nils-herrmann/clean_scirepeval huggingface dataset:
Heart Disease Prediction System Using Data Mining

Medical Diagnosis Systems play a vital role in medical practice and are used by medical practitioners for diagnosis and treatment. In this project, a medical diagnosis system is presented for predicting the risk of cardiovascular disease. This system is built by combining the relative advantages of genetic algorithm and neural network. Multilayered Back Propagation neural networks are particularly suited to complex classification problems. The weights of the neural network are determined using genetic algorithm because it finds acceptably good set of weights in less number of iterations. First, the preprocessing of data is done in order to make them suitable for training . GeneticNeural network is used for training the system. The final weights are stored in the weight base and then, it is used for predicting the risk of cardiovascular disease. The classification accuracy obtained using this approach is 90.17%.
Evaluation of Consumer Understanding of Different Front-of-Package Nutrition Labels, 2010–2011

Governments throughout the world are using or considering various front-of-package (FOP) food labeling systems to provide nutrition information to consumers. Our web-based study tested consumer understanding of different FOP labeling systems.



Adult participants (N = 480) were randomized to 1 of 5 groups to evaluate FOP labels: 1) no label; 2) multiple traffic light (MTL); 3) MTL plus daily caloric requirement icon (MTL+caloric intake); 4) traffic light with specific nutrients to limit based on food category (TL+SNL); or 5) the Choices logo. Total percentage correct quiz scores were created reflecting participants' ability to select the healthier of 2 foods and estimate amounts of saturated fat, sugar, and sodium in foods. Participants also rated products on taste, healthfulness, and how likely they were to purchase the product. Quiz scores and product perceptions were compared with 1-way analysis of variance followed by post-hoc Tukey tests.



The MTL+caloric intake group (mean [standard deviation], 73.3% [6.9%]) and Choices group (72.5% [13.2%]) significantly outperformed the no label group (67.8% [10.3%]) and the TL+SNL group (65.8% [7.3%]) in selecting the more healthful product on the healthier product quiz. The MTL and MTL+caloric intake groups achieved average scores of more than 90% on the saturated fat, sugar, and sodium quizzes, which were significantly better than the no label and Choices group average scores, which were between 34% and 47%.



An MTL+caloric intake label and the Choices symbol hold promise as FOP labeling systems and require further testing in different environments and population subgroups.
Immersive VR for Scientific Visualization: A Progress Report

potential to be a powerful tool for the visualization of burgeoning scientific data sets and models. In this article we sketch a research agenda for the hardware and software technology underlying IVR for scientific visualization. In contrast to Brooks’ excellent survey last year, which reported on the state of IVR and provided concrete examples of its production use, this article is somewhat speculative. We don’t present solutions but rather a progress report, a hope, and a call to action, to help scientists cope with a major crisis that threatens to impede their progress. Brooks’ examples show that the technology has only recently started to mature—in his words, it “barely works.” IVR is used for walkthroughs of buildings and other structures, virtual prototyping (vehicles such as cars, tractors, and airplanes), medical applications (surgical visualization, planning, and training), “experiences” applied as clinical therapy (reliving Vietnam experiences to treat post-traumatic stress disorder, treating agoraphobia), and entertainment. Building on Brooks’ work, here we concentrate on why scientific visualization is also a good application area for IVR. First we’ll briefly review scientific visualization as a means of understanding models and data, then discuss the problem of exploding data set size, both from sensors and from simulation runs, and the consequent demand for new approaches. We see IVR as part of the solution: as a richer visualization and interaction environment, it can potentially enhance the scientist’s ability to manipulate the levels of abstraction necessary for multi-terabyte and petabyte data sets and to formulate hypotheses to guide very long simulation runs. In short, IVR has the potential to facilitate a more balanced human-computer partnership that maximizes bandwidth to the brain by more fully engaging the human sensorium. We argue that IVR remains in a primitive state of development and is, in the case of CAVEs and tiled projection displays, very expensive and therefore not in routine use. (We use the term cave to denote both the original CAVE developed at the University of Illinois’ Electronic Visualization Laboratory and CAVE-style derivatives.) Evolving hardware and software technology may, however, enable IVR to become as ubiquitous as 3D graphics workstations—once exotic and very expensive—are today. Finally, we describe a research agenda, first for the technologies that enable IVR and then for the use of IVR for scientific visualization. Punctuating the discussion are sidebars giving examples of scientific IVR work currently under way at Brown University that addresses some of the research challenges, as well as other sidebars on data set size growth and IVR interaction metaphors.
Exploratory analysis of OpenStreetMap for land use classification

