nils-herrmann/clean_scirepeval huggingface dataset:
Spatial Data Mining Features between General Data Mining

Data mining is usually defined as searching,analyzing and sifting through large amounts of data to find relationships, patterns, or any significant statistical correlation. Spatial Data Mining (SDM) is the process of discovering interesting, useful, non-trivial patterns information or knowledge from large spatial datasets.Extracting interesting and useful patterns from spatial datasets must be more difficult than extracting the corresponding patterns from traditional numeric or categorical data due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation.Emphasized overviewed the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research. Extracting interesting patterns and rules from spatial datasets, such as remotely sensed imagery and associated ground data, can be of importance in precision agriculture, community planning,resource discovery and other areas.
A potentiometric tattoo sensor for monitoring ammonium in sweat.

The development and analytical characterization of a novel ion-selective potentiometric cell in a temporary-transfer tattoo platform for monitoring ammonium levels in sweat is presented. The fabrication of this skin-worn sensor, which is based on a screen-printed design, incorporates all-solid-state potentiometric sensor technology for both the working and reference electrodes, in connection to ammonium-selective polymeric membrane based on the nonactin ionophore. The resulting tattooed potentiometric sensor exhibits a working range between 10(-4) M to 0.1 M, well within the physiological levels of ammonium in sweat. Testing under stringent mechanical stress expected on the epidermis shows that the analytical performance is not affected by factors such as stretching or bending. Since the levels of ammonium are related to the breakdown of proteins, the new wearable potentiometric tattoo sensor offers considerable promise for monitoring sport performance or detecting metabolic disorders in healthcare. Such combination of the epidermal integration, screen-printed technology and potentiometric sensing represents an attractive path towards non-invasive monitoring of a variety of electrolytes in human perspiration.
On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations.

The expected number of secondary cases produced by a typical infected individual during its entire period of infectiousness in a completely susceptible population is mathematically defined as the dominant eigenvalue of a positive linear operator. It is shown that in certain special cases one can easily compute or estimate this eigenvalue. Several examples involving various structuring variables like age, sexual disposition and activity are presented.
Artificial neural networks: fundamentals, computing, design, and application.

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. This paper aims to familiarize the reader with ANN-based computing (neurocomputing) and to serve as a useful companion practical guide and toolkit for the ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation (BP) ANNs theory and design. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described. The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. Finally, as a practical application, BPANNs were used to model the microbial growth curves of S. flexneri. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH.
Comparison of Heart-Rate-Variability Recording With Smartphone Photoplethysmography, Polar H7 Chest Strap, and Electrocardiography.

To establish the validity of smartphone photoplethysmography (PPG) and heart-rate sensor in the measurement of heart-rate variability (HRV).



29 healthy subjects were measured at rest during 5 min of guided breathing and normal breathing using smartphone PPG, a heart-rate chest strap, and electrocardiography (ECG). The root mean sum of the squared differences between R-R intervals (rMSSD) was determined from each device.



Compared to ECG, the technical error of estimate (TEE) was acceptable for all conditions (average TEE CV% [90% CI] = 6.35 [5.13; 8.5]). When assessed as a standardized difference, all differences were deemed "trivial" (average standard difference [90% CI] = 0.10 [0.08; 0.13]). Both PPG- and heart-rate-sensor-derived measures had almost perfect correlations with ECG (R = 1.00 [0.99; 1.00]).



Both PPG and heart-rate sensors provide an acceptable agreement for the measurement of rMSSD when compared with ECG. Smartphone PPG technology may be a preferred method of HRV data collection for athletes due to its practicality and ease of use in the field.
Terrorism , Security , and the Threat of Counterterrorism

In America, Britain and Australia the threat of terrorism has been used to justify radical new legislation that gives police and intelligence agencies unprecedented powers to detain and question people believed to have information connected to terrorism. In this paper I explore the nature of the threat of non-state terrorism—threat to national security and the well-being of citizens. I argue that terrorism does not pose a threat sufficient to justify the kinds of counterterrorism legislation currently being enacted. Furthermore many of the current counterterrorism practices pose a greater threat to individual physical security and well-being than non-state terrorism. We should fear counterterrorism more than we fear terrorism.
Comparison of Longley-Rice, ITM and ITWOM propagation models for DTV and FM broadcasting

