nils-herrmann/clean_scirepeval huggingface dataset:
Everyday functioning of people with Parkinson's disease and impairments in executive function: a qualitative investigation.

Executive function is the key area of cognitive impairment in Parkinson's disease. This study investigated how cognitive difficulties impact on everyday life of people with Parkinson's disease and their carers, and whether they explicitly mention executive-type difficulties.



Semistructured interviews with 11 people with Parkinson's disease and six carers were analyzed thematically. People with Parkinson's disease performed within the normal range on cognitive screening tests, but all had abnormal scores on tests of executive function.



Despite relatively mild executive deficits and no global cognitive impairment, participants described executive-type difficulties as well as a range of problems in other cognitive domains, such as memory, processing speed and apathy. Cognitive difficulties had a far-reaching impact on everyday life and their significance depended on personal circumstances, such as the level of responsibilities of the person with Parkinson's disease and the extent of available support.



By presenting subjective accounts of living with Parkinson's disease and cognitive difficulties, this study improves our understanding of how the observed level of cognitive impairment translates into everyday functioning. The study results have implications for recognizing cognitive difficulties and for planning support for people with Parkinson's disease and their families, and can help identify ways of promoting effective self-management. Implications for rehabilitation Treatment of Parkinson's disease tends to focus on the movement disorder, meaning that cognitive difficulties and their impact can be overlooked. Participants in this study had only relatively mild executive deficits but described a range of cognitive problems, including executive-type difficulties. Cognitive difficulties have an emotional impact and can cause a range of challenges in everyday life, adding to the burden of physical symptoms of Parkinson's disease. Results of this study provide insights into how executive functions impairments translate into everyday difficulties in Parkinson's disease and have implications for planning support for people with Parkinson's disease and their families.
Intrusion Detection System using Support Vector Machine and Decision Tree

Support Vector Machines (SVM) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems. This method can decrease the training and testing time, increasing the efficiency of the system. The different ways to construct the binary trees divides the data set into two subsets from root to the leaf until every subset consists of only one class. The construction order of binary tree has great influence on the classification performance. In this paper we are studying an algorithm, Tree structured multiclass SVM, which has been used for classifying data. This paper proposes the decision tree based algorithm to construct multiclass intrusion detection system.
Cloud-Based NoSQL Data Migration

Cloud computing has enabled the Database-as-a-Service (DBaaS) model to manage large volumes of user-generated data using NoSQL data repositories. There are several NoSQL implementations such as document, columnar, and key-value which ensure high availability, fault tolerance and scalability to serve distinct client requirements. Nonetheless, different NoSQL data models may also introduce unnecessary heterogeneity in DBaaS, which further restricts the user to migrate the application services according to business or technology changes. In this paper, we propose a NoSQL data migration framework to foster data portability across cloud-based heterogeneous NoSQL data repositories. The proposed approach involves data standardisation and classification stages to render an efficient mapping, and translation between cloud-based different NoSQL data stores. The current implementation of the framework supports three different data models: document, columnar and graph. Moreover, the framework is meta-model driven, and therefore allows developers to extend the support for new database models. Our approach includes an online compression algorithm for data migration (document to graph) whereby a graph database requires up to 46% less space. There is also a significant reduction (37% to 55%) in the number of nodes in the compressed graph database.
Toward Automatic Program Synthesis

An elementary outline of the theorem-proving approach to automatic program synthesis is given, without dwelling on technical details. The method is illustrated by the automatic construction of both recursive and iterative programs operating on natural numbers, lists, and trees.
In order to construct a program satisfying certain specifications, a theorem induced by those specifications is proved, and the desired program is extracted from the proof. The same technique is applied to transform recursively defined functions into iterative programs, frequently with a major gain in efficiency.
It is emphasized that in order to construct a program with loops or with recursion, the principle of mathematical induction must be applied. The relation between the version of the induction rule used and the form of the program constructed is explored in some detail.
10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems