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of  classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas' values are remarkable and promising results that encourages us for future research on this topic.
Sonoliards: Rendering Audible Sound Spots by Reflecting the Ultrasound Beams

This paper proposes a dynamic acoustic field generation system for a spot audio towards particular person indoors. Spot audio techniques have been explored by generating the ultrasound beams toward the target person in certain area however everyone in this area can hear the sound. Our system recognizes the positions of each person indoor using motion capture and 3D model data of the room. After that we control direction of parametric speaker in real-time so that sound reach only particular person by calculating the reflection of sound on surfaces such as wall and ceiling. We calculate direction of parametric speaker using a beam tracing method. We present generating methods of dynamic acoustic field in our system and conducted the experiments on human factor to evaluate performance of proposed system.
A Side-Channel Analysis Resistant Description of the AES S-Box

So far, efficient algorithmic countermeasures to secure the AES algorithm against (first-order) differential side-channel attacks have been very expensive to implement. In this article, we introduce a new masking countermeasure which is not only secure against first-order sidechannel attacks, but which also leads to relatively small implementations compared to other masking schemes implemented in dedicated hardware. Our approach is based on shifting the computation of the finite field inversion in the AES S-box down to GF (4). In this field, the inversion is a linear operation and therefore it is easy to mask. Summarizing, the new masking scheme combines the concepts of multiplicative and additive masking in such a way that security against firstorder side-channel attacks is maintained, and that small implementations in dedicated hardware can be achieved.
Comparative text analytics via topic modeling in banking

In this paper, we compare and evaluate multiple topic modeling approaches and their effectiveness in analyzing a large set of SEC filings by US public banks. More specifically, we apply four major topic modeling methods to a corpus of 8-K and 10-K filings, from the years 2005–2016, of 578 bank holding companies. These methods include Principal Component Analysis, Non-negative Matrix Factorization, Latent Dirichlet Allocation and KATE, a novel k-competitive autoencoder for text documents. Separately for 8-K and 10-K, the usefulness and effectiveness of these methods is evaluated by comparing their performances on two classification tasks: (i) predicting which section each document corresponds to, where we consider each section within an 8-K or 10-K filing as an individual document, and (ii) detecting text from a bank's year of failure, a task for which we use bank failure data from the 2008 financial crisis. In addition, we qualitatively compare the topics discovered by the different methods. We conclude that topic modeling can be an effective tool in financial decision making and risk management.
Combining Neural Networks and Log-linear Models to Improve Relation Extraction

The last decade has witnessed the success of traditional feature-based methods for exploiting the discrete structures such as words or lexical patterns to extract relations from text. Recently, convolutional and recurrent neural networks have been shown to capture effective hidden structures within sentences via continuous representations, thereby significantly advancing the performance of relation extraction. The advantage of convolutional neural networks is their capacity to generalize the consecutive k-grams in the sentences while recurrent neural networks are effective to encode long range sentence context. This paper proposes to combine the traditional feature-based method, the convolutional and recurrent neural networks to simultaneously benefit from their advantages. Our systematic evaluation of different network architectures and combination methods demonstrates the effectiveness of this approach and results in the state-ofthe-art performance on the ACE 2005 and SemEval datasets.
FACE-CHANGE: Application-Driven Dynamic Kernel View Switching in a Virtual Machine

Kernel minimization has already been established as a practical approach to reducing the trusted computing base. Existing solutions have largely focused on whole-system profiling - generating a globally minimum kernel image that is being shared by all applications. However, since different applications use only part of the kernel's code base, the minimized kernel still includes an unnecessarily large attack surface. Furthermore, once the static minimized kernel is generated, it is not flexible enough to adapt to an altered execution environment (e.g., new workload). FACE-CHANGE is a virtualization-based system to facilitate dynamic switching at runtime among multiple minimized kernels, each customized for an individual application. Based on precedent profiling results, FACE-CHANGE transparently presents a customized kernel view for each application to confine its reach ability of kernel code. In the event that the application exceeds this boundary, FACE-CHANGE is able to recover the missing code and back trace its attack/exception provenance to analyze the anomalous behavior.
College students' academic motivation, media engagement and fear of missing out

