nils-herrmann/clean_scirepeval huggingface dataset:
INCOME TAX EVASION : A THEORETICAL ANALYSIS

Theoretical analysis of the connection between taxation and risktaking has mainly been concerned with the effect of taxes on portfolio decisions of consumers, Mossin (1968b) and Stiglitz (1969). However, there are some problems which are not naturally classified under this heading and which, although of considerable practical interest, have been left out of the theoretical discussions. One such problem is tax evasion. This takes many forms, and one can hardly hope to give a completely general analysis of all these. Our objective in this paper is therefore the more limited one of analyzing the individual taxpayer’s decision on whether and to what extent to avoid taxes by deliberate underreporting. On the one hand our approach is related to the studies of economics of criminal activity, as e.g. in the papers by Becker ( 1968) and by Tulkens and Jacquemin (197 1). On the other hand it is related to the analysis of optimal portfolio and insurance policies in the economics of uncertainty, as in the work by Arrow ( 1970), Mossin ( 1968a) and several others. We shall start by considering a simple static model where this decision is the only one with which the individual is concerned, so that we ignore the interrelationships that probably exist with other types of economic choices. After a detailed study of this simple case (sections
Land-vehicle navigation using GPS

The Global Positioning System (GPS) has made navigation systems practical for a number of land-vehicle navigation applications. Today, GPS-based navigation systems can be found in motor vehicles, farming and mining equipment, and a variety of other land-based vehicles (e.g., golf carts and mobile robots). In Section II of this paper, each of these applications is discussed, and the reader is introduced to some of the issues involved with each one. Beginning in Section III, one particular technical aspect of navigation for land vehicles is discussed. Specifically, the research discussed in this paper presents a quantitative examination of the impact that individual navigation sensors have on the performance of a land-vehicle navigation system. A range of navigation sensor performance levels and their influence on vehicle positioning accuracy are examined. Results show that, for a typical navigation system, positioning error is dominated by the accuracy of the position fixes provided by the GPS receiver when GPS position fixes are available and by the rate gyro’s bias drift when GPS position fixes are not available. Furthermore, results show that the accuracy of the GPS position fixes has a significant impact on the relative contributions that each dead-reckoning navigation sensor error makes. The implications of these results for navigation system design and sensor design are discussed.
Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networks

We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition. Opposing to deep CNN architectures with multiple convolutional and pooling layers topped up with multiple fully connected layers, the proposed network consists of only three layers: convolutional, pooling, and softmax layer. Two further features distinguish it from the deep architectures that have been proposed for the task: varying-size convolutional filters at the convolutional layer and 1-max pooling scheme at the pooling layer. In intuition, the network tends to select the most discriminative features from the whole audio signals for recognition. Our proposed CNN not only shows state-of-the-art performance on the standard task of robust audio event recognition but also outperforms other deep architectures up to 4.5% in terms of recognition accuracy, which is equivalent to 76.3% relative error reduction.
Train faster, generalize better: Stability of stochastic gradient descent

We show that parametric models trained by a stochastic gradient method (SGM) with few iterations have vanishing generalization error. We prove our results by arguing that SGM is algorithmically stable in the sense of Bousquet and Elisseeff. Our analysis only employs elementary tools from convex and continuous optimization. We derive stability bounds for both convex and non-convex optimization under standard Lipschitz and smoothness assumptions. Applying our results to the convex case, we provide new insights for why multiple epochs of stochastic gradient methods generalize well in practice. In the non-convex case, we give a new interpretation of common practices in neural networks, and formally show that popular techniques for training large deep models are indeed stability-promoting. Our findings conceptually underscore the importance of reducing training time beyond its obvious benefit.
Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain

Internet of Medical Things (IOMT) is playing vital role in healthcare industry to increase the accuracy, reliability and productivity of electronic devices. Researchers are contributing towards a digitized healthcare system by interconnecting the available medical resources and healthcare services. As IOT converge various domains but our focus is related to research contribution of IOT in healthcare domain. This paper presents the peoples contribution of IOT in healthcare domain, application and future challenges of IOT in term of medical services in healthcare. We do hope that this work will be useful for researchers and practitioners in the field, helping them to understand the huge potential of IoT in medical domain and identification of major challenges in IOMT. This work will also help the researchers to understand applications of IOT in healthcare domain. This contribution will help the researchers to understand the previous contribution of IOT in healthcare industry.
Discriminative Deep Random Walk for Network Classification

