nils-herrmann/clean_scirepeval huggingface dataset:
Mixed Methods Research : A Research Paradigm Whose Time Has Come

14 The purposes of this article are to position mixed methods research (mixed research is a synonym) as the natural complement to traditional qualitative and quantitative research, to present pragmatism as offering an attractive philosophical partner for mixed methods research, and to provide a framework for designing and conducting mixed methods research. In doing this, we briefly review the paradigm “wars” and incompatibility thesis, we show some commonalities between quantitative and qualitative research, we explain the tenets of pragmatism, we explain the fundamental principle of mixed research and how to apply it, we provide specific sets of designs for the two major types of mixed methods research (mixed-model designs and mixed-method designs), and, finally, we explain mixed methods research as following (recursively) an eight-step process. A key feature of mixed methods research is its methodological pluralism or eclecticism, which frequently results in superior research (compared to monomethod research). Mixed methods research will be successful as more investigators study and help advance its concepts and as they regularly practice it.
Intelligent Heart Disease Prediction System Using Probabilistic Neural Network

The diagnosis of diseases is a crucial and difficult job in medicine. The recognition of heart disease from diverse features or signs is a multi-layered problem that is not free from false assumptions and is frequently accompanied by hasty effects . Thus an attempt to exploit knowledge and experience of several specialists and clinical screening data of patients composed in databases to assist the diagnosis procedure is regarded as a great challenge. The healthcare industry gathers enormous amounts of heart disease data that unfortunately, are not mined to determine concealed information for effective diagnosing . In this paper, an efficient approach for the intelligent heart disease prediction based on Probabilistic Neural Network (PNN) technique is proposed. Initially, the data set containing 13 medical attributes were obtained from the Cleveland heart disease database. The dataset is clustered with the aid of k-means clustering algorithm. The PNN with radial basis function is trained using the selected data sets. The comparison between existing approaches and proposed approach with respect to accuracy of prediction, sensitivity and specificity is recorded in this paper. The results from experiments returned with diminishing fact that there is considerable improvement in classification and prediction. The proposed PNN works as promising tool for prediction of heart disease.
Parameter identification and digital control of speed of a permanent magnet DC motors

The digital control of speed of a permanent magnet DC motor based on a digital computer is analyzed in this paper. Datasheet information about the DC motor was initially unknown. Therefore, the parameter identification and parameter estimation of the motor was done. A type of PID controller, which parameters were optimized (PI), was used in process of controlling the speed of the motor. DAQ acquisition card was used for the purposes of motor's parameter identification and realization of computer control of motor speed in closed feedback loop. Manipulations with the same DAQ card were done using Matlab/Simulink software package. Experimental results were compared with the results obtained by simulation.
On monitoring development indicators using high resolution satellite images

We develop a machine learning based tool for accurate prediction of socio-economic indicators from daytime satellite imagery. The diverse set of indicators are often not intuitively related to observable features in satellite images, and are not even always well correlated with each other. Our predictive tool is more accurate than using night light as a proxy, and can be used to predict missing data, smooth out noise in surveys, monitor development progress of a region, and flag potential anomalies. Finally, we use predicted variables to do robustness analysis of a regression study of high rate of stunting in India.
Internal Uniplanar Antenna for LTE/WWAN/GPS/GLONASS Applications in Tablet/Laptop Computers

This letter presents an internal uniplanar small size multiband antenna for tablet/laptop computer applications. The proposed antenna in addition to common LTE/WWAN channels covers commercial GPS/GLONASS frequency bands. The antenna is comprised of three sections: coupled-fed, shorting and low frequency spiral strips with the size of 50 ×11 ×0.8 mm2. With the aid of spiral strip, lower band operation at 900 MHz is achieved. Two operating frequency bands cover 870-965 and 1556-2480 MHz. In order to validate simulation results, a prototype of the proposed printed antenna is fabricated and tested. Good agreement between the simulation and measurement results is obtained.
A new image thresholding method based on Gaussian mixture model

In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then,to fit the Gaussian mixtures to the histogram of image, the Expectation Maximization (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms and their corresponding parameterization. Finally, the optimal threshold which is the average of these Gaussian mixture means is chosen. And the experimental results show that the new algorithm performs better. In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then,to fit the Gaussian mixtures to the histogram of image, the Expectation Maximization (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms and their corresponding parameterization. Finally, the optimal threshold which is the average of these Gaussian mixture means is chosen. And the experimental results show that the new algorithm performs better.
Holographic Embeddings of Knowledge Graphs