With the advent of multi-channel EEG hardware systems and the concurrent development of topographic and tomographic signal source localization methods, the international 10/20 system, a standard system for electrode positioning with 21 electrodes, was extended to higher density electrode settings such as 10/10 and 10/5 systems, allowing more than 300 electrode positions. However, their effectiveness as relative head-surface-based positioning systems has not been examined. We previously developed a virtual 10/20 measurement algorithm that can analyze any structural MR head and brain image. Extending this method to the virtual 10/10 and 10/5 measurement algorithms, we analyzed the MR images of 17 healthy subjects. The acquired scalp positions of the 10/10 and 10/5 systems were normalized to the Montreal Neurological Institute (MNI) stereotactic coordinates and their spatial variability was assessed. We described and examined the effects of spatial variability due to the selection of positioning systems and landmark placement strategies. As long as a detailed rule for a particular system was provided, it yielded precise landmark positions on the scalp. Moreover, we evaluated the effective spatial resolution of 329 scalp landmark positions of the 10/5 system for multi-subject studies. As long as a detailed rule for landmark setting was provided, 241 scalp positions could be set effectively when there was no overlapping of two neighboring positions. Importantly, 10/10 positions could be well separated on a scalp without overlapping. This study presents a referential framework for establishing the effective spatial resolutions of 10/20, 10/10, and 10/5 systems as relative head-surface-based positioning systems.
Mobile telemonitoring for arrhythmias in outpatients in the Republic of Georgia: a brief report of a pilot study.

As the very first trial of mobile telemedicine in the Republic of Georgia, in June-December 2010 we investigated 35 outpatients with different types of arrhythmia (male/female ratio=16/19; 12-80 years old), among them 5 patients with concomitant epilepsy. The control group comprised 7 clinically healthy sportsmen (soccer players, all men; 15-17 years old), during a 30-min velo ergometer stress test. A three-lead electrocardiogram (ECG) loop recorder (Vitaphone BT 3300; Vitasystems GmbH, Mannheim, Germany) was used in automatic mode, using special LRMA software (MDT, Lázně Bohdaneč, Czech Republic) and a Nokia (Espoo, Finland) model 6730 Symbian phone. Automatically recorded arrhythmia events were transmitted from the loop recorder by Bluetooth(®) (Bluetooth SIG, Inc., Kirkland, WA) to a phone and then by 3G (through our partner mobile operator, MagtiCom Ltd.  to the Vitasystems server in Germany and were available to Georgian physicians via e-mail/Internet. Arrhythmias were recorded/monitored during 7-68 h of observation. The number of automatically recorded ECG events varied between 3 and 170 per observation, or 0.4-10.7 hourly. Cases of sinus brady- and tachyarrhythmia, sinus node weakness syndrome, atrial fibrillation, supraventricular tachycardia, supraventricular premature complexes, and ventricular premature complexes were correctly recognized by automatic recognition software and recorded. In 3 patients and 1 sportsman previously unspecified (despite multiple investigations), arrhythmias were recorded: paroxysmal tachycardia (n=1), sinus node weakness syndrome (n=1), and ventricular premature complexes (n=2). In 3 cases (all women) light insomnia and nervousness were reported. In 2 patients with neurosis (both elderly men, 1 with epilepsy) we had to stop investigation prematurely because of anxiety/agitation. Mobile telecardiology represents feasible methodology to monitor arrhythmias in outpatients in Georgia, promoting earlier discharge of non-life-threatening cases, improving patients' comfort of life, and increasing their mobility with enhanced safety. Mobile telehealth might also represent significant cost-saving for insurance companies (this is an ongoing study). Finally, in remote areas mobile telemonitoring of patients will improve quality of care by timely provision of a second opinion in cases when local expertise is not sufficient.
The feasibility of launching and detecting jamming attacks in wireless networks

Wireless networks are built upon a shared medium that makes it easy for adversaries to launch jamming-style attacks. These attacks can be easily accomplished by an adversary emitting radio frequency signals that do not follow an underlying MAC protocol. Jamming attacks can severely interfere with the normal operation of wireless networks and, consequently, mechanisms are needed that can cope with jamming attacks. In this paper, we examine radio interference attacks from both sides of the issue: first, we study the problem of conducting radio interference attacks on wireless networks, and second we examine the critical issue of diagnosing the presence of jamming attacks. Specifically, we propose four different jamming attack models that can be used by an adversary to disable the operation of a wireless network, and evaluate their effectiveness in terms of how each method affects the ability of a wireless node to send and receive packets. We then discuss different measurements that serve as the basis for detecting a jamming attack, and explore scenarios where each measurement by itself is not enough to reliably classify the presence of a jamming attack. In particular, we observe that signal strength and carrier sensing time are unable to conclusively detect the presence of a jammer. Further, we observe that although by using packet delivery ratio we may differentiate between congested and jammed scenarios, we are nonetheless unable to conclude whether poor link utility is due to jamming or the mobility of nodes. The fact that no single measurement is sufficient for reliably classifying the presence of a jammer is an important observation, and necessitates the development of enhanced detection schemes that can remove ambiguity when detecting a jammer. To address this need, we propose two enhanced detection protocols that employ consistency checking. The first scheme employs signal strength measurements as a reactive consistency check for poor packet delivery ratios, while the second scheme employs location information to serve as the consistency check. Throughout our discussions, we examine the feasibility and effectiveness of jamming attacks and detection schemes using the MICA2 Mote platform.
Learning Semantic Deformation Flows with 3D Convolutional Networks

