nils-herrmann/clean_scirepeval huggingface dataset:
HOW PERVASIVE IS CORPORATE FRAUD ?

We estimate what percentage of firms engage in fraud and the economic cost of fraud. Our estimates are based on detected frauds, and frauds that we infer are started but are not caught. To identify the ‘iceberg’ of undetected fraud we take advantage of an exogenous shock to the incentives for fraud detection: Arthur Andersen’s demise, which forces companies to change auditors. By assuming that the new auditor will clean house, and examining the change in fraud detection by new auditors, we infer that the probability of a company engaging in a fraud in any given year is 14.5%. We validate the magnitude of this estimate using alternative methods. We estimate that on average corporate fraud costs investors 22 percent of enterprise value in fraudcommitting firms and 3 percent of enterprise value across all firms. _____________________________ * We thank Patricia Dechow, Katherine Guthrie, Phil McCollough, Joseph P. Weber, Michael Weisbach as well as participants at the University of Illinois Symposium on Auditing Research, the European Finance Association, the University of Chicago, the Rotman School at the University of Toronto, Berkeley-Haas Accounting, Berkeley-Haas Finance, and Queens University. Alexander Dyck thanks the Connaught Fund of the University of Toronto, and Adair Morse and Luigi Zingales, the Center for Research on Security Prices, the Stigler Center, and the Initiative on Global Financial Markets at the University of Chicago for financial support. USC FBE FINANCE SEMINAR presented by Adair Morse FRIDAY, April 12, 2013 10:30 am – 12:00 pm,
Particle swarm optimization for hyper-parameter selection in deep neural networks

Deep neural networks (DNNs) have achieved unprecedented success in a wide array of tasks. However, the performance of these systems depends directly on their hyper-parameters which often must be selected by an expert. Optimizing the hyper-parameters remains a substantial obstacle in designing DNNs in practice. In this work, we propose to select them using particle swarm optimization (PSO). Such biologically-inspired approaches have not been extensively exploited for this task. We demonstrate that PSO efficiently explores the solution space, allowing DNNs of a minimal topology to obtain competitive classification performance over the MNIST dataset. We showed that very small DNNs optimized by PSO retrieve promising classification accuracy for CIFAR-10. Also, PSO improves the performance of existing architectures. Extensive experimental study, backed-up with the statistical tests, revealed that PSO is an effective technique for automating hyper-parameter selection and efficiently exploits computational resources.
Intellectual capital and performance in causal models Evidence from the information technology industry in Taiwan

This paper seeks to investigate the impact of intellectual capital elements on business performance, as well as the relationship among intellectual capital elements from a cause-effect perspective.  The partial least squares approach is used to examine the information technology (IT) industry in Taiwan.  Results show that intellectual capital elements directly affect business performance, with the exception of human capital. Human capital indirectly affects performance through the other three elements: innovation capital, process capital, and customer capital. There also exists a cause-effect relationship among four elements of intellectual capital. Human capital affects innovation capital and process capital. Innovation capital affects process capital, which in turn influences customer capital. Finally, customer capital contributes to performance. The cause-effect relationship between leading elements and lagged elements provides implications for the management of firms in the IT industry.  The model proposed in this study is applicable to the high-tech IT industry. Modification of the proposed model may be needed in applying this model to other industries.  This study helps management identify relevant intellectual capital elements and their indicators to enhance business performance.  This paper is a seminal work to propose an integrated cause-effect model to investigate the relationship among elements of intellectual capital for IT in Taiwan.
A Case Study on Learning a Unified Encoder of Relations

Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema. An individual corpus is often small, and the models may often be biased or overfitted to the corpus. We hypothesize that we can learn a better representation by combining multiple relation datasets. We attempt to use a shared encoder to learn the unified feature representation and to augment it with regularization by adversarial training. The additional corpora feeding the encoder can help to learn a better feature representation layer even though the relation schemas are different. We use ACE05 and ERE datasets as our case study for experiments. The multi-task model obtains significant improvement on both datasets.
A Meta-Analytic Test of Redundancy and Relative Importance of the Dark Triad and Five-Factor Model of Personality.