Deep Random Walk (DeepWalk) can learn a latent space representation for describing the topological structure of a network. However, for relational network classification, DeepWalk can be suboptimal as it lacks a mechanism to optimize the objective of the target task. In this paper, we present Discriminative Deep Random Walk (DDRW), a novel method for relational network classification. By solving a joint optimization problem, DDRW can learn the latent space representations that well capture the topological structure and meanwhile are discriminative for the network classification task. Our experimental results on several real social networks demonstrate that DDRW significantly outperforms DeepWalk on multilabel network classification tasks, while retaining the topological structure in the latent space. DDRW is stable and consistently outperforms the baseline methods by various percentages of labeled data. DDRW is also an online method that is scalable and can be naturally parallelized.
The effects of sensorimotor-based intervention versus therapeutic practice on improving handwriting performance in 6- to 11-year-old children.

The aim of this study was to investigate the effects of two interventions (sensorimotor and therapeutic practice) on handwriting and selected sensorimotor components in elementary-age children.



Thirty-eight children 6 to 11 years of age with handwriting dysfunction but no identified educational need were randomly assigned to one of the two intervention groups or a control group. Intervention groups met four times per week over 5 weeks. Handwriting was measured pre- and postintervention using the Test of Handwriting Skills. Visual perception (motor-reduced), visual-motor integration, proprioception, and in-hand manipulation were also measured.



Children receiving therapeutic practice moderately improved handwriting whereas children receiving sensorimotor intervention declined in handwriting performance. The control group did not change significantly. Sensorimotor impairment was noted at pretest in three or four components and selected sensorimotor component function improved with intervention.



Therapeutic practice was more effective than sensorimotor-based intervention at improving handwriting performance. Children who received sensorimotor intervention improved in some sensorimotor components but also experienced a clinically meaningful decline in handwriting performance.
Technology Trends and Challenges of Antennas for Satellite Communication Systems

Communication is one of the most essential activities of mankind and there is no doubt that satellite communications have made a tremendous stride in this matter. The goal has been to create uninterrupted, seamless, high throughput connectivity no matter where. The fact that this is all possible is because of the existence of electromagnetic waves: Antennas play a pivotal role in advanced functionality, efficient usage of the bandwidth, and cost effective deployment of this technology. The trends of the technological development of antennas for a wide range of commercial satellite communication services and the challenges faced in their development are chronologically reviewed. Various antenna concepts are summarized and future challenges are projected. A rather comprehensive list of references is included to assist the reader with further reading beyond this paper.
An efficient RFID mutual authentication scheme based on ECC

Recently, Radio Frequency Identification (RFID) technique has been widely deployed in many applications, such as medical drugs management in hospitals and missing children searching in amusement parks. The applications basically can be classified into two types: non-public key cryptosystem (PKC)-based and PKC-based. However, many of them have been found to be flawed in the aspect of privacy problem. Therefore, many researchers tried to resolve this problem. They mainly investigated on how low-cost RFID tags can be used in large-scale systems. However, after analyses, we found those studies have some problems, such as suffering physical attack or de-synch attack. Hence, in this paper, we try to design an efficient RFID scheme based on Elliptic Curve Cryptography (ECC) to avoid these problems. After analyses, we conclude that our scheme not only can resist various kinds of attacks but also outperforms the other ECC based RFID schemes in security requirements, with needing only little extra elliptic curve point multiplications.
A Simple Yet Effective Data Clustering Algorithm

In this paper, we use a simple concept based on k-reverse nearest neighbor digraphs, to develop a framework RECORD for clustering and outlier detection. We developed three algorithms - (i) RECORD algorithm (requires one parameter), (ii) Agglomerative RECORD algorithm (no parameters required) and (iii) Stability-based RECORD algorithm (no parameters required). Our experimental results with published datasets, synthetic and real-life datasets show that RECORD not only handles noisy data, but also identifies the relevant clusters. Our results are as good as (if not better than) the results got from other algorithms.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition

Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and nondominated sorting genetic algorithm II (NSGA-II). Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjective optimization problems. It has been shown that MOEA/D using objective normalization can deal with disparately-scaled objectives, and MOEA/D with an advanced decomposition method can generate a set of very evenly distributed solutions for 3-objective test instances. The ability of MOEA/D with small population, the scalability and sensitivity of MOEA/D have also been experimentally investigated in this paper.
A 6-bit 220-MS/s time-interleaving SAR ADC in 0.18-µm digital CMOS process

