nils-herrmann/clean_scirepeval huggingface dataset:
The phonological loop as a language learning device.

A relatively simple model of the phonological loop (A. D. Baddeley, 1986), a component of working memory, has proved capable of accommodating a great deal of experimental evidence from normal adult participants, children, and neuropsychological patients. Until recently, however, the role of this subsystem in everyday cognitive activities was unclear. In this article the authors review studies of word learning by normal adults and children, neuropsychological patients, and special developmental populations, which provide evidence that the phonological loop plays a crucial role in learning the novel phonological forms of new words. The authors propose that the primary purpose for which the phonological loop evolved is to store unfamiliar sound patterns while more permanent memory records are being constructed. Its use in retaining sequences of familiar words is, it is argued, secondary.
A Survey of SDN Security Research

Software defined networking (SDN) has established a new method for creating and administering networks, but has also changed the attack surface that is presented by networks. SDN provides several features that allow for easy mitigation of certain types of attacks, such as DoS, and allows for mitigation of other attacks with more work. However, SDN also introduces new vulnerabilities that are not present in traditional networks, such as a communication bottleneck between the control-plane and the data-plane. Many new technologies and techniques have been proposed to solve SDN security vulnerabilities and some additional work can be applied address them as well. Current research in SDN follows several identifiable trends that are related to the state of deployment of SDN technologies. As OpenFlow is the most popular implementation of SDN and is currently used in production settings, much research has been performed to utilize and improve the protocol. However, there is another research trend that has produced work that is applicable to SDN in general, including architectures that provide more flexibility than OpenFlow. Future research is likely to follow these trends by improving the OpenFlow protocol and proposing more general alternatives, and this research will include the further development of tools for the testing of network designs and the research of optimizations for OpenFlow when it is used in production environments. In this paper, I present a survey of current research on SDN security and other work in the field of SDNs that is applicable to security and a prediction of the directions of future research in SDN security.
Peer Coaching Through mHealth Targeting Physical Activity in People With Parkinson Disease: Feasibility Study

Long-term engagement in exercise and physical activity mitigates the progression of disability and increases quality of life in people with Parkinson disease (PD). Despite this, the vast majority of individuals with PD are sedentary. There is a critical need for a feasible, safe, acceptable, and effective method to assist those with PD to engage in active lifestyles. Peer coaching through mobile health (mHealth) may be a viable approach.



The purpose of this study was to develop a PD-specific peer coach training program and a remote peer-mentored walking program using mHealth technology with the goal of increasing physical activity in persons with PD. We set out to examine the feasibility, safety, and acceptability of the programs along with preliminary evidence of individual-level changes in walking activity, self-efficacy, and disability in the peer mentees.



A peer coach training program and a remote peer-mentored walking program using mHealth was developed and tested in 10 individuals with PD. We matched physically active persons with PD (peer coaches) with sedentary persons with PD (peer mentees), resulting in 5 dyads. Using both Web-based and in-person delivery methods, we trained the peer coaches in basic knowledge of PD, exercise, active listening, and motivational interviewing. Peer coaches and mentees wore FitBit Zip activity trackers and participated in daily walking over 8 weeks. Peer dyads interacted daily via the FitBit friends mobile app and weekly via telephone calls. Feasibility was determined by examining recruitment, participation, and retention rates. Safety was assessed by monitoring adverse events during the study period. Acceptability was assessed via satisfaction surveys. Individual-level changes in physical activity were examined relative to clinically important differences.



Four out of the 5 peer pairs used the FitBit activity tracker and friends function without difficulty. A total of 4 of the 5 pairs completed the 8 weekly phone conversations. There were no adverse events over the course of the study. All peer coaches were "satisfied" or "very satisfied" with the training program, and all participants were "satisfied" or "very satisfied" with the peer-mentored walking program. All participants would recommend this program to others with PD. Increases in average steps per day exceeding the clinically important difference occurred in 4 out of the 5 mentees.



