nils-herrmann/clean_scirepeval huggingface dataset:
A Formal Theory of Inductive Inference. Part II

The following sections will apply the foregoing induction systems to three specific types of problems, and discuss the “reasonableness” of the results obtained. Section 4.1 deals with the Bernoulli sequence. The predictions obtained are identical to those given by “Laplace’s Rule of Succession.” A particularly important technique is used to code the original sequence into a set of integers which constitute its “descriptions” for the problems of Sections 4.2 and 4.3. Section 4.2 deals with the extrapolation of a sequence in which there are certain kinds of intersymbol constraints. Codes for such sequences are devised by defining special symbols for subsequences whose frequencies are unusually high or low. Some properties of this coding method are discussed, and they are found to be intuitively reasonable. A preliminary computer program has been written for induction using this coding method. However, there are some important simplifications used in the program, and it is uncertain as to whether it can make useful predictions. Section 4.3 describes the use of phrase structure grammars for induction. A formal solution is presented and although the resultant analysis indicates that this model conforms to some extent to intuitive expectations, the author feels that it still has at least one serious shortcoming in that it has no good means
Design And Analysis Of Fiber Reinforced Polymer ( FRP ) Leaf Spring

Weight reduction is now the main issue in automobile industries. Weight reduction can be achieved primarily by the introduction of better material, design optimization and better manufacturing processes. The achievement of weight reduction with adequate improvement of mechanical properties has made composite a very good replacement material for conventional steel. Selection of material is based on cost and strength of material. The composite materials have more elastic strain energy storage capacity and high strength to weight ratio as compared with those of steel, so multi-leaf steel springs are being replaced by mono-leaf composite springs. The paper gives the brief look on the suitability of composite leaf spring on vehicles and their advantages. The objective of the present work is design, analysis and fabrication of mono composite leaf spring. The design constraints are stress and deflections. The material selected is glass fibre reinforced plastic (GFRP) and the epoxy resin can be used which is more economical to reduce total cost of composite leaf spring with similar mechanical and geometrical properties to the multileaf spring. The composite leaf spring is fabricated by hand layup technique and tested. The testing was performed experimentally with the help of UTM and by (FEA) using ANSYS software showing stresses and deflections were verified with analytical and experimental results. Compared to the steel spring, the composite spring has stresses that are much lower, the spring weight is nearly 74% lower.
Watch, attend and parse: An end-to-end neural network based approach to handwritten mathematical expression recognition

Machine recognition of a handwritten mathematical expression (HME) is challenging due to the ambiguities of handwritten symbols and the two-dimensional structure of mathematical expressions. Inspired by recent work in deep learning, we present Watch, Attend and Parse (WAP), a novel end-to-end approach based on neural network that learns to recognize HMEs in a two-dimensional layout and outputs them as one-dimensional character sequences in LaTeX format. Inherently unlike traditional methods, our proposed model avoids problems that stem from symbol segmentation, and it does not require a predefined expression grammar. Meanwhile, the problems of symbol recognition and structural analysis are handled, respectively, using a watcher and a parser. We employ a convolutional neural network encoder that takes HME images as input as the watcher and employ a recurrent neural network decoder equipped with an attention mechanism as the parser to generate LaTeX sequences. Moreover, the correspondence between the input expressions and the output LaTeX sequences is learned automatically by the attention mechanism. We validate the proposed approach on a benchmark published by the CROHME international competition. Using the official training dataset, WAP significantly outperformed the state-of-the-art method with an expression recognition accuracy of 46.55% on CROHME 2014 and 44.55% on CROHME 2016.
A secure authentication scheme based on elliptic curve cryptography for IoT and cloud servers

The Internet of Things (IoT) is now a buzzword for Internet connectivity which extends to embedded devices, sensors and other objects connected to the Internet. Rapid development of this technology has led to the usage of various embedded devices in our daily life. However, for resource sharing and communication among these devices, there is a requirement for connecting these embedded devices to a large pool of resources like a cloud. The promising applications of IoT in Government and commercial sectors are possible by integrating cloud servers with these embedded devices. But such an integration of technologies involves security issues like data privacy and authentication of devices whenever information is exchanged between them. Recently, Kalra and Sood proposed an authentication scheme based on elliptic curve cryptography (ECC) for IoT and cloud servers and claimed that their scheme satisfies all security requirements and is immune to various types of attacks. However, in this paper, we show that Kalra and Sood scheme is susceptible to offline password guessing and insider attacks and it does not achieve device anonymity, session key agreement, and mutual authentication. Keeping in view of the shortcomings of Kalra and Sood’s scheme, we have proposed an authentication scheme based on ECC for IoT and cloud servers. In the proposed scheme in this paper, we have formally analyzed the security properties of the designed scheme by the most widely accepted and used Automated Validation of Internet Security Protocols and Applications tool. Security and performance analysis show that when compared with other related schemes, the proposed scheme is more powerful, efficient, and secure with respect to various known attacks.
Multi-source transformer with combined losses for automatic post editing