We examined the relationships between Machiavellianism, narcissism, and psychopathy-the three traits of the Dark Triad (DT)-and the Five-Factor Model (FFM) of personality. The review identified 310 independent samples drawn from 215 sources and yielded information pertaining to global trait relationships and facet-level relationships. We used meta-analysis to examine (a) the bivariate relations between the DT and the five global traits and 30 facets of the FFM, (b) the relative importance of each of the FFM global traits in predicting DT, and (c) the relationship between the DT and FFM facets identified in translational models of narcissism and psychopathy. These analyses identified consistent and theoretically meaningful associations between the DT traits and the facets of the FFM. The five traits of the FFM, in a relative importance analysis, accounted for much of the variance in Machiavellianism, narcissism, and psychopathy, respectively, and facet-level analyses identified specific facets of each FFM trait that were consistently associated with narcissism (e.g., angry/hostility, modesty) and psychopathy (e.g., straightforwardness, deliberation). The FFM explained nearly all of the variance in psychopathy (R(2) c  = .88) and a substantial portion of the variance in narcissism (R(2) c  = .42).
Data Sparseness in Linear SVM

Large sparse datasets are common in many realworld applications. Linear SVM has been shown to be very efficient for classifying such datasets. However, it is still unknown how data sparseness would affect its convergence behavior. To study this problem in a systematic manner, we propose a novel approach to generate large and sparse data from real-world datasets, using statistical inference and the data sampling process in the PAC framework. We first study the convergence behavior of linear SVM experimentally, and make several observations, useful for real-world applications. We then offer theoretical proofs for our observations by studying the Bayes risk and PAC bound. Our experiment and theoretic results are valuable for learning large sparse datasets with linear SVM.
A compact 2.45 GHz, low power wireless energy harvester with a reflector-backed folded dipole rectenna

This paper paper describes the design procedure as well as the experimental performance of a 2.45GHz 10 μW wireless energy harvester (WEH) with a maximum total efficiency of ≈ 30% at 1 μW/cm2 incident power density. The WEH integrates a shunt high-speed rectifying diode with a folded dipole. A metal reflector increases the gain of the rectenna and a quarter wavelength differential line is used as a choke. Both a VDI WVD and a Skyworks GaAs Schottky diode are integrated with the antenna and their performance is compared.
GeTrust: A Guarantee-Based Trust Model in Chord-Based P2P Networks

More and more users are attracted by P2P networks characterized by decentralization, autonomy and anonymity. However, users’ unconstrained behavior makes it necessary to use a trust model when establishing trust relationships between peers. Most existing trust models are based on recommendations, which, however, suffer from the shortcomings of slow convergence and high complexity of trust computations, as well as huge overhead of network traffic. Inspired by the establishment of trust relationships in human society, a guarantee-based trust model, GeTrust, is proposed for Chord-based P2P networks. A service peer needs to choose its guarantee peer(s) for the service it is going to provide, and they are both required to pledge reputation mortgages for the service. The request peer makes evaluations on all the candidates of service peer by referring their service reputations and their guarantee peers’ reputations, and selects the one with highest evaluation to be its service provider. In order to enhance GeTrust's availability and prevent malicious behavior, we also present incentive mechanism and anonymous reputation management strategy. Simulation results show that GeTrust is effective and efficient in terms of improving successful transaction rate, resisting complex attacks, reducing network overhead and lowering computational complexity.
Reactive oxygen species: metabolism, oxidative stress, and signal transduction.