The concerns about the consequences of mental problems related to use of social media among university students have recently raised consciousness about a relatively new phenomenon termed Fear of Missing Out (FoMO). Drawing on the self-determination theory and on the assumption that low levels of basic need satisfaction may relate to FoMO and social media engagement, the aim of the present research was to examine for the first time possible links between FoMO, social media engagement, and three motivational constructs: Intrinsic, extrinsic and amotivation for learning. Data were gathered from 296 undergraduate students by using the following scales: Social Media Engagement (SME), Fear of Missing Out (FoMOs) and Academic Motivation. The SME is a new scale, specifically designed for this study to measure the extent to which students used social media in the classroom. This scale includes three categories: Social engagement, news information engagement and commercial information engagement. Path analysis results indicated that the positive links between social media engagement and two motivational factors: Extrinsic and amotivation for learning are more likely to be mediated by FoMO. Interpretation of these results, their congruence within the context of the theoretical frameworks and practical implications are discussed.
A High-Speed High-Resolution Latch Comparator for Pipeline Analog-to-Digital Converters

A high-speed and high-resolution comparator intended to be implemented in a 12 bit 100 MHz pipeline analog-to-digital converter (ADC) for frequency wireless local area network application is proposed. The designed comparator presents a rail-to-rail input range preamplifier without any capacitance required. This comparator with a novel architecture of output stage achieves very high speed at a low kickback noise. The simulation results using a 0.35 mum TSMC CMOS process technology show that this comparator exhibits a propagation delay of 2.8 ns and has a very high resolution for a rail-to-rail input signal range, while consumes only 1.0 mW of power with 5.0 V voltage supply.
Top-pinned perpendicular MTJ structure with a counter bias magnetic field layer for suppressing a stray-field in highly scalable STT-MRAM

We propose a new top-pinned perpendicular MTJ structure that can both achieve the magnetic stability of a pinned layer and reduce a magnetic stray-field to a free layer. The key point of the structure is that there is a large design margin of a pin configuration for the stray-field reduction due to a counter bias magnetic field layer being used instead of a SAF structure. Stable switching performances were successfully obtained without application of an external magnetic field in our 50nmF or less MTJs.
Container Port Performance Measurement and Comparison Leveraging Ship GPS Traces and Maritime Open Data

Container ports are generally measured and compared using performance indicators such as container throughput and facility productivity. Being able to measure the performance of container ports quantitatively is of great importance for researchers to design models for port operation and container logistics. Instead of relying on the manually collected statistical information from different port authorities and shipping companies, we propose to leverage the pervasive ship GPS traces and maritime open data to derive port performance indicators, including ship traffic, container throughput, berth utilization, and terminal productivity. These performance indicators are found to be directly related to the number of container ships arriving at the terminals and the number of containers handled at each ship. Therefore, we propose a framework that takes the ships' container-handling events at terminals as the basis for port performance measurement. With the inferred port performance indicators, we further compare the strengths and weaknesses of different container ports at the terminal level, port level, and region level, which can potentially benefit terminal productivity improvement, liner schedule optimization, and regional economic development planning. In order to evaluate the proposed framework, we conduct extensive studies on large-scale real-world GPS traces of container ships collected from major container ports worldwide through the year, as well as various maritime open data sources concerning ships and ports. Evaluation results confirm that the proposed framework not only can accurately estimate various port performance indicators but also effectively produces port comparison results such as port performance ranking and port region comparison.
New “Smart Parking” System Based on Resource Allocation and Reservations

We propose a novel “smart parking” system for an urban environment. The system assigns and reserves an optimal parking space based on the driver's cost function that combines proximity to destination and parking cost. Our approach solves a mixed-integer linear programming (MILP) problem at each decision point defined in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and is updated at the next decision point with a guarantee that there is no resource reservation conflict and that no driver is ever assigned a resource with a cost function higher than this driver's current cost function value. Based on simulation results, compared with uncontrolled parking processes or state-of-the-art guidance-based systems, our system reduces the average time to find a parking space and the parking cost, whereas the overall parking capacity is more efficiently utilized. We also describe full implementation in a garage to test this system, where a new light system scheme is proposed to guarantee user reservations.
A new approach for increasing the performance of the industrial Ethernet system PROFINET