Recent approaches to the Automatic Postediting (APE) of Machine Translation (MT) have shown that best results are obtained by neural multi-source models that correct the raw MT output by also considering information from the corresponding source sentence. To this aim, we present for the first time a neural multi-source APE model based on the Transformer architecture. Moreover, we employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics used for the task. These are the main features of our submissions to the WMT 2018 APE shared task, where we participated both in the PBSMT subtask (i.e. the correction of MT outputs from a phrase-based system) and in the NMT subtask (i.e. the correction of neural outputs). In the first subtask, our system improves over the baseline up to -5.3 TER and +8.23 BLEU points ranking second out of 11 submitted runs. In the second one, characterized by the higher quality of the initial translations, we report lower but statistically significant gains (up to -0.38 TER and +0.8 BLEU), ranking first out of 10 submissions.
Smooth trajectory planning for fully automated passengers vehicles - spline and clothoid based methods and its simulation

A new approach for mobility, providing an alternative to the private passenger car, by offering the same flexibility but with much less nuisances, is emerging, based on fully automated electric vehicles. A fleet of such vehicles might be an important element in a novel individual, door-to-door, transportation system to the city of tomorrow. For fully automated operation, trajectory planning methods that produce smooth trajectories, with low associated accelerations and jerk, for providing passenger ́s comfort, are required. This paper addresses this problem proposing an approach that consists of introducing a velocity planning stage to generate adequate time sequences for usage in the interpolating curve planners. Moreover, the generated speed profile can be merged into the trajectory for usage in trajectory-tracking tasks like it is described in this paper, or it can be used separately (from the generated 2D curve) for usage in pathfollowing tasks. Three trajectory planning methods, aided by the speed profile planning, are analysed from the point of view of passengers' comfort, implementation easiness, and trajectory tracking.
Do We Blindly Trust Self-Driving Cars

Trust is an essential factor in ensuring robust human-robot interaction. However, recent work suggests that people can be too trusting of the technology with which they interact during emergencies, causing potential harm to themselves. To test whether this "over-trust" also extends to normal day-to-day activities, such as driving a car, we carried out a series of experiments with an autonomous car simulator. Participants (N=73) engaged in a scenario with no, correct or false audible information regarding the state of traffic around the self-driving vehicle, and were told they could assume control at any point in the interaction. Results show that participants trust the autonomous system, even when they should not, leading to potential dangerous situations.
Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization

We propose a new unsupervised sentence salience framework for Multi-Document Summarization (MDS), which can be divided into two components: latent semantic modeling and salience estimation. For latent semantic modeling, a neural generative model called Variational Auto-Encoders (VAEs) is employed to describe the observed sentences and the corresponding latent semantic representations. Neural variational inference is used for the posterior inference of the latent variables. For salience estimation, we propose an unsupervised data reconstruction framework, which jointly considers the reconstruction for latent semantic space and observed term vector space. Therefore, we can capture the salience of sentences from these two different and complementary vector spaces. Thereafter, the VAEs-based latent semantic model is integrated into the sentence salience estimation component in a unified fashion, and the whole framework can be trained jointly by back-propagation via multi-task learning. Experimental results on the benchmark datasets DUC and TAC show that our framework achieves better performance than the state-of-the-art models.
Coordinating donors on crowdfunding websites

Crowdfunding websites like Kickstarter, Spot.Us and Donor's Choose seek to fund multiple projects simultaneously by soliciting donations from a large number of donors. Crowdfunding site designers must decide what to do with donations to projects that don't reach their goal by the deadline. Some crowdfunding sites use an all-or-nothing return rule in which donations are returned to donors if a project doesn't meet its goal. Other sites use a direct donation structure where all donations are kept by the project even if the total is insufficient. We simulated a crowdfunding site using a threshold public goods game in which a set of donors tries to fund multiple projects that vary in riskiness. We find that the return rule mechanism leads to a marginal improvement in productivity of a site -- more money is donated in total -- by eliciting more donations. However, the return rule also leads to a potential loss in efficiency (percentage of projects funded) because donations become spread across too many projects and are not coordinated to achieve the maximum possible impact. The direct donation model, though, encourages donors to coordinate to creates a more efficient but slightly less productive marketplace.
Simple Large-scale Relation Extraction from Unstructured Text