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HOLE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator, H OLE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. Experimentally, we show that holographic embeddings are able to outperform state-ofthe-art methods for link prediction on knowledge graphs and relational learning benchmark datasets.
Windows NT pagefile.sys Virtual Memory Analysis

As hard disk encryption, RAM disks, persistent data avoidance technology and memory resident malware become morewidespread, memory analysis becomes more important. In order to provide more virtual memory than is actually physicalpresent on a system, an operating system may transfer frames of memory to a pagefile on persistent storage. Current memoryanalysis software does not incorporate such pagefiles and thus misses important information. We therefore present a detailedanalysis of Windows NT paging. We use dynamic gray-box analysis, in which we place known data into virtual memory andexamine where it is mapped to, in either the physical memory or the pagefile, and cross-reference these findings with theWindows NT Research Kernel source code. We demonstrate how to decode the non-present page table entries, and accuratelyreconstruct the complete virtual memory space, including non-present memory pages on Windows NT systems using 32-bit,PAE or IA32e paging. Our analysis approach can be used to analyze other operating systems as well.
Relationships between handwriting performance and organizational abilities among children with and without dysgraphia: a preliminary study.

Organizational ability constitutes one executive function (EF) component essential for common everyday performance. The study aim was to explore the relationship between handwriting performance and organizational ability in school-aged children. Participants were 58 males, aged 7-8 years, 30 with dysgraphia and 28 with proficient handwriting. Group allocation was based on children's scores in the Handwriting Proficiency Screening Questionnaire (HPSQ). They performed the Hebrew Handwriting Evaluation (HHE), and their parents completed the Questionnaire for Assessing Students' Organizational Abilities-for Parents (QASOA-P). Significant differences were found between the groups for handwriting performance (HHE) and organizational abilities (QASOA-P). Significant correlations were found in the dysgraphic group between handwriting spatial arrangement and the QASOA-P mean score. Linear regression indicated that the QASOA-P mean score explained 42% of variance of handwriting proficiency (HPSQ). Based on one discriminant function, 81% of all participants were correctly classified into groups. Study results strongly recommend assessing organizational difficulties in children referred for therapy due to handwriting deficiency.
Gratitude and well-being: a review and theoretical integration.

This paper presents a new model of gratitude incorporating not only the gratitude that arises following help from others but also a habitual focusing on and appreciating the positive aspects of life", incorporating not only the gratitude that arises following help from others, but also a habitual focusing on and appreciating the positive aspects of life. Research into individual differences in gratitude and well-being is reviewed, including gratitude and psychopathology, personality, relationships, health, subjective and eudemonic well-being, and humanistically orientated functioning. Gratitude is strongly related to well-being, however defined, and this link may be unique and causal. Interventions to clinically increase gratitude are critically reviewed, and concluded to be promising, although the positive psychology literature may have neglected current limitations, and a distinct research strategy is suggested. Finally, mechanisms whereby gratitude may relate to well-being are discussed, including schematic biases, coping, positive affect, and broaden-and-build principles. Gratitude is relevant to clinical psychology due to (a) strong explanatory power in understanding well-being, and (b) the potential of improving well-being through fostering gratitude with simple exercises.
A survey on IoT communication and computation frameworks: An industrial perspective

This paper surveys fog computing and embedded systems platforms as the building blocks of Internet of Things (IoT). Many concepts around IoT architectures, with various examples, are also discussed. This paper reviews a high-level conceptual layered architecture for IoT from a computational perspective. The architecture incorporates fog computing to address several issues associated with cloud computing; however, it is never a binary decision between fog and cloud. Many of the world's physical objects are being embedded with sensors and actuators, tied by communication infrastructures, and managed by computational algorithms. IoT sensor networks and embedded systems connecting smart objects are revolutionizing how we approach our daily lives, health care, energy, and transportation. Such computational needs are addressed with an array of various models and frameworks. In an attempt to consolidate the use of these models, this paper reviews the state-of-the-art research in IoT, cloud computing, and fog computing.
Smart street light system looking like usual street lights based on sensor networks

