nils-herrmann/clean_scirepeval huggingface dataset:
Practical swing-up controller design for a cart-type single inverted pendulum having a serial second

This paper proposes a swing-up control scheme for a cart-type single pendulum having a serial second pendulum as a parasitic part. The proposed control scheme swings up the first pendulum with controlling the motion of a cart and being robust to the parasitic part. In the swing-up, the second pendulum is swinging at the top of the first pendulum and the motion of the second pendulum works as strong disturbance to the control of the first pendulum. Hence the control scheme is required to be strongly robust to the disturbance, and this paper uses sliding mode control law as the controller, which is strongly robust to disturbance. After the swing-up, the controller also stabilizes both angles of the pendulums and cart position. Numerical simulations are given to show the effectiveness of the proposed scheme.
The end of corporate imperialism.

As they search for growth, multinational corporations will have no choice but to compete in the big emerging markets of China, India, Indonesia, and Brazil. But while it is still common to question how such corporations will change life in those markets, Western executives would be smart to turn the question around and ask how multinationals themselves will be transformed by these markets. To be successful, MNCs will have to rethink every element of their business models, the authors assert in this seminal HBR article from 1998. During the first wave of market entry in the 1980s, multinationals operated with what might be termed an imperialist mind-set, assuming that the emerging markets would merely be new markets for their old products. But this mind-set limited their success: What is truly big and emerging in countries like China and India is a new consumer base comprising hundreds of millions of people. To tap into this huge opportunity, MNCs need to ask themselves five basic questions: Who is in the emerging middle class in these countries? How do the distribution networks operate? What mix of local and global leadership do you need to foster business opportunities? Should you adopt a consistent strategy for all of your business units within one country? Should you take on local partners? The transformation that multinational corporations must undergo is not cosmetic--simply developing greater sensitivity to local cultures will not do the trick, the authors say. To compete in the big emerging markets, multinationals must reconfigure their resources, rethink their cost structures, redesign their product development processes, and challenge their assumptions about who their top-level managers should be.
Feature Selection and Weighting Methods in Sentiment Analysis

Sentiment analysis is the task of identifying whether the opinion expressed in a document is positive or negative about a given topic. Unfortunately, many of the potential applications of sentiment analysis are currently infeasible due to the huge number of features found in standard corpora. In this paper we systematically evaluate a range of feature selectors and feature weights with both Naı̈ve Bayes and Support Vector Machine classifiers. This includes the introduction of two new feature selection methods and three new feature weighting methods. Our results show that it is possible to maintain a state-of-the art classification accuracy of 87.15% while using less than 36% of the features.
An Intersectional Definition of Fairness

We introduce a measure of fairness for algorithms and data with regard to multiple protected attributes. Our proposed definition, differential fairness, is informed by the framework of intersectionality, which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. We show that our criterion behaves sensibly for any subset of the set of protected attributes, and we illustrate links to differential privacy. A case study on census data demonstrates the utility of our approach.
Artificial Intelligence in Precision Cardiovascular Medicine.

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.
Does "Fans Economy" Work for Chinese Pop Music Industry?

China has become one of the largest entertainment markets in the world in recent years. Due to the success of Xiaomi, many Chinese pop music industry entrepreneurs believe "Fans Economy" works in the pop music industry. "Fans Economy" is based on the assumption that pop music consumer market could be segmented based on artists. Each music artist has its own exclusive loyal fans. In this paper, we provide an insightful study of the pop music artists and fans social network. Particularly, we segment the pop music consumer market and pop music artists respectively. Our results show that due to the Matthew Effect and limited diversity of consumer market, "Fans Economy" does not work for the Chinese pop music industry.
Predictions of Default Probabilities in Structural Models of Debt

We examine the default probabilities predicted by “structural” models of risky corporate debt. Two types of models are examined: those with “exogenous” default boundaries, typified by Longstaff and Schwartz (1995); and those with “endogenous” default boundaries, typified by Leland and Toft (1996). We focus on default probabilities rather than credit spreads because (i) they are not affected by additional factors such as liquidity, tax differences, and recovery rates; and (ii) prediction of the relative likelihood of default is often stated as the objective of bond ratings. We examine the ability of these models to capture the actual average default frequencies across bonds with different ratings reported in Moody’s (2001) corporate bond default data, 1970-2000.
Enhancing small satellite communication through effective antenna system design