Knowledge-based question answering relies on the availability of facts, the majority of which cannot be found in structured sources (e.g. Wikipedia info-boxes, Wikidata). One of the major components of extracting facts from unstructured text is Relation Extraction (RE). In this paper we propose a novel method for creating distant (weak) supervision labels for training a large-scale RE system. We also provide new evidence about the effectiveness of neural network approaches by decoupling the model architecture from the feature design of a state-of-the-art neural network system. Surprisingly, a much simpler classifier trained on similar features performs on par with the highly complex neural network system (at 75x reduction to the training time), suggesting that the features are a bigger contributor to the final performance.
An Analysis of Factors Affecting on Online Shopping Behavior of Consumers

The purpose of this study is to analyze factors affecting on online shopping behavior of consumers that might be one of the most important issues of e-commerce and marketing field. However, there is very limited knowledge about online consumer behavior because it is a complicated socio-technical phenomenon and involves too many factors. One of the objectives of this study is covering the shortcomings of previous studies that didn't examine main factors that influence on online shopping behavior. This goal has been followed by using a model examining the impact of perceived risks, infrastructural variables and return policy on attitude toward online shopping behavior and subjective norms, perceived behavioral control, domain specific innovativeness and attitude on online shopping behavior as the hypotheses of study. To investigate these hypotheses 200 questionnaires dispersed among online stores of Iran. Respondents to the questionnaire were consumers of online stores in Iran which randomly selected. Finally regression analysis was used on data in order to test hypothesizes of study. This study can be considered as an applied research from purpose perspective and descriptive-survey with regard to the nature and method (type of correlation). The study identified that financial risks and non-delivery risk negatively affected attitude toward online shopping. Results also indicated that domain specific innovativeness and subjective norms positively affect online shopping behavior. Furthermore, attitude toward online shopping positively affected online shopping behavior of consumers.
Automatic identification of cited text spans: a multi-classifier approach over imbalanced dataset

Recently, a new form of structured summary on scientific papers is explored by grouping cited text spans from the reference paper. Its primary goal is to generate summaries based on the cited paper itself. Previously, traditional scientific summarization focused on citation-based methods by aggregating all citances that cite one unique paper without doing content-based citation analysis, while sometimes citations might differ between researchers or time slots. By investigating original text spans where scholars cited, the new method can reflect exact contributions of reference papers more. Therefore, how to identify cited text spans accurately becomes the first important problem to solve. Generally, it can be converted into finding the sentences in reference paper that is more similar with citation sentences. Taking it as a classification task, we investigate the potential of four actions to improve identification performance. Firstly, feature selections are conducted carefully according to multi-classifiers. Secondly, we apply sampling-based algorithms to preprocess class-imbalanced datasets. Since we integrated results via a weighted voting system, the third action is tuning parameters like, voting weights for multi-classifiers integration or running settings to see if we can improve performance further. Evaluation results show effectiveness of each action and demonstrate that researchers can take these actions for more accurate cited text spans identification when doing scientific summarization.
Master Artificial Intelligence Learning Latent Customer Representations for Credit Card Fraud Detection January 5 , 2018

A growing amount of consumers are making purchases online. Due to this rise in online retail, online credit card fraud is increasingly becoming a common type of theft. Previously used rule based systems are no longer scalable, because fraudsters can adapt their strategies over time. The advantage of using machine learning is that it does not require an expert to design rules which need to be updated periodically. Furthermore, algorithms can adapt to new fraudulent behaviour by retraining on newer transactions. Nevertheless, fraud detection by means of data mining and machine learning comes with a few challenges as well. The very unbalanced nature of the data and the fact that most payment processing companies only process a fragment of the incoming traffic from merchants, makes it hard to detect reliable patterns. Previously done research has focussed mainly on augmenting the data with useful features in order to improve the detectable patterns. These papers have proven that focussing on customer transaction behavior provides the necessary patterns in order to detect fraudulent behavior. In this thesis we propose several bayesian network models which rely on latent representations of fraudulent transactions, non-fraudulent transactions and customers. These representations are learned using unsupervised learning techniques. We show that the methods proposed in this thesis significantly outperform state-of-the-art models without using elaborate feature engineering strategies. A portion of this thesis focuses on re-implementing two of these feature engineering strategies in order to support this claim. Results from these experiments show that modeling fraudulent and non-fraudulent transactions individually generates the best performance in terms of classification accuracy. In addition, we focus on varying the dimensions of the latent space in order to assess its effect on performance. Our final results show that a higher dimensional latent space does not necessarily improve the performance of our models.
USING LEGOS TO INTEREST HIGH SCHOOL STUDENTS AND IMPROVE K 12 STEM EDUCATION