This paper reports a 6-bit 220-MS/s time-interleaving successive approximation register analog-to-digital converter (SAR ADC) for low-power low-cost CMOS integrated systems. The major concept of the design is based on the proposed set-and-down capacitor switching method in the DAC capacitor array. Compared to the conventional switching method, the average switching energy is reduced about 81%. At 220-MS/s sampling rate, the measured SNDR and SFDR are 32.62 dB and 48.96 dB respectively. The resultant ENOB is 5.13 bits. The total power consumption is 6.8 mW. Fabricated in TSMC 0.18-µm 1P5M Digital CMOS technology, the ADC only occupies 0.032 mm2 active area.
Happy People Live Longer : Subjective Well-Being Contributes to Health and Longevityaphw

Seven types of evidence are reviewed that indicate that high subjective wellbeing (such as life satisfaction, absence of negative emotions, optimism, and positive emotions) causes better health and longevity. For example, prospective longitudinal studies of normal populations provide evidence that various types of subjective well-being such as positive affect predict health and longevity, controlling for health and socioeconomic status at baseline. Combined with experimental human and animal research, as well as naturalistic studies of changes of subjective well-being and physiological processes over time, the case that subjective well-being influences health and longevity in healthy populations is compelling. However, the claim that subjective well-being lengthens the lives of those with certain diseases such as cancer remains controversial. Positive feelings predict longevity and health beyond negative feelings. However, intensely aroused or manic positive affect may be detrimental to health. Issues such as causality, effect size, types of subjective well-being, and statistical controls are discussed.
On the Analysis and Interpretation of Inhomogeneous Quadratic Forms as Receptive Fields

In this letter, we introduce some mathematical and numerical tools to analyze and interpret inhomogeneous quadratic forms. The resulting characterization is in some aspects similar to that given by experimental studies of cortical cells, making it particularly suitable for application to second-order approximations and theoretical models of physiological receptive fields. We first discuss two ways of analyzing a quadratic form by visualizing the coefficients of its quadratic and linear term directly and by considering the eigenvectors of its quadratic term. We then present an algorithm to compute the optimal excitatory and inhibitory stimulithose that maximize and minimize the considered quadratic form, respectively, given a fixed energy constraint. The analysis of the optimal stimuli is completed by considering their invariances, which are the transformations to which the quadratic form is most insensitive, and by introducing a test to determine which of these are statistically significant. Next we propose a way to measure the relative contribution of the quadratic and linear term to the total output of the quadratic form. Furthermore, we derive simpler versions of the above techniques in the special case of a quadratic form without linear term. In the final part of the letter, we show that for each quadratic form, it is possible to build an equivalent two-layer neural network, which is compatible with (but more general than) related networks used in some recent articles and with the energy model of complex cells. We show that the neural network is unique only up to an arbitrary orthogonal transformation of the excitatory and inhibitory subunits in the first layer.
Method construction - a core approach to organizational engineering

Dominated by the behavioral science approach for a long time, information systems research increasingly acknowledges design science as a complementary approach. While primarily information systems instantiations, but also constructs and models have been discussed quite comprehensively, the design of methods is addressed rarely. But methods appear to be of utmost importance particularly for organizational engineering. This paper justifies method construction as a core approach to organizational engineering. Based on a discussion of fundamental scientific positions in general and approaches to information systems research in particular, appropriate conceptualizations of 'method' and 'method construction' are presented. These conceptualizations are then discussed regarding their capability of supporting organizational engineering. Our analysis is located on a meta level: Method construction is conceptualized and integrated from a large number of references. Method instantiations or method engineering approaches however are only referenced and not described in detail.
Autonomous Helicopter Aerobatics through Apprenticeship Learning

Autonomous helicopter flight is widely regarded to be a highly challenging control problem. Despite this fact, human experts can reliably fly helicopters through a wide range of maneuvers, including aerobatic maneuvers at the edge of the helicopter’s capabilities. We present apprenticeship learning algorithms, which leverage expert demonstrations to efficiently learn good controllers for tasks being demonstrated by an expert. These apprenticeship learning algorithms have enabled us to significantly extend the state of the art in autonomous helicopter aerobatics. Our experimental results include the first autonomous execution of a wide range of maneuvers, including but not limited to in-place flips, in-place rolls, loops and hurricanes, and even auto-rotation landings, chaos and tic-tocs, which only exceptional human pilots can perform. Our results also include complete airshows, which require autonomous transitions between many of these maneuvers. Our controllers perform as well as, and often even better than, our expert pilot.
The Multilevel Modular DC Converter