Several reactive oxygen species (ROS) are continuously produced in plants as byproducts of aerobic metabolism. Depending on the nature of the ROS species, some are highly toxic and rapidly detoxified by various cellular enzymatic and nonenzymatic mechanisms. Whereas plants are surfeited with mechanisms to combat increased ROS levels during abiotic stress conditions, in other circumstances plants appear to purposefully generate ROS as signaling molecules to control various processes including pathogen defense, programmed cell death, and stomatal behavior. This review describes the mechanisms of ROS generation and removal in plants during development and under biotic and abiotic stress conditions. New insights into the complexity and roles that ROS play in plants have come from genetic analyses of ROS detoxifying and signaling mutants. Considering recent ROS-induced genome-wide expression analyses, the possible functions and mechanisms for ROS sensing and signaling in plants are compared with those in animals and yeast.
Scalable pseudo-random noise radar

This paper describes the implementation of a pseudo-random noise radar system on a scalable sensor platform that consists of several multi-purpose 61- and 122-GHz transceivers in a Silicon-Germanium BiCMOS technology. The multi-band transceivers are equipped with a frequency multiplier to generate the 61- and 122-GHz carrier signals from a single 30.5-GHz LO signal and can be cascaded to build a MIMO radar system with 2 different frequency bands. The implemented scalable sensor system is suitable for various radar and communication applications by using the same transceiver chips with integrated binary phase-shift keying modulators and I/Q receiver. A suitable pseudo-random binary sequence was used in a radar measurement to demonstrate the applicability of the developed chips.
Adversarial Meta-Learning

Meta-learning enables a model to learn from very limited data to undertake a new task. In this paper, we study the general meta-learning with adversarial samples. We present a meta-learning algorithm, ADML (ADversarial Meta-Learner), which leverages clean and adversarial samples to optimize the initialization of a learning model in an adversarial manner. ADML leads to the following desirable properties: 1) it turns out to be very effective even in the cases with only clean samples; 2) it is model-agnostic, i.e., it is compatible with any learning model that can be trained with gradient descent; and most importantly, 3) it is robust to adversarial samples, i.e., unlike other meta-learning methods, it only leads to a minor performance degradation when there are adversarial samples. We show via extensive experiments that ADML delivers the state-of-the-art performance on two widelyused image datasets, MiniImageNet and CIFAR100, in terms of both accuracy and robustness.
Speed/accuracy trade-offs in target-directed movements.

This target article presents a critical survey of the scientific literature dealing with the speed/accuracy trade-offs in rapid-aimed movements. It highlights the numerous mathematical and theoretical interpretations that have been proposed in recent decades. Although the variety of points of view reflects the richness of the field and the high degree of interest that such basic phenomena attract in the understanding of human movements, it calls into question the ability of 'many models to explain the basic observations consistently reported in the field. This target article summarizes the kinematic theory of rapid human movements, proposed recently by R. Plamondon (1993b; 1993c; 1995a; 1995b), and analyzes its predictions in the context of speed/accuracy trade-offs. Data from human movement literature are reanalyzed and reinterpreted in the context of the new theory. It is shown that the various aspects of speed/accuracy trade-offs can be taken into account by considering the asymptotic behavior of a large number of coupled linear systems, from which a delta-lognormal law can be derived to describe the velocity profile of an end-effector driven by a neuromuscular synergy. This law not only describes velocity profiles almost perfectly, it also predicts the kinematic properties of simple rapid movements and provides a consistent framework for the analysis of different types of speed/accuracy trade-offs using a quadratic (or power) law that emerges from the model.
Exploring the Relationship between Organizational Adoption Motives and the Tailoring of Agile Methods

Advocates of agile information systems development methods originally called for implementation of the method in full - either perform all of the method's practices, or don't call it “agile”. Over time this quest for orthodoxy was replaced by the pragmatic tailoring of agile methods to the organization's environment. However, little empirical research has investigated the forces that impact the manner in which agile methods are tailored. This article describes an exploratory study that investigates the relationships between the motives for adopting agile methods, and the agile practices adopted. Using the source data from the VersionOne State of Agile 2011 survey, we identified a sample of 2304 agile practitioners. Our study finds that three motives for agile adoption - a desire for increased software quality, increased efficiency, or increased effectiveness are each associated with different configurations of project management focused agile practices and agile practices related to the software development approach.
A novel broadband omnidirectional dual polarized MIMO antenna for 4G LTE applications