With the rapid deployment of digital TV, there is an increasing need for accurate point-to-area prediction tools. There is a great deal of propagation models for coverage prediction of DTV. Some of them are pure empirical models, and others are mixed, empirical-analytical models, based on measurement campaigns and electromagnetic theory. The aim of this paper is to compare accurate measurements taken by a Rohde & Schwarz FSH-3 portable spectrum analyzer and precision antennas (biconical and log-periodic), with simulation results derived from coverage prediction models, like the NTIA-ITS Longley-Rice model, the ITM (Irregular Terrain Model) using the 3-arc-second SRTM (Satellite Radar Topography Mission) data that is available freely, and the newer ITWOM (Irregular Terrain with Obstructions Model) model which combines equations from ITU-R P.1546 model with Beer's law and Snell's law. Furthermore, measurements for analog FM broadcasting are compared to predictions from the above mentioned models.
Radiation-induced soft errors in advanced semiconductor technologies

The once-ephemeral radiation-induced soft error has become a key threat to advanced commercial electronic components and systems. Left unchallenged, soft errors have the potential for inducing the highest failure rate of all other reliability mechanisms combined. This article briefly reviews the types of failure modes for soft errors, the three dominant radiation mechanisms responsible for creating soft errors in terrestrial applications, and how these soft errors are generated by the collection of radiation-induced charge. The soft error sensitivity as a function of technology scaling for various memory and logic components is then presented with a consideration of which applications are most likely to require soft error mitigation.
X-Tree Diff+: Efficient Change Detection Algorithm in XML Documents

As web documents proliferate fast, the need fo real-time computation of change (edit script) between web documents increases. Though fast heuristic algorithms have been proposed recently, the qualities of edit scripts produced by them are not satisfactory. In this paper, we propose X-tree Diff+ which produces better quality of edit scripts by introducing a tuning step based on the notion of consistency of matching. We also add copy operation to provide users more convenience. Tuning and copy operation increase matching ratio drastically. X-tree Diff+ produces better quality of edit scripts and runs fast equivalent to the time complexity of fastest heuristic algorithms.
Induction motor fault diagnosis using labview

Now-a-days, manufacturing companies of electrical machines are in need of easy and early fault detection techniques as they are attire by the customers. Though many researchers have developed many fault diagnosing techniques, user friendly software with automated long measurement process is needed so that the non-technical persons also can operate to identify the faults occurred. In our work the healthy induction motor is modeled in stationary reference frame. Faulty conditions are included in the model to analyze the effect of fault in the motor using the user friendly software, LabVIEW.
Language, thought and reference.

How should we best analyse the meaning of proper names, indexicals, demonstratives, both simple and complex, and definite descriptions? In what relation do such expressions stand to the objects they designate? In what relation do they stand to mental representations of those objects? Do these expressions form a semantic class, or must we distinguish between those that arc referential and those that are quantificational? Such questions have constituted one of the core research areas in the philosophy of language for much of the last century, yet consensus remains elusive: the field is still divided, for instance, between those who hold that all such expressions are semantically descriptive and those who would analyse most as the natural language counterparts of logical individual constants. The aim of this thesis is to cast new light on such questions by approaching them from within the cognitive framework of Sperber and Wilson's Relevance Theory. Relevance Theory offers not just an articulated pragmatics but also a broad conception of the functioning of natural language which differs radically from that presupposed within (most of) the philosophy of language. The function of linguistic expressions, on this conception, is not to determine propositional content, but rather to provide inferential premises which, in parallel with context and general pragmatic principles, will enable a bearer to reach the speaker's intended interpretation. Working within this framework, I shall argue that the semantics of the expressions discussed should best be analysed not in terms of their relation to those objects which, on occasions of use, they may designate, but rather in terms of the indications they offer a hearer concerning the mental representation which constitutes the content of a speaker's informative intention. Such an analysis can, I shall claim, capture certain key data on reference which have proved notoriously problematic, while respecting a broad range of apparently conflicting intuitions.
Bangladeshi Number Plate Detection: Cascade Learning vs. Deep Learning