Currently, in the whole world, enormous electric energy is consumed by the street lights, which are automatically turn on when it becomes dark and automatically turn off when it becomes bright. This is the huge waste of energy in the whole world and should be changed. This paper discusses a smart street light system, whose concept is proposed by Fujii et al. The main aim of smart street light systems is that lights turn on when needed and light turn off when not needed. Moreover, the smart street light system in this paper behaves like usual street lights that turn on all night. The ideal behavior of the smart street light system is that no one finds turn-off of street lights at night. Whenever someone see street lights, they turn on and whenever no one see street lights, they turn off. The smart street light system consists of LED lights, brightness sensors, motion sensors and short-distance communication networks. The lights turn on before pedestrians and vehicles come and turn off or reduce brightness when there is no one. It will be difficult for pedestrians and drivers of vehicles to distinguish our smart street lights and the conventional street lights because our street lights all turn on before they come.
Underground Mine Communications: A Survey

After a recent series of unfortunate underground mining disasters, the vital importance of communications for underground mining is underlined one more time. Establishing reliable communication is a very difficult task for underground mining due to the extreme environmental conditions. Until now, no single communication system exists which can solve all of the problems and difficulties encountered in underground mine communications. However, combining research with previous experiences might help existing systems improve, if not completely solve all of the problems. In this survey, underground mine communication is investigated. Major issues which underground mine communication systems must take into account are discussed. Communication types, methods, and their significance are presented.
Noncontact Wearable Wireless ECG Systems for Long-Term Monitoring

Electrocardiography (ECG) is the most common and extensively used vital sign monitoring method in modern healthcare systems. Different designs of ambulatory ECG systems were developed as alternatives to the commonly used 12-lead clinical ECG systems. These designs primarily focus on portability and user convenience, while maintaining signal integrity and lowering power consumption. Here, a wireless ECG monitoring system is developed using flexible and dry capacitive electrodes for long-term monitoring of cardiovascular health. Our capacitive-coupled dry electrodes can measure ECG signals over a textile-based interface material between the skin and electrodes. The electrodes are connected to a data acquisition system that receives the raw ECG signals from the electrodes and transmits the data using Bluetooth to a computer. A software application was developed to process, store, and display the ECG signal in real time. ECG measurements were obtained over different types of textile materials and in the presence of body movements. Our experimental results show that the performance of our ECG system is comparable to other reported ECG monitoring systems. In addition, to put this research into perspective, recent ambulatory ECG monitoring systems, ECG systems-on-chip, commercial ECG monitoring systems, and different state-of-the-art ECG systems are reviewed, compared, and critically discussed.
Centroidal Voronoi tessellation in universal covering space of manifold surfaces

The centroidal Voronoi tessellation (CVT) has found versatile applications in geometric modeling, computer graphics, and visualization, etc. In this paper, we first extend the concept of CVT from Euclidean space to spherical space and hyperbolic space, and then combine all of them into a unified framework – the CVT in universal covering space. The novel spherical and hyperbolic CVT energy functions are defined, and the relationship between minimizing the energy and the CVT is proved. We also show by our experimental results that both spherical and hyperbolic CVTs have the similar property as their Euclidean counterpart where the sites are uniformly distributed with respect to given density values. As an example of the application, we utilize the CVT in universal covering space to compute uniform partitions and high-quality remeshing results for genus-0, genus-1, and high-genus (genus>1) surfaces.
Convenient or Useful ? Consumer Adoption of Smartphones for Mobile Commerce

Merchants have developed apps for the smartphone that assist consumers through the buying process, from when they gather information to when they decide to purchase. Sharing information, such as location, shopping preferences and financial data, can enhance consumers’ experience. However, they may be reluctant to disclose these details unless they perceive that the benefits gained are more than the risk of privacy loss. This privacy calculus is added to the unified theory of acceptance and use of technology (UTAUT2) in order to explain consumers’ willingness to exchange the disclosure of personal information for additional value. Sharing information makes mobile commerce more convenient by saving time and effort. Companies are able to send offers that are tailored to a specific customer. Payments are processed faster because the merchant already has the financial data on hand. UTAUT2 is further extended with the Theory of Convenience. Results from a survey of over 300 consumers show that perceived value and perceived convenience are influencing variables and that perceived value mediates the influence of perceived convenience on intention to use.
A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