A low-profile broadband omnidirectional multiple-input and multiple-output (MIMO) antenna with dual polarization is presented, which provides polarization diversity for the 4G Long Term Evolution (LTE) communication systems, networks, and base stations. It consists of two orthogonally polarized radiating elements with separate feeds. The horizontally-polarized element is comprised of four pairs of flag-shaped dipoles, four parasitical strips, and a balun with tapered striplines for balance-unbalance transition. It has a fractional bandwidth of 55% covering from 1.7 GHz to 3.0 GHz with return loss greater than 10 dB and an overall size of 43×43×1.6 mm3. The vertically polarized element consists of a discone antenna, a round sleeve, and a top-loading ring shorted to the ground-plane for bandwidth enhancement. It has a fractional bandwidth of 137% covering from 671 MHz to 3580 MHz, and an overall volume of 170×170×56 mm3. The measurement results show both antenna elements exhibit smooth gain over the operational bandwidth. In addition, the geometrical shape and compactness of the proposed design, along with the omnidirectional radiation patterns, makes it ideal for ceiling or surface-mounted 4G LTE applications.
Classification using Hybrid SVM and KNN Approach

Phishing is a potential web threat that includes mimicking official websites to trick users by stealing their important information such as username and password related to financial systems. The attackers use social engineering techniques like email, SMS and malware to fraud the users. Due to the potential financial losses caused by phishing, it is essential to find effective approaches for phishing websites detection. This paper proposes a hybrid approach for classifying the websites as Phishing, Legitimate or Suspicious websites, the proposed approach intelligently combines the K-nearest neighbors (KNN) algorithm with the Support Vector Machine (SVM) algorithm in two stages. Firstly, the KNN was utilized as a robust to noisy data and effective classifier. Secondly, the SVM is employed as a powerful classifier. The proposed approach integrates the simplicity of KNN with the effectiveness of SVM. The experimental results show that the proposed hybrid approach achieved the highest accuracy of 90.04% when compared with other approaches. Information security; phishing websites; support vector machine; K-nearest neighbors
FASHION , FICTION , FUNCTION : MEDIATING WEARABLE DESIGN THROUGH FASHION FILM

Fashion technology is an expanding field, yet the question of how technology can be considered fashionable remains unexplored. According to fashion theories, mediation plays a fundamental role in transforming clothing items into fashionable garments. In this study, we explored how fashion films, as one of the most important fashion media in the industry today, could make wearable design concepts fashionable by merging aesthetics, experience and fiction. By synthesizing research in fashion studies and Human-Computer Interaction (HCI), we sketch out a framework for producing fashion film for wearables. We then describe our own process of making a fashion film for a fictional concept, and reflect on our process of using the framework. The contribution of the study includes: 1) proposing fashion films as means of mediating wearable design concepts; 2) advocating a balance between fashion, fiction and function in fashion technology mediation; and 3) foregrounding the discussion of design mediation as part of the design process broadly.
A Wearable Inertial Measurement System With Complementary Filter for Gait Analysis of Patients With Stroke or Parkinson’s Disease

This paper presents a wearable inertial measurement system and its associated spatiotemporal gait analysis algorithm to obtain quantitative measurements and explore clinical indicators from the spatiotemporal gait patterns for patients with stroke or Parkinson’s disease. The wearable system is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, and an RF wireless transmission module. The spatiotemporal gait analysis algorithm, consisting of procedures of inertial signal acquisition, signal preprocessing, gait phase detection, and ankle range of motion estimation, has been developed for extracting gait features from accelerations and angular velocities. In order to estimate accurate ankle range of motion, we have integrated accelerations and angular velocities into a complementary filter for reducing the accumulation of integration error of inertial signals. All 24 participants mounted the system on their foot to walk along a straight line of 10 m at normal speed and their walking recordings were collected to validate the effectiveness of the proposed system and algorithm. Experimental results show that the proposed inertial measurement system with the designed spatiotemporal gait analysis algorithm is a promising tool for automatically analyzing spatiotemporal gait information, serving as clinical indicators for monitoring therapeutic efficacy for diagnosis of stroke or Parkinson’s disease.
Design of hybrid resistive-capacitive DAC for SAR A/D converters