A Cube-Satellite (CubeSat) is a small satellite weighing no more than one kilogram. CubeSats are used for space research, but their low-rate communication capability limits functionality. As greater payload and instrumentation functions are sought, increased data rate is needed. Since most CubeSats currently transmit at a 437 MHz frequency, several directional antenna types were studied for a 2.45 GHz, larger bandwidth transmission. This higher frequency provides the bandwidth needed for increasing the data rate. A deployable antenna mechanism maybe needed because most directional antennas are bigger than the CubeSat size constraints. From the study, a deployable hemispherical helical antenna prototype was built. Transmission between two prototype antenna equipped transceivers at varying distances tested the helical performance. When comparing the prototype antenna's maximum transmission distance to the other commercial antennas, the prototype outperformed all commercial antennas, except the patch antenna. The root cause was due to the helical antenna's narrow beam width. Future work can be done in attaining a more accurate alignment with the satellite's directional antenna to downlink with a terrestrial ground station.
Smartphones as Practical and Secure Location Verification Tokens for Payments

We propose a novel location-based second-factor authentication solution for modern smartphones. We demonstrate our solution in the context of point of sale transactions and show how it can be effectively used for the detection of fraudulent transactions caused by card theft or counterfeiting. Our scheme makes use of Trusted Execution Environments (TEEs), such as ARM TrustZone, commonly available on modern smartphones, and resists strong attackers, even those capable of compromising the victim phone applications and OS. It does not require any changes in the user behavior at the point of sale or to the deployed terminals. In particular, we show that practical deployment of smartphone-based second-factor authentication requires a secure enrollment phase that binds the user to his smartphone TEE and allows convenient device migration. We then propose two novel enrollment schemes that resist targeted attacks and provide easy migration. We implement our solution within available platforms and show that it is indeed realizable, can be deployed with small software changes, and does not hinder user experience.
Word Representations: A Simple and General Method for Semi-Supervised Learning

If we take an existing supervised NLP system, a simple and general way to improve accuracy is to use unsupervised word representations as extra word features. We evaluate Brown clusters, Collobert and Weston (2008) embeddings, and HLBL (Mnih & Hinton, 2009) embeddings of words on both NER and chunking. We use near state-of-the-art supervised baselines, and find that each of the three word representations improves the accuracy of these baselines. We find further improvements by combining different word representations. You can download our word features, for off-the-shelf use in existing NLP systems, as well as our code, here: http://metaoptimize. com/projects/wordreprs/
(Non-)Random Sequences from (Non-)Random Permutations—Analysis of RC4 Stream Cipher

RC4 has been the most popular stream cipher in the history of symmetric key cryptography. Its internal state contains a permutation over all possible bytes from 0 to 255, and it attempts to generate a pseudo-random sequence of bytes (called keystream) by extracting elements of this permutation. Over the last twenty years, numerous cryptanalytic results on RC4 stream cipher have been published, many of which are based on non-random (biased) events involving the secret key, the state variables, and the keystream of the cipher. Though biases based on the secret key are common in RC4 literature, none of the existing ones depends on the length of the secret key. In the first part of this paper, we investigate the effect of RC4 keylength on its keystream, and report significant biases involving the length of the secret key. In the process, we prove the two known empirical biases that were experimentally reported and used in recent attacks against WEP and WPA by Sepehrdad, Vaudenay and Vuagnoux in EUROCRYPT 2011. After our current work, there remains no bias in the literature of WEP and WPA attacks without a proof. In the second part of the paper, we present theoretical proofs of some significant initial-round empirical biases observed by Sepehrdad, Vaudenay and Vuagnoux in SAC 2010. In the third part, we present the derivation of the complete probability distribution of the first byte of RC4 keystream, a problem left open for a decade since the observation by Mironov in CRYPTO 2002. Further, the existence of positive biases towards zero for all the initial bytes 3 to 255 is proved and exploited towards a generalized broadcast attack on RC4. We also investigate for long-term non-randomness in the keystream, and prove a new long-term bias of RC4.
A Wireless Battery Charger for Mobile Device

