nils-herrmann/clean_scirepeval huggingface dataset:
Development of Novel Non-Contact Electrodes for Mobile Electrocardiogram Monitoring System

Real-time monitoring of cardiac health is helpful for patients with cardiovascular disease. Many telemedicine systems based on ubiquitous computing and communication techniques have been proposed for monitoring the user's electrocardiogram (ECG) anywhere and anytime. Usually, wet electrodes are used in these telemedicine systems. However, wet electrodes require conduction gels and skin preparation that can be inconvenient and uncomfortable for users. In order to overcome this issue, a new non-contact electrode circuit was proposed and applied in developing a mobile electrocardiogram monitoring system. The proposed non-contact electrode can measure bio-potentials across thin clothing, allowing it to be embedded in a user's normal clothing to monitor ECG in daily life. We attempted to simplify the design of these non-contact electrodes to reduce power consumption while continuing to provide good signal quality. The electrical specifications and the performance of monitoring arrhythmia in clinical settings were also validated to investigate the reliability of the proposed design. Experimental results show that the proposed non-contact electrode provides good signal quality for measuring ECG across thin clothes.
Automated partitioning design in parallel database systems

In recent years, Massively Parallel Processors (MPPs) have gained ground enabling vast amounts of data processing. In such environments, data is partitioned across multiple compute nodes, which results in dramatic performance improvements during parallel query execution. To evaluate certain relational operators in a query correctly, data sometimes needs to be re-partitioned (i.e., moved) across compute nodes. Since data movement operations are much more expensive than relational operations, it is crucial to design a suitable data partitioning strategy that minimizes the cost of such expensive data transfers. A good partitioning strategy strongly depends on how the parallel system would be used. In this paper we present a partitioning advisor that recommends the best partitioning design for an expected workload. Our tool recommends which tables should be replicated (i.e., copied into every compute node) and which ones should be distributed according to specific column(s) so that the cost of evaluating similar workloads is minimized. In contrast to previous work, our techniques are deeply integrated with the underlying parallel query optimizer, which results in more accurate recommendations in a shorter amount of time. Our experimental evaluation using a real MPP system, Microsoft SQL Server 2008 Parallel Data Warehouse, with both real and synthetic workloads shows the effectiveness of the proposed techniques and the importance of deep integration of the partitioning advisor with the underlying query optimizer.
An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.
Survey on Techniques for Ontology Interoperability in Semantic Web

Ontology is a shared conceptualization of knowledge representation of particular domain. These are used for the enhancement of semantic information explicitly. It is considered as a key element in semantic web development. Creation of global web data sources is impossible because of the dynamic nature of the web. Ontology Interoperability provides the reusability of ontologies. Different domain experts and ontology engineers create different ontologies for the same or similar domain depending on their data modeling requirements. These cause ontology heterogeneity and inconsistency problems. For more better and precise results ontology mapping is the solution. As their use has increased, providing means of resolving semantic differences has also become very important. Papers on ontology interoperability report the results on different frameworks and this makes their comparison almost impossible. Therefore, the main focus of this paper will be on providing some basics of ontology interoperability and briefly introducing its different approaches. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies and its related techniques and tools.
New approach for clustering of big data: DisK-means

The exponential growth in the amount of data gathered from various sources has resulted in the need for more efficient algorithms to quickly analyze large datasets. Clustering techniques, like K-Means are useful in analyzing data in a parallel fashion. K-Means largely depends upon a proper initialization to produce optimal results. K-means++ initialization algorithm provides solution based on providing an initial set of centres to the K-Means algorithm. However, its inherent sequential nature makes it suffer from various limitations when applied to large datasets. For instance, it makes k iterations to find k centres. In this paper, we present an algorithm that attempts to overcome the drawbacks of previous algorithms. Our work provides a method to select a good initial seeding in less time, facilitating fast and accurate cluster analysis over large datasets.
A framework for narrative adaptation in interactive story-based learning environments