Shape deformation requires expert user manipulation even when the object under consideration is in a high fidelity format such as a 3D mesh. It becomes even more complicated if the data is represented as a point set or a depth scan with significant self occlusions. We introduce an end-to-end solution to this tedious process using a volumetric Convolutional Neural Network (CNN) that learns deformation flows in 3D. Our network architectures take the voxelized representation of the shape and a semantic deformation intention (e.g., make more sporty) as input and generate a deformation flow at the output. We show that such deformation flows can be trivially applied to the input shape, resulting in a novel deformed version of the input without losing detail information. Our experiments show that the CNN approach achieves comparable results with state of the art methods when applied to CAD models. When applied to single frame depth scans, and partial/noisy CAD models we achieve ∼60% less error compared to the state-of-the-art.
Dynamic simulation for analysis of hybrid electric vehicle system and subsystem interactions, including power electronics

Simulation tools for hybrid electric vehicles (HEVs) can be classified into steady-state and dynamic models, according to their purpose. Tools with steady-state models are useful for system-level analysis. The information gained is helpful for assessing long-term behavior of the vehicle. Tools that utilize dynamic models give in-depth information about the short-term behavior of sublevel components. In this paper, a dynamic model of a hybrid electric vehicle that includes fuel cells, batteries, ultracapacitors, and induction machine drives is presented. Simulation results of vehicle configurations with a battery, a fuel cell-battery combination and a fuel cell-ultracapacitor combination are discussed. The focus of the model is a detailed assessment of different subsystem components, particularly component losses.
Literature Review of Teamwork Models

Both human collaboration and software agent collaboration have been thoroughly studied, but there is relatively little research on hybrid human-agent teamwork. Some research has identified the roles that agents could play in hybrid teams: supporting individual team members, being a teammate, or supporting the team as a whole . Some other work [57] has investigated trust concepts as the fundamental building block for effective human-agent teamwork or posited the types of shared knowledge that promote mutual understanding between cooperating humans and agents [9, 68]. However, many of the facets of human agent teamwork models, such as communication protocols for forming mutual intelligibility, performing team monitoring to assess progress, forming joint goals, addressing task interdependencies in hybrid teamwork are still unexplored. In this report, we address the following questions: 1. what factors affect human team task performance and cognition? 2. how can agent coordination mechanisms be adapted for human-agent teams? 3. with current technologies, what roles can agents successfully fill in hybrid human-agent teams? 4. what are the barriers to human-agent interaction?
Laser speckle photography for surface tampering detection

It is often desirable to detect whether a surface has been touched, even when the changes made to that surface are too subtle to see in a pair of before and after images. To address this challenge, we introduce a new imaging technique that combines computational photography and laser speckle imaging. Without requiring controlled laboratory conditions, our method is able to detect surface changes that would be indistinguishable in regular photographs. It is also mobile and does not need to be present at the time of contact with the surface, making it well suited for applications where the surface of interest cannot be constantly monitored. Our approach takes advantage of the fact that tiny surface deformations cause phase changes in reflected coherent light which alter the speckle pattern visible under laser illumination. We take before and after images of the surface under laser light and can detect subtle contact by correlating the speckle patterns in these images. A key challenge we address is that speckle imaging is very sensitive to the location of the camera, so removing and reintroducing the camera requires high-accuracy viewpoint alignment. To this end, we use a combination of computational rephotography and correlation analysis of the speckle pattern as a function of camera translation. Our technique provides a reliable way of detecting subtle surface contact at a level that was previously only possible under laboratory conditions. With our system, the detection of these subtle surface changes can now be brought into the wild.
Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

