implementing effective policies of customer satisfaction and loyalty. In hotel industry customer satisfaction is largely hooked upon quality of service. A management approach focused on customer satisfaction can improve customer loyalty, thus increasing the positive image of the touristic destination. Hence, exploring the importance for customers of hotel attributes in hotel selection is indispensable. Research on the topic of guest satisfaction, which translates into the consideration of whether or not customers will return to a hotel or advise it to other tourists, is pivotal to the success of the hospitality business. Neglecting to pay attention to those hotel attributes considered most important by guests, may lead to negative evaluation of the hotel, thus restricting the chance of repeat patronage. We perform a qualitative analysis of a large hotel in Sicily (IT), the Sporting Club Hotel in the town Cefalù, using the Critical Incident Approach (Hayes, 2008). Through the analysis of this case we evaluate the overall customer satisfaction level for the hotel and for each service supplied. We conclude discussing the result and proposing improvement in customer satisfaction management of the hotel.