A key functionality provided by interactive narrative systems is narrative adaptation: augmenting story experiences in response to users' actions, and tailoring story elements to individual users' preferences and needs. However, the task of interactive narrative adaptation is not yet well defined. There is little consensus about the key features of a general model for narrative adaptation, which results in a limited ability to draw comparisons between alternate solutions. This paper aims to synthesize a diverse range of approaches to narrative adaptation, as well as related experience management techniques from intelligent tutoring systems, in order to devise a framework for more clearly conceptualizing the general task of interactive narrative adaptation. The framework has three components: plot adaptation, discourse adaptation, and user tailoring. The conceptual framework is illustrated with examples from Crystal Island, an interactive narrative-centered learning environment for middle school microbiology. The framework is being used to inform preliminary development efforts on an interactive narrative director agent for the Crystal Island learning environment, and aims to provide a first step toward establishing a general model of interactive narrative.
Towards an automated system for short-answer assessment using ontology mapping

A key concern for any e-assessment tool (computer assisted assessment) is its efficiency in assessing the learner’s knowledge, skill set and ability. Multiple-choice questions are the most common means of assessment used in e-assessment systems, and are also successful. An efficient e-assessment system should use variety of question types including shortanswers, essays etc. and modes of response to assess learner’s performance. In this paper, we consider the task of assessing short-answer questions. Several researches have been performed on the evaluation and assessment of short-answer questions and many products are deployed to assess the same as part of e-learning systems. We propose an automated system for assessing short-answers using ontology mapping. We also compare our approach with some existing systems and give an overall evaluation of experiment results, which shows that our approach using ontology mapping gives an optimized result.
Optimizing coflow completion times with utility max-min fairness

In data parallel frameworks such as MapReduce and Spark, a coflow represents a set of network flows used to transfer intermediate data between successive computation stages for a job. The completion time of a job is then determined by the collective behavior of such a coflow, rather than any individual flow within, and influenced by the amount of network bandwidth allocated to it. Different jobs in a shared cluster have different degrees of sensitivity to their completion times, modeled by their respective utility functions. In this paper, we focus on the design and implementation of a new utility optimal scheduler across competing coflows, in order to provide differential treatment to coflows with different degrees of sensitivity, yet still satisfying max-min fairness across these coflows. Though this objective can be formulated as a lexicographical maximization problem, it is challenging to solve in practice due to its inherent multi-objective and discrete nature. To address this challenge, we first divide the problem into iterative steps of single-objective subproblems; and in each of these steps, we then perform a series of transformations to obtain an equivalent linear programming (LP) problem, which can be efficiently solved in practice. To demonstrate that our solutions are practically feasible, we have implemented it as a real-world coflow scheduler based on the Varys open-source framework to evaluate its effectiveness.
Designing an Interactive Messaging and Reminder Display for Elderly

Despite the wealth of information and communicatio n technology in society today, there appears to be a lack of accept able information services for the growing elderly population in need of care. Acc eptability is not only related to human factors such as button size and legibility , but rather relates to perceived value and harmony in relation to existing l ving patterns. This paper describes the design of an asynchronous interactive ommunication system based upon a bulletin board metaphor. A panel of en d-users was involved in various stages of the design process. To improve ea s of use, functionality exposed to elderly users is limited, while caregive rs are given extended control. A pilot field study with a working prototype showed a high degree of user acceptance. The user centered approach resulted in a design concept that was acceptable for the elderly participants.
Data Science and Ebola

Data Science---Today, everybody and everything produces data. People produce large amounts of data in social networks and in commercial transactions. Medical, corporate, and government databases continue to grow. Sensors continue to get cheaper and are increasingly connected, creating an Internet of Things, and generating even more data. In every discipline, large, diverse, and rich data sets are emerging, from astrophysics, to the life sciences, to the behavioral sciences, to finance and commerce, to the humanities and to the arts. In every discipline people want to organize, analyze, optimize and understand their data to answer questions and to deepen insights. The science that is transforming this ocean of data into a sea of knowledge is called data science. This lecture will discuss how data science has changed the way in which one of the most visible challenges to public health is handled, the 2014 Ebola outbreak in West Africa.
Axial-Ratio-Bandwidth Enhancement of a Microstrip-Line-Fed Circularly Polarized Annular-Ring Slot Antenna

The design of a new microstrip-line-fed wideband circularly polarized (CP) annular-ring slot antenna (ARSA) is proposed. Compared with existing ring slot antennas, the ARSAs designed here possess much larger CP bandwidths. The main features of the proposed design include a wider ring slot, a pair of grounded hat-shaped patches, and a deformed bent feeding microstrip line. The ARSAs designed using FR4 substrates in the L and S bands have 3-dB axial-ratio bandwidths (ARBWs) of as large as 46% and 56%, respectively, whereas the one using an RT5880 substrate in the L band, 65%. In these 3-dB axial-ratio bands, impedance matching with VSWR ≤ 2 is also achieved.
Cooperative Control Strategy of Energy Storage System and Microsources for Stabilizing the Microgrid during Islanded Operation

