nils-herrmann/clean_scirepeval huggingface dataset:
A Large-Scale Evaluation of U.S. Financial Institutions’ Standardized Privacy Notices

Financial institutions in the United States are required by the Gramm-Leach-Bliley Act to provide annual privacy notices. In 2009, eight federal agencies jointly released a model privacy form for these disclosures. While the use of this model privacy form is not required, it has been widely adopted. We automatically evaluated 6,191 U.S. financial institutions’ privacy notices posted on the World Wide Web. We found large variance in stated practices, even among institutions of the same type. While thousands of financial institutions share personal information without providing the opportunity for consumers to opt out, some institutions’ practices are more privacy protective. Regression analyses show that large institutions and those headquartered in the northeastern region share consumers’ personal information at higher rates than all other institutions. Furthermore, our analysis helped us uncover institutions that do not let consumers limit data sharing when legally required to do so, as well as institutions making self-contradictory statements. We discuss implications for privacy in the financial industry, issues with the design and use of the model privacy form on the World Wide Web, and future directions for standardized privacy notice.
ReVACNN: Real-Time Visual Analytics for Convolutional Neural Network

References D. Bruckner, J. Rosen, and E. R. Sparks. deepviz: Visualizing convolutional neural networks for image classification. 2014. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012. Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research,9(2579-2605):85, 2008. Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hods Lipson. Understanding neural networks through deep visualization.  2015. Matthew D Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In Computer vision–ECCV 2014, pages 818–833. Springer, 2014. Network visualization of ReVACNN
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables

The `1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix even under high-dimensional settings. However, it requires solving a difficult non-smooth log-determinant program with number of parameters scaling quadratically with the number of Gaussian variables. State-of-the-art methods thus do not scale to problems with more than 20, 000 variables. In this paper, we develop an algorithm BIGQUIC, which can solve 1 million dimensional `1regularized Gaussian MLE problems (which would thus have 1000 billion parameters) using a single machine, with bounded memory. In order to do so, we carefully exploit the underlying structure of the problem. Our innovations include a novel block-coordinate descent method with the blocks chosen via a clustering scheme to minimize repeated computations; and allowing for inexact computation of specific components. In spite of these modifications, we are able to theoretically analyze our procedure and show that BIGQUIC can achieve super-linear or even quadratic convergence rates.
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the behaviours developed by agents in simulation are often specific to the characteristics of the simulator. Due to modeling error, strategies that are successful in simulation may not transfer to their real world counterparts. In this paper, we demonstrate a simple method to bridge this “reality gap”. By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained. This adaptivity enables the policies to generalize to the dynamics of the real world without any training on the physical system. Our approach is demonstrated on an object pushing task using a robotic arm. Despite being trained exclusively in simulation, our policies are able to maintain a similar level of performance when deployed on a real robot, reliably moving an object to a desired location from random initial configurations. We explore the impact of various design decisions and show that the resulting policies are robust to significant calibration error.
Aboutness: towards foundations for the Information Artifact Ontology

The Information Artifact Ontology (IAO) was created to serve as a domain‐neutral resource for the representation of types of information content entities (ICEs) such as documents, data‐bases, and digital im‐ ages. We identify a series of problems with the current version of the IAO and suggest solutions designed to advance our understanding of the relations between ICEs and associated cognitive representations in the minds of human subjects. This requires embedding IAO in a larger framework of ontologies, including most importantly the Mental Func‐ tioning Ontology (MFO). It also requires a careful treatment of the aboutness relations between ICEs and associated cognitive representa‐ tions and their targets in reality.
A vision based traffic accident detection method using extreme learning machine

Over the past years, automatic traffic accident detection (ATAD) based on video has become one of the most promising applications in intelligent transportation and is playing a more and more important role in ensuring travel safety. This paper proposes a classifier-based supervised method by viewing the last seconds before motor vehicle collisions as the detection target. In our method, we devise a novel algorithm called OF-SIFT as the low-level feature. Deriving from the optical flow and Scale Invariant Feature Transform (SIFT), it is designed to extract local motion information from the temporal domain rather than gradient-based local appearance from the spatial domain. The purpose of OF-SIFT is to generate a feature that can capture sufficient and distinctive dynamic motion information for motion detection without using the static state information of moving objects. Further, in order to develop a more compact image representation without considering the explicit vehicle geometry shape, we use the idea of Bag of Feature (BOF) model with spatial information to encode features. Finally, an extreme learning machine (ELM) classifier is introduced as the basic classifier owing to its excellent and fast generalization. Experiments using real-world data have shown that the proposed method has achieved good performance in handling ordinary video scenes.
Emotion recognition based on physiological changes in music listening

Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological data set to a feature-based multiclass classification. In order to collect a physiological data set from multiple subjects over many weeks, we used a musical induction method that spontaneously leads subjects to real emotional states, without any deliberate laboratory setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity, and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, and positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. An improved recognition accuracy of 95 percent and 70 percent for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.
Divided representation of concurrent goals in the human frontal lobes.

