nils-herrmann/clean_scirepeval huggingface dataset:
LVCSR rescoring with modified loss functions: a decision theoretic perspective

The problem of speech decoding is considered here in a Decision Theoretic framework and a modified speech decoding procedure to minimize the expected risk under a general loss function is formulated. A specific word error rate loss function is considered and an implementation in an N-best list rescoring procedure is presented. Methods for estimation of the parameters of the resulting decision rules are provided for both supervised and unsupervised training. Preliminary experiments on an LVCSR task show small but statistically significant error rate improvements.
Learning Invariant Representations of Molecules for Atomization Energy Prediction

The accurate prediction of molecular energetics in chemical compound space is a crucial ingredient for rational compound design. The inherently graph-like, non-vectorial nature of molecular data gives rise to a unique and difficult machine learning problem. In this paper, we adopt a learning-from-scratch approach where quantum-mechanical molecular energies are predicted directly from the raw molecular geometry. The study suggests a benefit from setting flexible priors and enforcing invariance stochastically rather than structurally. Our results improve the state-of-the-art by a factor of almost three, bringing statistical methods one step closer to chemical accuracy.
Vocabulary acquisition from extensive reading : A case study

A number of studies have shown that second language learners acquire vocabulary through reading, but only relatively small amounts. However, most of these studies used only short texts, measured only the acquisition of meaning, and did not credit partial learning of words. This case study of a learner of French explores whether an extensive reading program can enhance lexical knowledge. The study assessed a relatively large number of words (133), and examined whether one month of extensive reading enhanced knowledge of these target words' spelling, meaning, and grammatical characteristics. The measurement procedure was a one-on-one interview that allowed a very good indication of whether learning occurred. The study also explores how vocabulary acquisition varies according to how often words are encountered in the texts. The results showed that knowledge of 65% of the target words was enhanced in some way, for a pickup rate of about 1 of every 1.5 words tested. Spelling was strongly enhanced, even from a small number of exposures. Meaning and grammatical knowledge were also enhanced, but not to the same extent. Overall, the study indicates that more vocabulary acquisition is possible from extensive reading than previous studies have suggested.
Description of interest regions with local binary patterns

This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT.
Performance of Image and Video Processing with General-Purpose Processors and Media ISA Extensions

This paper aims to provide a quantitative understanding of the performance of image and video processing applications on general-purpose processors, without and with media ISA extensions. We use detailed simulation of 12 benchmarks to study the effectiveness of current architectural features and identify future challenges for these workloads.Our results show that conventional techniques in current processors to enhance instruction-level parallelism (ILP) provide a factor of 2.3X to 4.2X performance improvement. The Sun VIS media ISA extensions provide an additional 1.1X to 4.2X performance improvement. The ILP features and media ISA extensions significantly reduce the CPU component of execution time, making 5 of the image processing benchmarks memory-bound.The memory behavior of our benchmarks is characterized by large working sets and streaming data accesses. Increasing the cache size has no impact on 8 of the benchmarks. The remaining benchmarks require relatively large cache sizes (dependent on the display sizes) to exploit data reuse, but derive less than 1.2X performance benefits with the larger caches. Software prefetching provides 1.4X to 2.5X performance improvement in the image processing benchmarks where memory is a significant problem. With the addition of software prefetching, all our benchmarks revert to being compute-bound.
Deep Neural Network Embeddings for Text-Independent Speaker Verification

This paper investigates replacing i-vectors for text-independent speaker verification with embeddings extracted from a feedforward deep neural network. Long-term speaker characteristics are captured in the network by a temporal pooling layer that aggregates over the input speech. This enables the network to be trained to discriminate between speakers from variablelength speech segments. After training, utterances are mapped directly to fixed-dimensional speaker embeddings and pairs of embeddings are scored using a PLDA-based backend. We compare performance with a traditional i-vector baseline on NIST SRE 2010 and 2016. We find that the embeddings outperform i-vectors for short speech segments and are competitive on long duration test conditions. Moreover, the two representations are complementary, and their fusion improves on the baseline at all operating points. Similar systems have recently shown promising results when trained on very large proprietary datasets, but to the best of our knowledge, these are the best results reported for speaker-discriminative neural networks when trained and tested on publicly available corpora.
Scale-Aware Fast R-CNN for Pedestrian Detection

In this paper, we consider the problem of pedestrian detection in natural scenes. Intuitively, instances of pedestrians with different spatial scales may exhibit dramatically different features. Thus, large variance in instance scales, which results in undesirable large intracategory variance in features, may severely hurt the performance of modern object instance detection methods. We argue that this issue can be substantially alleviated by the divide-and-conquer philosophy. Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework. The model introduces multiple built-in subnetworks which detect pedestrians with scales from disjoint ranges. Outputs from all of the subnetworks are then adaptively combined to generate the final detection results that are shown to be robust to large variance in instance scales, via a gate function defined over the sizes of object proposals. Extensive evaluations on several challenging pedestrian detection datasets well demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our method achieves state-of-the-art performance on Caltech [P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,”  Apr. 2012], and obtains competitive results on INRIA [N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc.  2005, pp. 886–893], ETH [A. Ess, B. Leibe, and L. V. Gool, “Depth and appearance for mobile scene analysis,” in Proc.  2007, pp. 1–8], and KITTI [A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in Proc.  2012, ].
Role of social media in tackling challenges in mental health