The modular multilevel converter (M2C) has become an increasingly important topology in medium- and high-voltage applications. A limitation is that it relies on positive and negative half-cycles of the ac output voltage waveform to achieve charge balance on the submodule capacitors. To overcome this constraint a secondary power loop is introduced that exchanges power with the primary power loops at the input and output. Power is exchanged between the primary and secondary loops by using the principle of orthogonality of power flow at different frequencies. Two modular multilevel topologies are proposed to step up or step down dc in medium- and high-voltage dc applications: the tuned filter modular multilevel dc converter and the push-pull modular multilevel dc converter. An analytical simulation of the latter converter is presented to explain the operation.
Kernelized locality-sensitive hashing for scalable image search

Fast retrieval methods are critical for large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sub-linear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful image-based kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several large-scale datasets, and show that it enables accurate and fast performance for example-based object classification, feature matching, and content-based retrieval.
Automatic Detection and Classification of Road Lane Markings Using Onboard Vehicular Cameras

This paper presents a new approach for road lane classification using an onboard camera. Initially, lane boundaries are detected using a linear-parabolic lane model, and an automatic on-the-fly camera calibration procedure is applied. Then, an adaptive smoothing scheme is applied to reduce noise while keeping close edges separated, and pairs of local maxima-minima of the gradient are used as cues to identify lane markings. Finally, a Bayesian classifier based on mixtures of Gaussians is applied to classify the lane markings present at each frame of a video sequence as dashed, solid, dashed solid, solid dashed, or double solid. Experimental results indicate an overall accuracy of over 96% using a variety of video sequences acquired with different devices and resolutions.
A Double-Sided LCC Compensation Network and Its Tuning Method for Wireless Power Transfer

This paper proposes a double-sided LCC compensation network and its tuning method for wireless power transfer (WPT). With the proposed topology and its tuning method, the resonant frequency is irrelevant with the coupling coefficient between the two coils and is also independent of the load condition, which means that the system can work at a constant switching frequency. Analysis in frequency domain is given to show the characteristics of the proposed method. We also propose a method to tune the network to realize zero voltage switching (ZVS) for the Primary-side switches. Simulation and experimental results verified analysis and validity of the proposed compensation network and the tuning method. A wireless charging system with output power of up to 7.7 kW for electric vehicles was built, and 96% efficiency from dc power source to battery load is achieved.
Recognizing Text with Perspective Distortion in Natural Scenes

This paper presents an approach to text recognition in natural scene images. Unlike most existing works which assume that texts are horizontal and frontal parallel to the image plane, our method is able to recognize perspective texts of arbitrary orientations. For individual character recognition, we adopt a bag-of-key points approach, in which Scale Invariant Feature Transform (SIFT) descriptors are extracted densely and quantized using a pre-trained vocabulary. Following [1, 2], the context information is utilized through lexicons. We formulate word recognition as finding the optimal alignment between the set of characters and the list of lexicon words. Furthermore, we introduce a new dataset called StreetViewText-Perspective, which contains texts in street images with a great variety of viewpoints. Experimental results on public datasets and the proposed dataset show that our method significantly outperforms the state-of-the-art on perspective texts of arbitrary orientations.
XONTO: An Ontology-Based System for Semantic Information Extraction from PDF Documents

Information extraction is of paramount importance in several real world applications in the areas of business intelligence, competitive and military intelligence. Although several sophisticated and indeed complex approaches were proposed, they are still limited in many aspects. In this paper the novel ontology-based system named XONTO, that allows the semantic extraction of information from PDF unstructured documents, is presented. The XONTO system is founded on the idea of self-describing ontologies in which objects and classes can be equipped by a set of rules named descriptors. These rules represent patterns that allow to automatically recognize and extract ontology objects contained in PDF documents also when information is arranged in tabular form. This way a self-describing ontology expresses the semantic of the information to extract and the rules that, in turn, populate itself. In the paper XONTO system behaviors and structure are sketched by means of a running example.