Remote peer coaching using mHealth is feasible, safe, and acceptable for persons with PD. Peer coaching using mHealth technology may be a viable method to increase physical activity in individuals with PD. Larger controlled trials are necessary to examine the effectiveness of this approach.
Visualization of omics data for systems biology

High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions.
DIAGNOSIS OF LYME DISEASE

The use of serologic testing and its value in the diagnosis of Lyme disease remain confusing and controversial for physicians, especially concerning persons who are at low risk for the disease. The approach to diagnosing Lyme disease varies depending on the probability of disease (based on endemicity and clinical findings) and the stage at which the disease may be. In patients from endemic areas, Lyme disease may be diagnosed on clinical grounds alone in the presence of erythema migrans. These patients do not require serologic testing, although it may be considered according to patient preference. When the pretest probability is moderate (e.g., in a patient from a highly or moderately endemic area who has advanced manifestations of Lyme disease), serologic testing should be performed with the complete two-step approach in which a positive or equivocal serology is followed by a more specific Western blot test. Samples drawn from patients within four weeks of disease onset are tested by Western blot technique for both immunoglobulin M and immunoglobulin G antibodies; samples drawn more than four weeks after disease onset are tested for immunoglobulin G only. Patients who show no objective signs of Lyme disease have a low probability of the disease, and serologic testing in this group should be kept to a minimum because of the high risk of false-positive results. When unexplained nonspecific systemic symptoms such as myalgia, fatigue, and paresthesias have persisted for a long time in a person from an endemic area, serologic testing should be performed with the complete two-step approach described above.
Detecting Learning vs Memorization in Deep Neural Networks using Shared Structure Validation Sets

The roles played by learning and memorization represent an important topic in deep learning research. Recent work on this subject has shown that the optimization behavior of DNNs trained on shuffled labels is qualitatively different from DNNs trained with real labels. Here, we propose a novel permutation approach that can differentiate memorization from learning in deep neural networks (DNNs) trained as usual (i.e., using the real labels to guide the learning, rather than shuffled labels). The evaluation of weather the DNN has learned and/or memorized, happens in a separate step where we compare the predictive performance of a shallow classifier trained with the features learned by the DNN, against multiple instances of the same classifier, trained on the same input, but using shuffled labels as outputs. By evaluating these shallow classifiers in validation sets that share structure with the training set, we are able to tell apart learning from memorization. Application of our permutation approach to multi-layer perceptrons and convolutional neural networks trained on image data corroborated many findings from other groups. Most importantly, our illustrations also uncovered interesting dynamic patterns about how DNNs memorize over increasing numbers of training epochs, and support the surprising result that DNNs are still able to learn, rather than only memorize, when trained with pure Gaussian noise as input.
Secured Smart Healthcare Monitoring System Based on Iot Bhoomika

Technology plays the major role in healthcare not only for sensory devices but also in communication, recording and display device. It is very important to monitor various medical parameters and post operational days. Hence the latest trend in Healthcare communication method using IOT is adapted. Internet of things serves as a catalyst for the healthcare and plays prominent role in wide range of healthcare applications. In this project the PIC18F46K22 microcontroller is used as a gateway to communicate to the various sensors such as temperature sensor and pulse oximeter sensor. The microcontroller picks up the sensor data and sends it to the network through Wi-Fi and hence provides real time monitoring of the health care parameters for doctors. The data can be accessed anytime by the doctor. The controller is also connected with buzzer to alert the caretaker about variation in sensor output. But the major issue in remote patient monitoring system is that the data as to be securely transmitted to the destination end and provision is made to allow only authorized user to access the data. The security issue is been addressed by transmitting the data through the password protected Wi-Fi module ESP8266 which will be encrypted by standard AES128 and the users/doctor can access the data by logging to the html webpage. At the time of extremity situation alert message is sent to the doctor through GSM module connected to the controller. Hence quick provisional medication can be easily done by this system. This system is efficient with low power consumption capability, easy setup, high performance and time to time response.
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference

Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing. It requires to infer the logical relationship between two given sentences. While current approaches mostly focus on the interaction architectures of the sentences, in this paper, we propose to transfer knowledge from some important discourse markers to augment the quality of the NLI model. We observe that people usually use some discourse markers such as “so” or “but” to represent the logical relationship between two sentences. These words potentially have deep connections with the meanings of the sentences, thus can be utilized to help improve the representations of them. Moreover, we use reinforcement learning to optimize a new objective function with a reward defined by the property of the NLI datasets to make full use of the labels information. Experiments show that our method achieves the state-of-the-art performance on several large-scale datasets.
Early Stage Integrated Parametric Ship Design