In this research, an innovative design of a wireless battery charger for portable electronic devices is proposed. The wireless power transfer is implemented through the magnetic coupling between a power transmitter, which is connected to the grid, and a power receiver, which is integrated inside the load device. An innovative receiver architecture which heavily improves the power conversion efficiency is presented. A laboratory prototype of the proposed wireless battery charger has been realized and tested to evaluate system performances. over the entire range of operating conditions the receiver efficiency lies within the 96.5% to99.9% range.
Building Information Modelling ( BIM ) and Project Management : a Stakeholders Perspective

Construction projects are becoming much more complex and difficult to manage. As a response Information and Communication Technology (ICT) has been developing at a very fast pace. The major shift in ICT for the Architecture Engineering and Construction (AEC) sector is the spreading of Building Information Modeling (BIM) (Bryde, Broquetas, & Volm, 2013). BIM is a system and its main objective is the managing of the information and because of that it is also a project management matter. Our research work is originated from the understanding of the managerial relationship gap between the two realities of BIM and project management. In particular this paper aims at addressing this gap from a specific perspective: stakeholders. Stakeholder management is one of the most important project critical success factors, as project success highly depends on stakeholders' satisfaction. The goal is relevant for both practitioners and educators. The exploration is done mainly through the literature review, but it is also strongly supported by the collection of primary data. The second source regards direct interviews to a portfolio of stakeholders of the AEC industry, e.g. project managers, architects, BIM experts, software resellers, building developer owners, innovation managers. The findings regard the classification of the key stakeholders in BIM adoption and the contextual situation in the different European countries, with particular focus on the role of the Governments.
Neural Rating Regression with Abstractive Tips Generation for Recommendation

Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to "translate'' user and item latent representations into a concise sentence. Extensive experiments on benchmark datasets from different domains show that NRT achieves significant improvements over the state-of-the-art methods. Moreover, the generated tips can vividly predict the user experience and feelings.
Sylvester Normalizing Flows for Variational Inference

OMNIGLOT is a dataset containing 1,623 hand-written characters from 50 various alphabets. Each character is represented by about 20 images that makes the problem very challenging. The dataset is split into 24,345 training datapoints and 8,070 test images. We randomly pick 1,345 training examples for validation. During training we applied dynamic binarization of data similarly to dynamic MNIST.
The Semantic Web: The Roles of XML and RDF

representation techniques. T he World Wide Web is possible because a set of widely established standards guarantees interoperability at various levels. Until now, the Web has been designed for direct human processing, but the next-generation Web, which Tim Berners-Lee and others call the “Semantic Web,” aims at machine-processible information.1 The Semantic Web will enable intelligent services—such as information brokers, search agents, and information filters—which offer greater functionality and interoperability than current stand-alone services. The Semantic Web will only be possible once further levels of interoperability have been established. Standards must be defined not only for the syntactic form of documents, but also for their semantic content. Notable among recent W3C standardization efforts are XML/XML schema and RDF/RDF schema, which facilitate semantic interoperability. In this article, we explain the role of ontologies in the architecture of the Semantic Web. We then briefly summarize key elements of XML and RDF, showing why using XML as a tool for semantic interoperability will be ineffective in the long run. We argue that a further representation and inference layer is needed on top of the Web’s current layers, and to establish such a layer, we propose a general method for encoding ontology representation languages into RDF/RDF schema. We illustrate the extension method by applying it to Ontology Interchange Language (OIL), an ontology representation and inference
Fake News Detection Through Multi-Perspective Speaker Profiles

Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5% in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles.
Investment under Uncertainty, Debt and Taxes