In todaypsilas realtime Ethernet systems two approaches can be differentiated regarding the organisation of the frame transmission: On the one hand there is a summation frame approach in which one frame can supply several nodes with data at the same time. On the other hand, there is the approach of providing data with individual frames for each node. The two most relevant factors influencing the performance in terms of the minimum achievable update time are the propagation delay and the frame transfer time. In this paper we introduce a new concept for reducing the minimum possible update time for individual frame based systems like PROFINET IRT. The performance evaluation compares the results of our concept with the summation frame based EtherCAT System.
Music emotion recognition: the role of individuality

It has been realized in the music emotion recognition (MER) community that personal difference, or individuality, has significant impact on the success of an MER system in practice. However, no previous work has explicitly taken individuality into consideration in an MER system. In this paper, the group-wise MER approach (GWMER) and personalized MER approach (PMER) are proposed to study the role of individuality. GWMER evaluates the importance of each individual factor such as sex, personality, and music experience, whereas PMER evaluates whether the prediction accuracy for a user is significantly improved if the MER system is personalized for the user. Experimental results demonstrate the effect of personalization and suggest the need for a better representation of individuality and for better prediction accuracy.
An efficient MapReduce algorithm for counting triangles in a very large graph

Triangle counting problem is one of the fundamental problem in various domains. The problem can be utilized for computation of clustering coefficient, transitivity, trianglular connectivity, trusses, etc. The problem have been extensively studied in internal memory but the algorithms are not scalable for enormous graphs. In recent years, the MapReduce has emerged as a de facto standard framework for processing large data through parallel computing. A MapReduce algorithm was proposed for the problem based on graph partitioning. However, the algorithm redundantly generates a large number of intermediate data that cause network overload and prolong the processing time. In this paper, we propose a new algorithm based on graph partitioning with a novel idea of triangle classification to count the number of triangles in a graph. The algorithm substantially reduces the duplication by classifying triangles into three types and processing each triangle differently according to its type. In the experiments, we compare the proposed algorithm with recent existing algorithms using both synthetic datasets and real-world datasets that are composed of millions of nodes and billions of edges. The proposed algorithm outperforms other algorithms in most cases. Especially, for a twitter dataset, the proposed algorithm is more than twice as fast as existing MapReduce algorithms. Moreover, the performance gap increases as the graph becomes larger and denser.
Dense 3D surface acquisition by structured light using off-the-shelf components

This paper describes the implementation of a LCD stripe projection system, based on a new processing scheme called line shift processing. One advantage of the method is, that the projection device can, but is not required to be stable over time nor does it have to be calibrated. The new method therefore allows us to use an off-the-shelf multimedia projector to acquire dense 3-D data (several 100K Triangles) of large objects in a matter of seconds. Since we are able to control our source of illumination, we can also acquire registered color information using standard monochrome cameras. Besides the description of the system some practical examples are given. The calibration of the system is also addressed.
A tale of two cannabinoids: the therapeutic rationale for combining tetrahydrocannabinol and cannabidiol.

This study examines the current knowledge of physiological and clinical effects of tetrahydrocannabinol (THC) and cannabidiol (CBD) and presents a rationale for their combination in pharmaceutical preparations. Cannabinoid and vanilloid receptor effects as well as non-receptor mechanisms are explored, such as the capability of THC and CBD to act as anti-inflammatory substances independent of cyclo-oxygenase (COX) inhibition. CBD is demonstrated to antagonise some undesirable effects of THC including intoxication, sedation and tachycardia, while contributing analgesic, anti-emetic, and anti-carcinogenic properties in its own right. In modern clinical trials, this has permitted the administration of higher doses of THC, providing evidence for clinical efficacy and safety for cannabis based extracts in treatment of spasticity, central pain and lower urinary tract symptoms in multiple sclerosis, as well as sleep disturbances, peripheral neuropathic pain, brachial plexus avulsion symptoms, rheumatoid arthritis and intractable cancer pain. Prospects for future application of whole cannabis extracts in neuroprotection, drug dependency, and neoplastic disorders are further examined. The hypothesis that the combination of THC and CBD increases clinical efficacy while reducing adverse events is supported.