Innovative ship design projects often require an extensive concept design phase to allow a wide range of potential solutions to be investigated, identifying which best suits the requirements. In these situations, the majority of ship design tools do not provide the best solution, limiting quick reconfiguration by focusing on detailed definition only. Parametric design, including generation of the hull surface, can model topology as well as geometry offering advantages often not exploited. Paramarine is an integrated ship design environment that is based on an objectorientated framework which allows the parametric connection of all aspects of both the product model and analysis together. Design configuration is managed to ensure that relationships within the model are topologically correct and kept up to date. While this offers great flexibility, concept investigation is streamlined by the Early Stage Design module, based on the (University College London) Functional Building Block methodology, collating design requirements, product model definition and analysis together to establish the form, function and layout of the design. By bringing this information together, the complete design requirements for the hull surface itself are established and provide the opportunity for parametric hull form generation techniques to have a fully integrated role in the concept design process. This paper explores several different hull form generation techniques which have been combined with the Early Stage Design module to demonstrate the capability of this design partnership.
Neural Lattice Language Models

In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models. These models construct a lattice of possible paths through a sentence and marginalize across this lattice to calculate sequence probabilities or optimize parameters. This approach allows us to seamlessly incorporate linguistic intuitions — including polysemy and the existence of multiword lexical items — into our language model. Experiments on multiple language modeling tasks show that English neural lattice language models that utilize polysemous embeddings are able to improve perplexity by 9.95% relative to a word-level baseline, and that a Chinese model that handles multi-character tokens is able to improve perplexity by 20.94% relative to a character-level baseline.
Symmetric Variational Autoencoder and Connections to Adversarial Learning

A new form of the variational autoencoder (VAE) is proposed, based on the symmetric KullbackLeibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sVAE) has close connections to previously developed adversarial-learning methods. This relationship helps unify the previously distinct techniques of VAE and adversarially learning, and provides insights that allow us to ameliorate shortcomings with some previously developed adversarial methods. In addition to an analysis that motivates and explains the sVAE, an extensive set of experiments validate the utility of the approach.
Understanding brand performance measures : using Dirichlet benchmarks

Sales of a brand are determined by measures such as how many customers buy the brand, how often, and how much they also buy other brands. Scanner panel operators routinely report these ‘‘brand performance measures’’ (BPMs) to their clients. In this position paper, we consider how to understand, interpret, and use these measures. The measures are shown to follow well-established patterns. One is that big and small brands differ greatly in how many buyers they have, but usually far less in how loyal these buyers are. The Dirichlet model predicts these patterns. It also provides a broader framework for thinking about all competitive repeat-purchase markets—from soup to gasoline, prescription drugs to aviation fuel, where there are large and small brands, and light and heavy buyers, in contexts as diverse as the United States, United Kingdom, Japan, Germany, and Australasia. Numerous practical uses of the framework are illustrated: auditing the performance of established brands, predicting and evaluating the performance of new brands, checking the nature of unfamiliar markets, of partitioned markets, and of dynamic market situations more generally (where the Dirichlet provides theoretical benchmarks for price promotions, advertising, etc.). In addition, many implications for our understanding of consumers, brands, and the marketing mix logically follow from the Dirichlet framework. In repeat-purchase markets, there is often a lack of segmentation between brands and the typical consumer exhibits polygamous buying behavior (though there might be strong segmentation at the category level). An understanding of these applications and implications leads to consumer insights, imposes constraints on marketing action, and provides norms for evaluating brands and for assessing marketing initiatives. D 2003 Elsevier Inc. All rights reserved.
Myo-Inositol in the Treatment of Teenagers Affected by PCOS

To compare the effectiveness of myo-inositol (MI) and oral contraceptive pills (OCPs) in monotherapy and MI in combination with OCPs in the treatment of teenagers affected by polycystic ovary syndrome (PCOS).  61 adolescent girls aged 13-19 years, with PCOS, were involved in the prospective, open-label study. Patients were randomized into three groups: I group, 20 patients receiving drospirenone 3 mg/ethinyl estradiol 30 μg; II group, 20 patients receiving 4 g myo-inositol plus 400 mg folic acid; III group, 21 patients receiving both medications.  After receiving MI significant reduction in weight, BMI, glucose, C-peptide, insulin, HOMA-IR, FT, and LH was detected. The levels of SHBG, TT, FAI, DHEA-S, and AMH did not change statistically significantly. After receiving OCPs weight and BMI slightly increased, but metabolic parameters did not change. Combination of MI and OCPs did not change weight and BMI, but reduction in C-peptide, insulin, and HOMA-IR was detected. TT, FT, FAI, DHEA-S, LH, and AMH levels decreased and SHBG increased.  Administration of MI is a safe and effective method to prevent and correct metabolic disorders in teenagers affected by PCOS. With combination of MI and OCPs antiandrogenic effects are enhanced, negative impact of OCPs on weight gain is balanced, and metabolic profile is improved.
Adversarial Learning of Portable Student Networks