In this paper, the cooperative control strategy of microsources and the energy storage system (ESS) during islanded operation is presented and evaluated by a simulation and experiment. The ESS handles the frequency and the voltage as a primary control. And then, the secondary control in microgrid management system returns the current power output of the ESS into zero. The test results show that the proposed cooperative control strategy can regulate the frequency and the voltage, and the secondary control action can contribute to improve the control capability.
Low losses printed distribution network technologies for planar antennas in Ka band

Study of Substrate Integrated Waveguide and Gap Waveguide technologies used to manufacture some demonstration prototypes at Ka band for a low losses distribution network in a low profile antenna. A transmission losses comparison is presented using lossy and lossless dielectric substrates in the simulations for the different technologies at 30 GHz. Various structures such as transitions from WR-28, bends, and power dividers have been designed and simulated. The simulations of different models are compared with the manufactured prototypes in metallized 3D-printed plastic technology and in RO4350B and FR-4 substrates for a band from 28 to 30 GHz.
Lifting Wavelet Transform and Singular Values Decomposition for Secure Image Watermarking

In this paper, we present a technique of secure watermarking of grayscale and color images. This technique consists in applying the Singular Value Decomposition (SVD) in LWT (Lifting Wavelet Transform) domain in order to insert the watermark image (grayscale) in the host image (grayscale or color image). It also uses signature in the embedding and extraction steps. The technique is applied on a number of grayscale and color images. The performance of this technique is proved by the PSNR (Pick Signal to Noise Ratio), the MSE (Mean Square Error) and the SSIM (structural similarity) computations. Color image, grayscale image, singular values decomposition, lifting wavelet transform, image watermarking, watermark, secure.
Classification of Substitution Ciphers using Neural Networks

Most of the time of a cryptanalyst is spent on finding the cipher technique used for encryption rather than the finding the key/ plaintext of the received cipher text. The Strength of the classical substitution cipher‟s success lie on the variety of characters employed to represent a single character. More, the characters employed more the complexity. Thus, in order to reduce the work of the cryptanalyst, neural network based identification is done based on the features of the cipher methods. In this paper, classical substitution ciphers, namely, Playfair, Vigenère and Hill ciphers are considered. The features of the cipher methods under consideration were extracted and a backpropagation neural network was trained. The network was tested for random texts with random keys of various lengths. The cipher text size was fixed as 1Kb. The results so obtained were encouraging.
Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring

Low power consumption, low transceiver chip cost and large coverage area are the main characteristics of the low power wide area networks (LPWAN) technologies. We expect that LPWAN can be part of enabling new human-centric health and wellness monitoring applications. Therefore in this work we study the indoor performance of one LPWAN technology, namely LoRa, by the means of real-life measurements. The measurements were conducted using the commercially available equipment in the main campus of the University of Oulu, Finland, which has an indoor area spanning for over 570 meters North to South and over 320 meters East to West. The measurements were executed for a sensor node operating close to human body that was periodically reporting the sensed data to a base station. The obtained results show that when using 14 dBm transmit power and the largest spreading factor of 12 for the 868 MHz ISM band, the whole campus area can be covered. Measured packet success delivery ratio was 96.7 % without acknowledgements and retransmissions.
Subcarrier-index modulation OFDM

A new transmission approach, referred to as subcarrier-index modulation (SIM) is proposed to be integrated with the orthogonal frequency division multiplexing (OFDM) systems. More specifically, it relates to adding an additional dimension to the conventional two-dimensional (2-D) amplitude/phase modulation (APM) techniques, i.e. amplitude shift keying (ASK) and quadrature amplitude modulation (QAM). The key idea of SIM is to employ the subcarrier-index to convey information to the receiver. Furthermore, a closed-form analytical bit error ratio (BER) of SIM OFDM in Rayleigh channel is derived. Analytical and simulation results show error probability performance gain of 4 dB over 4-QAM OFDM systems for both coded and uncoded data without power saving policy. Alternatively, power saving policy retains an average gain of 1 dB while using 3 dB less transmit power per OFDM symbol.
Treatment guidelines of lymphatic malformations of the head and neck.