Deep Convolutional Neural Networks have revolutionized Computer Go. Large networks have emerged as state-of-the-art models for move prediction and are used not only as stand-alone players but also inside Monte Carlo Tree Search to select and bias moves. Using neural networks inside the tree search is a challenge due to their slow execution time even if accelerated on a GPU. In this paper we evaluate several strategies to limit the number of nodes in the search tree in which neural networks are used. All strategies are assessed using the freely available cuDNN library. We compare our strategies against an optimal upper bound which can be estimated by removing timing constraints. We show that the best strategies are only 50 ELO points worse than this upper bound.
Social Persuasion in Online and Physical Networks

Social persuasion to influence the actions, beliefs, and behaviors of individuals, embedded in a social network, has been widely studied. It has been applied to marketing, healthcare, sustainability, political campaigns, and public policy. Traditionally, there has been a separation between physical (offline) and cyber (online) worlds. While persuasion methods in the physical world focused on strong interpersonal trust and design principles, persuasion methods in the online world were rich on data-driven analysis and algorithms. Recent trends including Internet of Things, “big data,” and smartphone adoption point to the blurring divide between the cyber world and the physical world in the following ways. Fine grained data about each individual's location, situation, social ties, and actions are collected and merged from different sources. The messages for persuasion can be transmitted through both worlds at suitable times and places. The impact of persuasion on each individual is measurable. Hence, we posit that the social persuasion will soon be able to span seamlessly across these worlds and will be able to employ computationally and empirically rigorous methods to understand and intervene in both cyber and physical worlds. Several early examples indicate that this will impact the fundamental facets of persuasion including who, how, where, and when, and pave way for multiple opportunities as well as research challenges.
Hatha yoga: improved vital capacity of college students.

The vital capacity of the lungs is a critical component of good health. Vital capacity is an important concern for those with asthma, heart conditions, and lung ailments; those who smoke; and those who have no known lung problems.



To determine the effects of yoga postures and breathing exercises on vital capacity.



Using the Spiropet spirometer, researchers measured vital capacity. Vital capacity determinants were taken near the beginning and end of two 17-week semesters. No control group was used.



Midwestern university yoga classes taken for college credit.



A total of 287 college students, 89 men and 198 women.



Subjects were taught yoga poses, breathing techniques, and relaxation in two 50-minute class meetings for 15 weeks.



Vital capacity over time for smokers, asthmatics, and those with no known lung disease.



The study showed a statistically significant (P < .001) improvement in vital capacity across all categories over time.



It is not known whether these findings were the result of yoga poses, breathing techniques, relaxation, or other aspects of exercise in the subjects' life. The subjects' adherence to attending class was 99.96%. The large number of 287 subjects is considered to be a valid number for a study of this type. These findings are consistent with other research studies reporting the positive effect of yoga on the vital capacity of the lungs.
SMARTMUSEUM: A mobile recommender system for the Web of Data

Semantic and context knowledge have been envisioned as an appropriate solution for addressing the content heterogeneity and information overload in mobile Web information access, but few have explored their full potential in mobile scenarios, where information objects refer to their physical counterparts, and retrieval is context-aware and personalized for users. We present SMARTMUSEUM, a mobile ubiquitous recommender system for the Web of Data, and its application to information needs of tourists in context-aware on-site access to cultural heritage. The SMARTMUSEUM system utilizes Semantic Web languages as the form of data representation. Ontologies are used to bridge the semantic gap between heterogeneous content descriptions, sensor inputs, and user profiles. The system makes use of an information retrieval framework wherein context data and search result clustering are used in recommendation of suitable content for mobile users. Results from laboratory experiments demonstrate that ontology-based reasoning, query expansion, search result clustering, and context knowledge lead to significant improvement in recommendation performance. The results from field trials show that the usability of the system meets users’ expectations in real-world use. The results indicate that semantic content representation and retrieval can significantly improve the performance of mobile recommender systems in knowledge-rich domains.
Principal Filter Analysis for Guided Network Compression

Principal Filter Analysis (PFA), is an elegant, easy to implement, yet effective methodology for neural network compression. PFA exploits the intrinsic correlation between filter responses within network layers to recommend a smaller network footprint. We propose two compression algorithms: the first allows a user to specify the proportion of the original spectral energy that should be preserved in each layer after compression, while the second is a parameter-free approach that automatically selects the compression used at each layer. Both algorithms are evaluated against several architectures and datasets, and we show considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10 and CIFAR-100 PFA achieves a compression rate of 8x and 3x with an accuracy gain of 0.4% points and 1.4% points, respectively. In our tests we also demonstrate that networks compressed with PFA achieve an accuracy that is very close to the empirical upper bound for a given compression ratio.