1 Lawrence E. Whitman, Wichita State University, Industrial & Manufacturing Engineering Department, 120G Engineering Building, Wichita, KS 672600035 larry.whitman@wichita.edu 2 Tonya A. Witherspoon, Wichita State University, College of Education, 156C Corbin Education Center, Wichita, KS 67260-0131 tonya.witherspoon@wichita.edu Abstract Wichita State University is actively using LEGOs to encourage science math engineering and technology (SMET). There are two major thrusts in our efforts. The college of engineering uses LEGO blocks to simulate a factory environment in the building of LEGO airplanes. This participative demonstration has been used at middle school, high school, and college classes. LEGOs are used to present four manufacturing scenarios of traditional, cellular, pull, and single piece flow manufacturing. The demonstration presents to students how the design of a factory has significant impact on the success of the company. It also encourages students to pursue engineering careers. The college of education uses robotics as a vehicle to integrate technology and engineering into math and science preservice and inservice teacher education.. The purpose is to develop technologically astute and competent teachers who are capable of integrating technology into their curriculum to improve the teaching and learning of their students. This paper will discuss each effort, the collaboration between the two, and provide examples of success.
Statistical Properties of Tonal Harmony in Bach ’ s Chorales

This study aims to contribute empirical computational results to the understanding of tonality and harmonic structure. It analyses aspects of tonal harmony and harmonic patterns based on a statistical, computational corpus analysis of Bach’s chorales. This is carried out using a novel heuristic method of segmentation developed specifically for that purpose. Analyses of distributions of single pc sets, chord classes and pc set transitions reveal very different structural patterns in both modes, many, but not all of which accord with standard music theory. In addition, most frequent chord transitions are found to exhibit a large degree of asymmetry, or, directedness, in way that for two pc sets A,B the transition frequencies f(A→B) and f(B→A) may differ to a large extent. Distributions of unigrams and bigrams are found to follow a Zipf distribution, i.e. decay in frequency roughly according to 1/x which implies that the majority of the musical structure is governed by a few frequent elements. The findings provide evidence for an underlying harmonic syntax which results in distinct statistical patterns. A subsequent hierarchical cluster analysis of pc sets based on respective antecedent and consequent patterns finds that this information suffices to group chords into meaningful functional groups solely on intrinsic statistical grounds without regard to pitch
Multilevel Inverter Topology Survey Master of Science Thesis in Electric Power Engineering

Multilevel inverters have become more popular over the years in electric high power application with the promise of less disturbances and the possibility to function at lower switching frequencies than ordinary two-level inverters. This report presents information about several multilevel inverter topologies, such as the Neutral-Point Clamped Inverter and the Cascaded Multicell Inverter. These multilevel inverters will also be compared with two-level inverters in simulations to investigate the advantages of using multilevel inverters. Modulation strategies, component comparison and solutions to the multilevel voltage source balancing problem will also be presented in this work. It is shown that multilevel inverters only produce 22% and 32% voltage THD while the two-level inverter for the same 1kHz test produces 115% voltage THD. For another simulation, while using lower switching frequency, it is shown that when the two-level inverter generates 25.1W switching losses, the tested multilevel inverters only produce 2.1W and 2.2W switching losses.
High Frequency Design RF POWER AMPLIFIERS RF and Microwave Power Amplifier and Transmitter Technologies — Part 4 By

RF Feedback The basis of this technique is similar to its audio-frequency counterpart. A portion of the RF-output signal from the amplifier is fed back to, and subtracted from, the RF-input signal without detection or downconversion. Considerable care must be taken when using feedback at RF as the delays involved must be small to ensure stability. In addition, the loss of gain at RF is generally a more significant sacrifice than it is at audio frequencies. For these reasons, the use of RF feedback in discrete circuits is usually restricted to HF and lower VHF frequencies [99]. It can be applied within MMIC devices, however, well into the microwave region. In an active RF feedback system, the voltage divider of a conventional passive-feedback system is replaced by an active (amplifier) stage. The gain in the feedback path reduces the power dissipated in the feedback components. While such systems demonstrate IMD reduction [105], they tend to work best at a specific signal level.