Effective methods for learning deep neural networks with fewer parameters are urgently required, since storage and computations of heavy neural networks have largely prevented their widespread use on mobile devices. Compared with algorithms which directly remove weights or filters for obtaining considerable compression and speed-up ratios, training thin deep networks exploiting the student-teacher learning paradigm is more flexible. However, it is very hard to determine which formulation is optimal to measure the information inherited from teacher networks. To overcome this challenge, we utilize the generative adversarial network (GAN) to learn the student network. In practice, the generator is exactly the student network with extremely less parameters and the discriminator is used as a teaching assistant for distinguishing features extracted from student and teacher networks. By simultaneously optimizing the generator and the discriminator, the resulting student network can produce features of input data with the similar distribution as that of features of the teacher network. Extensive experimental results on benchmark datasets demonstrate that the proposed method is capable of learning well-performed portable networks, which is superior to the state-of-the-art methods.
Opportunity Makes the Thief Practical theory for crime prevention

The Policing and Reducing Crime Unit (PRC Unit) was formed in 1998 as a result of the merger of the Police Research Group (PRG) and the Research and Statistics Directorate. The PRC Unit is now one part of the Research, Development and Statistics Directorate of the Home Office. The PRC Unit carries out and commissions research in the social and management sciences on policing and crime reduction, broadening out the role that PRG played. The PRC Unit has now combined PRG's two main series into the Police Research Series, continuing PRG's earlier work. This will present research material on crime prevention and detection as well as police management and organisation issues. Research commissioned by PRG will appear as a PRC Unit publication. Throughout the text there may be references to PRG and these now need to be understood as relating to the PRC Unit. C Forewor d Along with personal and social factors that are usually thought of as causes, this report makes the case for seeing opportunity as a third principal cause of crime. Combining the wisdom from several recent and converging opportunity theories, this report is timely in view of the local crime and disorder strategies that will have to be developed over the next few months. It improves our understanding of how opportunities to commit crime contribute to criminal motivation, and provides a perspective for developing workable solutions to prevent specific crime problems. Acknowledgements We owe an intellectual debt of gratitude to many colleagues whose work has had a mojor impact on our own. We have also been influenced by the work of Johannes Knutsson and Eckart Kuhlhorn in Sweden and Ross Homel in Australia. Many others have changed our thinking in small ways and large. Lastly, we would like to thank Wes Skogan, professor at Northwestern University in Chicago for agreeing to referee this report. of Illinois. He is best known as a criminological theorist, specifically as an originator of routine activity theory. In recent years, he has turned his attention to crime prevention. His most recent book is Crime and Everyday Life , of which the second edition was published in 1998. Ronald V. Clarke is Professor and Dean at the Rutgers University School of Criminal Justice, posts he has held for more than ten years. He was previously Head of the Home Research and Planning Unit where he played a significant part in the …
Rating Prediction via Graph Signal Processing

This paper develops new designs for recommendation systems inspired by recent advances in graph signal processing. Recommendation systems aim to predict unknown ratings by exploiting the information revealed in a subset of user-item observed ratings. Leveraging the notions of graph frequency and graph filters, we demonstrate that classical collaborative filtering methods, such as $k$ -nearest neighbors (NN), can be modeled as a specific band-stop graph filter on networks describing similarities between users or items. We also demonstrate that linear latent factor (LF) models, such as low-rank matrix completion, can be viewed as bandlimited interpolation algorithms that operate in a frequency domain given by the spectrum of a joint user and item network. These new interpretations pave the way to new methods for enhanced rating prediction. For NN-based collaborative filtering, we develop more general band-stop graph filters, and present a novel predictor, called mirror filtering (MiFi), that filters jointly across user and item networks. For LF, we propose a low complexity method by exploiting the eigenvector of correlation matrices constructed from known ratings. The performance of our algorithms is assessed in the MovieLens 100 k dataset, showing that our designs reduce the root mean-squared error (up to a 6.85% for MiFi) compared to one incurred by the benchmark collaborative filtering approach.