Predicting stock market price is considered as a challenging task of financial time series analysis, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in this area to predict the stock market price, including regression algorithms which can be useful tools to provide good performance of financial time series prediction. Support Vector Regression is one of the most powerful algorithms in machine learning. There have been countless successes in utilizing SVR algorithm for stock market prediction. In this paper, we propose a novel hybrid approach based on machine learning and filtering techniques. Our proposed approach combines Support Vector Regression and Hodrick–Prescott filter in order to optimize the prediction of stock price. To assess the performance of this proposed approach, we have conducted several experiments using real world datasets. The principle objective of this paper is to demonstrate the improvement in predictive performance of stock market and verify the works of our proposed model in comparison with other optimized models. The experimental results confirm that the proposed algorithm constitutes a powerful model for predicting stock market prices.
Reverse Engineering Digital Circuits Using Structural and Functional Analyses

Integrated circuits (ICs) are now designed and fabricated in a globalized multivendor environment making them vulnerable to malicious design changes, the insertion of hardware Trojans/malware, and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs, and detect IP violations. In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders, and subtractors. Our techniques require no manual intervention and experiments show that they determine the functionality of >45% and up to 93% of the gates in each of the test circuits that we examine. We also demonstrate that our algorithms are scalable to real designs by experimenting with a very large, highly-optimized system-on-chip (SOC) design with over 375000 combinational elements. Our inference algorithms cover 68% of the gates in this SOC. We also demonstrate that our algorithms are effective in aiding a human analyst to detect hardware Trojans in an unstructured netlist.
A low-profile magneto-electric dipole antenna

A low-profile wideband magneto-electric dipole antenna is proposed. The antenna with a low profile of 13.79mm (0.128λ0, where λ0 refers to the center frequency of the operating frequency band), consisting of a shorted bowtie and two planar electric dipoles, fed by a coaxial-to-microstrip impedance transformer, achieves a wide impedance bandwidth of 45% with SWR≤1.5 from 2.12 to 3.35GHz and 54% with SWR≤2 from 2.05 to 3.55GHz. Within the operating frequency range, the antenna gain varies from 6 to 9.3dBi. Stable radiation pattern with low cross-polarization and low back radiation is observed.
The FFT on a GPU

The Fourier transform is a well known and widely used tool in many scientific and engineering fields. The Fourier transform is essential for many image processing techniques, including filtering, manipulation, correction, and compression. As such, the computer graphics community could benefit greatly from such a tool if it were part of the graphics pipeline. As of late, computer graphics hardware has become amazingly cheap, powerful, and flexible. This paper describes how to utilize the current generation of cards to perform the fast Fourier transform (FFT) directly on the cards. We demonstrate a system that can synthesize an image by conventional means, perform the FFT, filter the image, and finally apply the inverse FFT in well under 1 second for a 512 by 512 image. This work paves the way for performing complicated, real-time image processing as part of the rendering pipeline.
Ultrasound to improve the safety of hyaluronic acid filler treatments.

Hyaluronic acid fillers are known for a reliable safety profile, but complications do occur, even serious vascular adverse events.



To improve the safety of hyaluronic acid filler treatments.



Ultrasound is used to image hyaluronic acid fillers.



Before a filler treatment is performed with ultrasound, previous filler treatments can be brought in to sight and vascular mapping can be performed. In case of adverse events, the filler and the surrounding tissues are visible. Dislocation, abscesses, and vascular adverse events can be seen. Under ultrasound guidance, hyaluronidase can be injected directly into the filler deposit.



Ultrasound examination can be an important tool to improve the safety of hyaluronic acid filler treatments.
A phase shift full bridge based reconfigurable PEV onboard charger with extended ZVS range and zero duty cycle loss

In this paper, an integrated onboard charger architecture is proposed for plug-in electric vehicle (PEV). In this architecture, the phase shift full bridge (PSFB) converter serves as the main high voltage battery charging topology, and the half bridge LLC resonant converter serves as the low voltage battery charging topology. Under light charging mode, the half-bridge LLC is reconfigured to be paralleled with the PSFB topology, to guarantee zero voltage switching (ZVS) of the lagging-leg MOSFETs. Practical design considerations are presented for both the PSFB and the half bridge LLC converters. Switching frequency and the shifted phase angle provide two degrees of freedom to regulate the output voltage/current of both converters. The proposed architecture maintains low cost and high efficiency in this specific application. A 390V input, 420V/2.4A, 14V/21A outputs converter prototype is designed, simulated, and analyzed to verify the proof of concept.