This work investigated two different machine learning techniques: Cascade Learning and Deep Learning, to find out which algorithm performs better to detect the number plate of vehicles registered in Bangladesh. To do this, we created a dataset of about 1000 images collected from a security camera of Independent University, Bangladesh. Each image in the dataset were then labelled manually by selecting the Region of Interest (ROI). In the Cascade Learning approach, a sliding window technique was used to detect objects. Then a cascade classifier was employed to determine if the window contained object of interest or not. In the Deep Learning approach, CIFAR-10 dataset was used to pre-train a 15-layer Convolutional Neural Network (CNN). Using this pretrained CNN, a Regions with CNN (R-CNN) was then trained using our dataset. We found that the Deep Learning approach (maximum accuracy 99.60% using 566 training images) outperforms the detector constructed using Cascade classifiers (maximum accuracy 59.52% using 566 positive and 1022 negative training images) for 252 test images.
The Value of IT-Enabled Retailer Learning: Personalized Product Recommendations and Customer Store Loyalty in Electronic Markets

In general, most items on this list match my preferences very well.  In general, most items on this list fit my tastes very well.  In general, most items on this list are interesting to me. Website Knowledge WBKN1: I always know where I can find the products/information I am looking for at Amazon.com’s website. WBKN2: I visit Amazon.com very often. WBKN3: I have been to Amazon.com many times. Product Evaluation Cost (reverse coded) PEC1: It was very easy for me to make this purchase decision. PEC2: I had no difficulty deciding which item would be best for me. PEC3: Making this purchase decision was an easy task for me. Product Screening Cost (reverse coded) PSC1: I had no problem locating the items I was interested in at Amazon.com.  It was very easy for me to locate the items I was interested in at Amazon.com.  Locating the items I was interested in at Amazon.com was very easy. Decision-making Quality DMQ1: I have picked the items that best fit my taste among all DVDs available at Amazon.com. DMQ2: I have selected the items I like the most among all DVDs available at Amazon.com.  These two items are my favorite among all DVDs available at Amazon.com. Repurchase Intention REPUR1: I will consider Amazon.com the first choice to buy similar products in the future. REPUR2: I will buy more similar products at Amazon.com in the future. REPUR3: I will come back to Amazon.com to buy similar products in the future.
A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests

We propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.
Spectrum and Energy-Efficient Beamspace MIMO-NOMA for Millimeter-Wave Communications Using Lens Antenna Array

The recent concept of beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems without obvious performance loss. However, the fundamental limit of existing beamspace MIMO is that the number of supported users cannot be larger than the number of RF chains at the same time-frequency resources. To break this fundamental limit, in this paper, we propose a new spectrum and energy-efficient mmWave transmission scheme that integrates the concept of non-orthogonal multiple access (NOMA) with beamspace MIMO, i.e., beamspace MIMO-NOMA. By using NOMA in beamspace MIMO systems, the number of supported users can be larger than the number of RF chains at the same time-frequency resources. In particular, the achievable sum rate of the proposed beamspace MIMO-NOMA in a typical mmWave channel model is analyzed, which shows an obvious performance gain compared with the existing beamspace MIMO. Then, a precoding scheme based on the principle of zero forcing is designed to reduce the inter-beam interferences in the beamspace MIMO-NOMA system. Furthermore, to maximize the achievable sum rate, a dynamic power allocation is proposed by solving the joint power optimization problem, which not only includes the intra-beam power optimization, but also considers the inter-beam power optimization. Finally, an iterative optimization algorithm with low complexity is developed to realize the dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA can achieve higher spectrum and energy efficiency compared with the existing beamspace MIMO.
Effect of different surface pre-treatments and luting materials on shear bond strength to PEEK.

To assess the bonding potential of a universal composite resin cement and an adhesive/composite system to differently pre-treated PEEK surfaces.



One hundred and fifty PEEK disks were embedded in epoxy resin, polished (P4000 grit) and treated as follows (n=30/group): (A) no treatment, (B) acid etching with sulfuric acid (98%) for 1 min, (C) sandblasting for 10s with 50 microm alumina, (D) sandblasting for 10s with 110 microm alumina and (E) silica coating using the Rocatec system (3M ESPE). Polished and sandblasted (50 microm alumina) cp titanium (grade 4) served as a control. Acrylic hollow cylinders were either luted with a universal composite resin cement (RelyX Unicem) or an unfilled resin (Heliobond) and a hybrid composite (Tetric) to the specimens. Bond strength was measured in a shear test and failure modes were assessed. Statistic analysis was performed with one-way ANOVA followed by a post hoc Scheffé test and unpaired t-tests.