While hybrid capacitive-resistive D/A Converter (DAC) has been known for many years, its potential for energy-efficient operation is sometimes overlooked. This paper investigates the utilization of hybrid DACs in successive-approximation register A/D converters. To improve energy efficiency of SAR ADCs, a new hybrid DAC is introduced. In an exemplar 10-bit 100-MS/s ADC, simulation results show that the energy efficiency and chip area (of passive devices) can be improved by more than an order of magnitude.
Reinforcement learning of motor skills with policy gradients

Autonomous learning is one of the hallmarks of human and animal behavior, and understanding the principles of learning will be crucial in order to achieve true autonomy in advanced machines like humanoid robots. In this paper, we examine learning of complex motor skills with human-like limbs. While supervised learning can offer useful tools for bootstrapping behavior, e.g., by learning from demonstration, it is only reinforcement learning that offers a general approach to the final trial-and-error improvement that is needed by each individual acquiring a skill. Neither neurobiological nor machine learning studies have, so far, offered compelling results on how reinforcement learning can be scaled to the high-dimensional continuous state and action spaces of humans or humanoids. Here, we combine two recent research developments on learning motor control in order to achieve this scaling. First, we interpret the idea of modular motor control by means of motor primitives as a suitable way to generate parameterized control policies for reinforcement learning. Second, we combine motor primitives with the theory of stochastic policy gradient learning, which currently seems to be the only feasible framework for reinforcement learning for humanoids. We evaluate different policy gradient methods with a focus on their applicability to parameterized motor primitives. We compare these algorithms in the context of motor primitive learning, and show that our most modern algorithm, the Episodic Natural Actor-Critic outperforms previous algorithms by at least an order of magnitude. We demonstrate the efficiency of this reinforcement learning method in the application of learning to hit a baseball with an anthropomorphic robot arm.
COSY: An Energy-Efficient Hardware Architecture for Deep Convolutional Neural Networks Based on Systolic Array

Deep convolutional neural networks (CNNs) show extraordinary abilities in artificial intelligence applications, but their large scale of computation usually limits their uses on resource-constrained devices. For CNN’s acceleration, exploiting the data reuse of CNNs is an effective way to reduce bandwidth and energy consumption. Row-stationary (RS) dataflow of Eyeriss is one of the most energy-efficient state-of-the-art hardware architectures, but has redundant storage usage and data access, so the data reuse has not been fully exploited. It also requires complex control and is intrinsically unable to skip over zero-valued inputs in timing. In this paper, we present COSY (CNN on Systolic Array), an energy-efficient hardware architecture based on the systolic array for CNNs. COSY adopts the method of systolic array to achieve the storage sharing between processing elements (PEs) in RS dataflow at the RF level, which reduces low-level energy consumption and on-chip storage. Multiple COSY arrays sharing the same storage can execute multiple 2-D convolutions in parallel, further increasing the data reuse in the low-level storage and improving throughput. To compare the energy consumption of COSY and Eyeriss running actual CNN models, we build a process-based energy consumption evaluation system according to the hardware storage hierarchy. The result shows that COSY can achieve an over 15% reduction in energy consumption under the same constraints, improving the theoretical Energy-Delay Product (EDP) and Energy-Delay Squared Product (ED^2P) by 1.33 x on average. In addition, we prove that COSY has the intrinsic ability for zero-skipping, which can further increase the improvements to 2.25× and 3.83× respectively.