The anterior prefrontal cortex (APC) confers on humans the ability to simultaneously pursue several goals. How does the brain's motivational system, including the medial frontal cortex (MFC), drive the pursuit of concurrent goals? Using brain imaging, we observed that the left and right MFC, which jointly drive single-task performance according to expected rewards, divide under dual-task conditions: While the left MFC encodes the rewards driving one task, the right MFC concurrently encodes those driving the other task. The same dichotomy was observed in the lateral frontal cortex, whereas the APC combined the rewards driving both tasks. The two frontal lobes thus divide for representing simultaneously two concurrent goals coordinated by the APC. The human frontal function seems limited to driving the pursuit of two concurrent goals simultaneously.
Automatic Generation of Raven's Progressive Matrices

Raven’s Progressive Matrices (RPMs) are a popular family of general intelligence tests, and provide a non-verbal measure of a test subject’s reasoning abilities. Traditionally RPMs have been manually designed. To make them readily available for both practice and examination, we tackle the problem of automatically synthesizing RPMs. Our goal is to efficiently generate a large number of RPMs that are authentic (i.e. similar to manually written problems), interesting (i.e. diverse in terms of difficulty), and well-formed (i.e. unambiguous). The main technical challenges are: How to formalize RPMs to accommodate their seemingly enormous diversity, and how to define and enforce their validity? To this end, we (1) introduce an abstract representation of RPMs using first-order logic, and (2) restrict instantiations to only valid RPMs. We have realized our approach and evaluated its efficiency and effectiveness. We show that our system can generate hundreds of valid problems per second with varying levels of difficulty. More importantly, we show, via a user study with 24 participants, that the generated problems are statistically indistinguishable from actual problems. This work is an exciting instance of how logic and reasoning may aid general learning.
Modular multilevel converter for large-scale multistring photovoltaic energy conversion system

This paper presents a new grid connected photovoltaic energy conversion system configuration for large scale power plants. The grid-tied converter is based on a modular multilevel converter using voltage source H-bridge cells. The proposed converter is capable of concentrating a multimegawatt PV plant with distributed string MPPT tracking capability, high power quality and increased efficiency compared to the classic two-level voltage source converters. The main challenge is to handle the inherent power unbalances which may occur, not only between the different cells of one phase of the converter, but also between the three phases. The control strategy to deal with these unbalances is analyzed in this paper. Simulation results for a downsized 7 level MMC composed of 18 H-bridge cells and PV strings are presented to validate the proposed topology and control method.
Profanity use in online communities

As user-generated Web content increases, the amount of inappropriate and/or objectionable content also grows. Several scholarly communities are addressing how to detect and manage such content: research in computer vision focuses on detection of inappropriate images, natural language processing technology has advanced to recognize insults. However, profanity detection systems remain flawed. Current list-based profanity detection systems have two limitations. First, they are easy to circumvent and easily become stale - that is, they cannot adapt to misspellings, abbreviations, and the fast pace of profane slang evolution. Secondly, they offer a one-size fits all solution; they typically do not accommodate domain, community and context specific needs. However, social settings have their own normative behaviors - what is deemed acceptable in one community may not be in another. In this paper, through analysis of comments from a social news site, we provide evidence that current systems are performing poorly and evaluate the cases on which they fail. We then address community differences regarding creation/tolerance of profanity and suggest a shift to more contextually nuanced profanity detection systems.
Tensor Deep Stacking Networks

A novel deep architecture, the tensor deep stacking network (T-DSN), is presented. The T-DSN consists of multiple, stacked blocks, where each block contains a bilinear mapping from two hidden layers to the output layer, using a weight tensor to incorporate higher order statistics of the hidden binary (([0,1])) features. A learning algorithm for the T-DSN's weight matrices and tensors is developed and described in which the main parameter estimation burden is shifted to a convex subproblem with a closed-form solution. Using an efficient and scalable parallel implementation for CPU clusters, we train sets of T-DSNs in three popular tasks in increasing order of the data size: handwritten digit recognition using MNIST (60k), isolated state/phone classification and continuous phone recognition using TIMIT (1.1 m), and isolated phone classification using WSJ0 (5.2 m). Experimental results in all three tasks demonstrate the effectiveness of the T-DSN and the associated learning methods in a consistent manner. In particular, a sufficient depth of the T-DSN, a symmetry in the two hidden layers structure in each T-DSN block, our model parameter learning algorithm, and a softmax layer on top of T-DSN are shown to have all contributed to the low error rates observed in the experiments for all three tasks.
Why testing improves memory: mediator effectiveness hypothesis.