With the universal composite resin cement, no bond could be established on any PEEK surfaces, except specimens etched with sulfuric acid (19.0+/-3.4MPa). Shear bond strength to titanium was significantly lower (8.7+/-2.8MPa, p<0.05). Applying the adhesive/composite system, shear bond strength values on pre-treated PEEK ranged from 11.5+/-3.2MPa (silica coating) to 18.2+/-5.4MPa (acid etched) with no statistically significant differences (p>0.05). No bond was obtained on the polished surface.



Bonding to PEEK is possible when using a bonding system. No adherence can be achieved with the tested universal composite resin cement except on an etched surface. The results strongly encourage further research in PEEK application in dentistry.
A Solution to Single Point of Failure Using Voter Replication and Disagreement Detection

This paper suggests a method, called distributed voting, to overcome the problem of the single point of failure in a TMR system used in robotics and industrial control applications. It uses time redundancy and is based on TMR with disagreement detector feature. This method masks faults occurring in the voter where the TMR system can continue its function properly. The method has been evaluated by injecting faults into Vertex2Pro and Vertex4 Xilinx FPGAs An analytical evolution is also performed. The results of both evaluation approaches show that the proposed method can improve the reliability and the mean time to failure (MTTF) of a TMR system by at least a factor of (2-RV (t)) where RV(t) is the reliability of the voter
Eigenvalues of a real supersymmetric tensor

In this paper, we define the symmetric hyperdeterminant, eigenvalues and E-eigenvalues of a real supersymmetric tensor. We show that eigenvalues are roots of a one-dimensional polynomial, and when the order of the tensor is even, E-eigenvalues are roots of another one-dimensional polynomial. These two one-dimensional polynomials are associated with the symmetric hyperdeterminant. We call them the characteristic polynomial and the E-characteristic polynomial of that supersymmetric tensor. Real eigenvalues (E-eigenvalues) with real eigenvectors (E-eigenvectors) are called H-eigenvalues (Z-eigenvalues). W hen the order of the supersymmetric tensor is even, H-eigenvalues (Z-eigenvalues) exist and the supersymmetric tensor is positive definite if and only if all of its H-eigenvalues (Z-eigenv alues) are positive. Anmth-order n-dimensi onal supersymmetric tensor where m is even has exactlyn(m − 1)n−1 eigenvalues, and the number of its E-eigenvalues is strictly less thann(m − 1)n−1 when m ≥ 4. We show that the product of all the eigenvalues is equal to the value of the symmetric hyperdeterminant, while the sum of all the eigenvalues is equal to the sum of the diagonal elements of tha t supersymmetric tensor, multiplied by (m − 1)n−1. The n(m−1)n−1 eigenvalues are distributed in n disks inC. Thecenters and radii of these n disks are the diagonal elements, and the sums of the absolute values of the corresponding off-diagonal elements, of that supersymmetric tensor. On the other hand, E-eigenvalues are invariant under orthogonal transformations.
Voice Activated Virtual Assistants Personality Perceptions and Desires- Comparing Personality Evaluation Frameworks

Currently, Voice Activated Virtual Assistants and Artificial Intelligence technologies are not just about performance or the functionalities they can carry out, it is also about the associated personality. This empirical multi-country study explores the personality perceptions of current VAVA users regarding these technologies. Since this is a rather unexplored territory for research, this study has identified two well-established personality evaluation methodologies, Aaker’s traits approach and Jung’s archetypes, to investigate current perceived personality and future desired personality of the four main Voice Activated Virtual Assistants: Siri, Google Assistant, Cortana and Alexa. Following are a summary of results by each methodology, and an analysis of the commonalities found between the two methodologies.
A Brief Review of Digital Games and Learning

Digital games have caught the attention of scholars across a variety of disciplines. Today, scholars from fields as diverse as media design, literature, computer science, education, and theatre studies have together contributed to the understanding of this emergent medium and its phenomenon as a learning tool The unsatisfying experience of directly adopting game formats into educational contexts has motivated researchers to study successful (commercial) digital games, in particular to examine the play activities of digital games and the principles underlying the design of digital games in order to develop concepts and strategies for rich virtual learning contexts. In this review, provided is a brief account of academic arguments regarding digital games and learning.