We present a capital budgeting valuation framework that takes into account both personal and corporate taxation. This has implications even for all-equity-financed projects. It is also important when the firm or project is partially financed by debt, of course. The setting is a Miller equilibrium economy with differential taxation of debt and equity income that is generalized to allow cross-sectional variation in corporate tax rates. We show broad circumstances under which taxes do not affect the martingale operator (the martingale operator is the same before and after personal taxes, which we call “valuation neutrality”) and in which there are no tax-timing options. One implication of this is that the appropriate discount rate for riskless equity-financed flows (martingale expectations or certaintyequivalents) is an equity rate that differs from the riskless debt rate by a tax wedge. This tax wedge factor is the after-tax retention rate for the corporate tax rate that corresponds to tax neutrality in the Miller equilibrium. We then extend this result to the valuation of the interest tax shield when the firm has an exogenous debt policy, where the debt may or may not have default risk. Interest tax shields accrue at a net rate corresponding to the difference between the corporate tax rate that will be faced by the project and the Miller equilibrium tax rate. Depending on the financing system, interest tax shields can be incorporated by using a tax-adjusted discount rate or by implementing an APV-like approach with additive interest tax shields. We also analyze the effect of uncertainty and debt financing on the value of investment real options and on the exercise policy, including the effect of default risk. For low uncertainty, a rise in leverage reduces the time value of the real option and increases the probability of being exercised. This last effect on the exercise policy is completely offset when the firm is close to default (i.e, a high coupon). In this situation, more debt or more uncertainty reduces the probability of investing.
Residual Squeeze VGG16

Deep learning has given way to a new era of machine learning, apart from computer vision. Convolutional neural networks have been implemented in image classification, segmentation and object detection. Despite recent advancements, we are still in the very early stages and have yet to settle on best practices for network architecture in terms of deep design, small in size and a short training time. In this work, we propose a very deep neural network comprised of 16 Convolutional layers compressed with the Fire Module adapted from the SQUEEZENET model. We also call for the addition of residual connections to help suppress degradation. This model can be implemented on almost every neural network model with fully incorporated residual learning. This proposed model Residual-Squeeze-VGG16 (ResSquVGG16) trained on the large-scale MIT Places365-Standard scene dataset. In our tests, the model performed with accuracy similar to the pre-trained VGG16 model in Top-1 and Top-5 validation accuracy while also enjoying a 23.86% reduction in training time and an 88.4% reduction in size. In our tests, this model was trained from scratch.  Convolutional Neural Networks; VGG16; Residual learning; Squeeze Neural Networks; Residual-Squeeze-VGG16; Scene Classification; ResSquVGG16.
Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications

The current electric grid is an inefficient system that wastes significant amounts of the electricity it produces because there is a disconnect between the amount of energy consumers require and the amount of energy produced from generation sources. Power plants typically produce more power than necessary to ensure adequate power quality. By taking advantage of energy storage within the grid, many of these inefficiencies can be removed. When using battery energy storage systems (BESS) for grid storage, advanced modeling is required to accurately monitor and control the storage system. A battery management system (BMS) controls how the storage system will be used and a BMS that utilizes advanced physics-based models will offer for much more robust operation of the storage system. The paper outlines the current state of the art for modeling in BMS and the advanced models required to fully utilize BMS for both lithium-ion batteries and vanadium redox-flow batteries. In addition, system architecture and how it can be useful in monitoring and control is discussed. A pathway for advancing BMS to better utilize BESS for grid-scale applications is outlined.
Joint Event Extraction via Structured Prediction with Global Features

Traditional approaches to the task of ACE event extraction usually rely on sequential pipelines with multiple stages, which suffer from error propagation since event triggers and arguments are predicted in isolation by independent local classifiers. By contrast, we propose a joint framework based on structured prediction which extracts triggers and arguments together so that the local predictions can be mutually improved. In addition, we propose to incorporate global features which explicitly capture the dependencies of multiple triggers and arguments. Experimental results show that our joint approach with local features outperforms the pipelined baseline, and adding global features further improves the performance significantly. Our approach advances state-ofthe-art sentence-level event extraction, and even outperforms previous argument labeling methods which use external knowledge from other sentences and documents.