A wealth of research has established that practice tests improve memory for the tested material. Although the benefits of practice tests are well documented, the mechanisms underlying testing effects are not well understood. We propose the mediator effectiveness hypothesis, which states that more-effective mediators (that is, information linking cues to targets) are generated during practice involving tests with restudy versus during restudy only. Effective mediators must be retrievable at time of test and must elicit the target response. We evaluated these two components of mediator effectiveness for learning foreign language translations during practice involving either test-restudy or restudy only. Supporting the mediator effectiveness hypothesis, test-restudy practice resulted in mediators that were more likely to be retrieved and more likely to elicit targets on a final test.
Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems

Recent studies focus primarily on low energy consumption or execution time for task scheduling with precedence constraints in heterogeneous computing systems. In most cases, system reliability is more important than other performance metrics. In addition, energy consumption and system reliability are two conflicting objectives. A novel bi-objective genetic algorithm (BOGA) to pursue low energy consumption and high system reliability for workflow scheduling is presented in this paper. The proposed BOGA offers users more flexibility when jobs are submitted to a data center. On the basis of real-world and randomly generated application graphs, numerous experiments are conducted to evaluate the performance of the proposed algorithm. In comparison with excellent algorithms such as multi-objective heterogeneous earliest finish time (MOHEFT) and multi-objective differential evolution (MODE), BOGA performs significantly better in terms of finding the spread of compromise solutions.
Constructing large and fast multi-level cell STT-MRAM based cache for embedded processors

MLC STT-MRAM (Multi-level Cell Spin-Transfer Torque Magnetic RAM), an emerging non-volatile memory technology, has become a promising candidate to construct L2 caches for high-end embedded processors. However, the long write latency limits the effectiveness of MLC STT-MRAM based L2 caches. In this paper, we address this limitation with two novel designs: Line Pairing (LP) and Line Swapping (LS). LP forms fast cachelines by re-organizing MLC soft bits which are faster to write. LS dynamically stores frequently written data into these fast cachelines. Our experimental results show that LP and LS improve system performance by 15% and reduce energy consumption by 21%.
Avalanche instability in oxide charge balanced power MOSFETs

Power MOSFET designs have been moving to higher performance particularly in the medium voltage area. (60V to 300V) New designs require lower specific on-resistance (RSP) thus forcing designers to push the envelope of increasing the electric field stress on the shielding oxide, reducing the cell pitch, and increasing the epitaxial (epi) drift doping to reduce on resistance. In doing so, time dependant avalanche instabilities have become a concern for oxide charge balanced power MOSFETs. Avalanche instabilities can initiate in the active cell and/or the termination structures. These instabilities cause the avalanche breakdown to increase and/or decrease with increasing time in avalanche. They become a reliability risk when the drain to source breakdown voltage (BVdss) degrades below the operating voltage of the application circuit. This paper will explain a mechanism for these avalanche instabilities and propose an optimum design for the charge balance region. TCAD simulation was employed to give insight to the mechanism. Finally, measured data will be presented to substantiate the theory.
The Peril of Fragmentation: Security Hazards in Android Device Driver Customizations

Android phone manufacturers are under the perpetual pressure to move quickly on their new models, continuously customizing Android to fit their hardware. However, the security implications of this practice are less known, particularly when it comes to the changes made to Android's Linux device drivers, e.g., those for camera, GPS, NFC etc. In this paper, we report the first study aimed at a better understanding of the security risks in this customization process. Our study is based on ADDICTED, a new tool we built for automatically detecting some types of flaws in customized driver protection. Specifically, on a customized phone, ADDICTED performs dynamic analysis to correlate the operations on a security-sensitive device to its related Linux files, and then determines whether those files are under-protected on the Linux layer by comparing them with their counterparts on an official Android OS. In this way, we can detect a set of likely security flaws on the phone. Using the tool, we analyzed three popular phones from Samsung, identified their likely flaws and built end-to-end attacks that allow an unprivileged app to take pictures and screenshots, and even log the keys the user enters through touch screen. Some of those flaws are found to exist on over a hundred phone models and affect millions of users. We reported the flaws and helped the manufacturers fix those problems. We further studied the security settings of device files on 2423 factory images from major phone manufacturers, discovered over 1,000 vulnerable images and also gained insights about how they are distributed across different Android versions, carriers and countries.
State-of-the-Art in Visual Attention Modeling

Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.
American Economic Association Performance Pay and Productivity

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Multi-Domain Neural Machine Translation through Unsupervised Adaptation

We investigate the application of Neural Machine Translation (NMT) under the following three conditions posed by realworld application scenarios. First, we operate with an input stream of sentences coming from many different domains and with no predefined order. Second, the sentences are presented without domain information. Third, the input stream should be processed by a single generic NMT model. To tackle the weaknesses of current NMT technology in this unsupervised multi-domain setting, we explore an efficient instance-based adaptation method that, by exploiting the similarity between the training instances and each test sentence, dynamically sets the hyperparameters of the learning algorithm and updates the generic model on-the-fly. The results of our experiments with multi-domain data show that local adaptation outperforms not only the original generic NMT system, but also a strong phrase-based system and even single-domain NMT models specifically optimized on each domain and applicable only by violating two of our aforementioned assumptions.