Lymphatic malformations, traditionally called lymphangiomas, are diseases caused by development errors of the lymphatic system. About 90% of the cases occur within 2years of age, except a few cases which occur in adulthood, and approximately 75% of the lesions are located in the head and neck region. The lesions can grow rapidly with infection, trauma or bleeding, resulting in disfigurement as well as severe impairment of respiration, swallow and speech. Although lymphatic malformations are benign lesions, they rarely resolve spontaneously, their infiltrating nature coupled with the difficulty in distinguishing involved vital structures of head and neck from adjacent normal tissues makes complete surgical resection even more difficult. The likelihood of postsurgical recurrence and complications is thus higher than other vascular lesions. Surgical resection, sclerotherapy and laser therapy are currently the main treatment modes of lymphatic malformations. Various treatment options have their advantages and disadvantages, the selection of treatment modalities should depend on the patient's individual status and available technology and expertise. The treatment protocol should be individualized, comprehensive as well as sequential in order to obtain the best treatment outcome. Based on published literatures and clinical experiences, we devised the treatment guideline for management of head and neck lymphatic malformations. This protocol will be reviewed and updated periodically to include cutting edge knowledge to provide the best treatment options to benefit our patients.
Better k-best Parsing

We discuss the relevance of k-best parsing to recent applications in natural language processing, and develop efficient algorithms for k-best trees in the framework of hypergraph parsing. To demonstrate the efficiency, scalability and accuracy of these algorithms, we present experiments on Bikel’s implementation of Collins’ lexicalized PCFG model, and on Chiang’s CFG-based decoder for hierarchical phrase-based translation. We show in particular how the improved output of our algorithms has the potential to improve results from parse reranking systems and other applications.
Using occupancy grids for mobile robot perception and navigation

An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid is reviewed. The occupancy grid is a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice. To construct a sensor-derived map of the robot's world, the cell state estimates are obtained by interpreting the incoming range readings using probabilistic sensor models. Bayesian estimation procedures allow the incremental updating of the occupancy grid, using readings taken from several sensors over multiple points of view. The use of occupancy grids from mapping and for navigation is examined. Operations on occupancy grids and extensions of the occupancy grid framework are briefly considered.<<ETX>>
Cloud Computing - Research Issues, Challenges, Architecture, Platforms and Applications: A Survey

—Cloud computing is the development of parallel computing, distributed computing, grid computing and virtualization technologies which define the shape of a new era. Cloud computing is an emerging model of business computing. In this paper, we explore the concept of cloud architecture and compares cloud computing with grid computing. We also address the characteristics and applications of several popular cloud computing platforms. In this paper, we aim to pinpoint the challenges and issues of cloud computing. We identified several challenges from the cloud computing adoption perspective and we also highlighted the cloud interoperability issue that deserves substantial further research and development. However, security and privacy issues present a strong barrier for users to adapt into cloud computing systems. In this paper, we investigate several cloud computing system providers about their concerns on security and privacy issues.
Learning decision trees from dynamic data streams

This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental algorithm, that works online, processing each example in constant time, and performing a single scan over the training examples. It uses analytical techniques to choose the splitting criteria, and the information gain to estimate the merit of each possible splitting-test. For multi-class problems the algorithm grows a binary tree for each possible pair of classes, leading to a forest of trees. Decision nodes and leaves contain naive-Bayes classifiers playing different roles during the induction process. Naive-Bayes in leaves are used to classify test examples, naive-Bayes in inner nodes can be used as multivariate splitting-tests if chosen by the splitting criteria, and used to detect drift in the distribution of the examples that traverse the node. When a drift is detected, all the sub-tree rooted at that node will be pruned. The use of naive-Bayes classifiers at leaves to classify test examples, the use of splitting-tests based on the outcome of naive-Bayes, and the use of naive-Bayes classifiers at decision nodes to detect drift are directly obtained from the sufficient statistics required to compute the splitting criteria, without no additional computations. This aspect is a main advantage in the context of high-speed data streams. This methodology was tested with artificial and real-world data sets. The experimental results show a very good performance in comparison to a batch decision tree learner, and high capacity to detect and react to drift.