Mental illness is a serious and widespread health challenge in our society today. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. This position paper highlights some recent attempts examining the potential for leveraging social media postings as a new type of lens in understanding mental illness in individuals and populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements of behavior over time while radically expanding population sample sizes. We conclude highlighting how this research direction may be useful in developing tools for identifying the onset of depressive disorders in individuals, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from mental illness to be more proactive about their mental health.
Characterizing large-scale use of a direct manipulation application in the wild

Examining large-scale, long-term application use is critical to understanding how an application meets the needs of its user community. However, there have been few published analyses of long-term use of desktop applications, and none that have examined applications that support creating and modifying content using direct manipulation. In this paper, we present an analysis of 2 years of usage data from an instrumented version of the GNU Image Manipulation Program, including data from over 200 users. In the course of our analysis, we show that previous findings concerning the sparseness of command use and idiosyncrasy of users’ command vocabularies extend to a new domain and interaction style. These findings motivate continued research in adaptive and mixed-initiative interfaces. We also describe the novel application of a clustering technique to characterize a user community’s higher-level tasks from low-level logging data.
Summarization evaluation using relative utility

We present a series of experiments to demonstrate the validity of Relative Utility (RU) as a measure for evaluating extractive summarizers. RU is applicable in both single-document and multi-document summarization, is extendable to arbitrary compression rates with no extra annotation effort, and takes into account both random system performance and interjudge agreement. Our results using the JHU summary corpus indicate that RU is a reasonable and often superior alternative to several common evaluation metrics.
High power pulse generator based on avalanche transistor Marx circuit

Marx bank pulse circuit based on avalanche transistors can generate high amplitude, high-repetition-rate and low-jitter ultra-wideband pulses. The rising edge of the pulse is determined mainly by the performance of avalanche transistors. The amplitude of the pulse can be adjusted by changing the number of stage and the value of charging capacitors. In this paper, A 25 stages Marx bank pulse circuit is designed and tested, In order to reduce the jitter of the output pulses, an optimized driver circuit is designed and the charging resistors and capacitors are tuned to achieve a higher amplitude. Finally, 2.7 kV peak voltage of the output pulse is obtained, the FWHM is 1 ns, the maximum repetition rate is 10 kHz.
Augmented Reality versus Virtual Reality for 3D Object Manipulation

Virtual Reality (VR) Head-Mounted Displays (HMDs) are on the verge of becoming commodity hardware available to the average user and feasible to use as a tool for 3D work. Some HMDs include front-facing cameras, enabling Augmented Reality (AR) functionality. Apart from avoiding collisions with the environment, interaction with virtual objects may also be affected by seeing the real environment. However, whether these effects are positive or negative has not yet been studied extensively. For most tasks it is unknown whether AR has any advantage over VR. In this work we present the results of a user study in which we compared user performance measured in task completion time on a 9 degrees of freedom object selection and transformation task performed either in AR or VR, both with a 3D input device and a mouse. Our results show faster task completion time in AR over VR. When using a 3D input device, a purely VR environment increased task completion time by 22.5 percent on average compared to AR (<inline-formula> <tex-math notation="LaTeX">${p}<0.024$</tex-math><alternatives> <inline-graphic xlink:href="krichenbauer-ieq1-2658570.gif"/></alternatives></inline-formula>). Surprisingly, a similar effect occurred when using a mouse: users were about 17.3 percent slower in VR than in AR (<inline-formula> <tex-math notation="LaTeX">${p}<0.04$</tex-math><alternatives> <inline-graphic xlink:href="krichenbauer-ieq2-2658570.gif"/></alternatives></inline-formula>). Mouse and 3D input device produced similar task completion times in each condition (AR or VR) respectively. We further found no differences in reported comfort.
Detecting Important Life Events on Twitter Using Frequent Semantic and Syntactic Subgraphs

Identifying global events from social media has been the focus of much research in recent years. However, the identification of personal life events poses new requirements and challenges that have received relatively little research attention. In this paper we explore a new approach for life event identification, where we expand social media posts into both semantic, and syntactic networks of content. Frequent graph patterns are mined from these networks and used as features to enrich life-event classifiers. Results show that our approach significantly outperforms the best performing baseline in accuracy (by 4.48% points) and F-measure (by 4.54% points) when used to identify five major life events identified from the psychology literature: Getting Married, Having Children, Death of a Parent, Starting School, and Falling in Love. In addition, our results show that, while semantic graphs are effective at discriminating the theme of the post (e.g. the topic of marriage), syntactic graphs help identify whether the post describes a personal event (e.g. someone getting married).
Non-Intrusive Signature Extraction for Major Residential Loads

This paper presents a technique to extract load signatures non-intrusively by using the smart meter data. Load signature extraction is different from load activity identification. It is a new and important problem to solve for the applications of non-intrusive load monitoring (NILM). For a target appliance whose signatures are to be extracted, the proposed technique first selects the candidate events that are likely to be associated with the appliance by using generic signatures and an event filtration step. It then applies a clustering algorithm to identify the authentic events of this appliance. In the third step, the operation cycles of appliances are estimated using an association algorithm. Finally, the electric signatures are extracted from these operation cycles. The results can have various applications. One is to create signature databases for the NILM applications. Another is for load condition monitoring. Validation results based on the data collected from three actual houses and a laboratory experiment have shown that the proposed method is a promising solution to the problem of load signature collection.
Mutual Coupling Reduction Using F-Shaped Stubs in UWB-MIMO Antenna

A compact, high performance, and novel-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with low mutual coupling is presented in this paper. The proposed antenna consists of two radiating elements with shared ground plane having an area of <inline-formula> <tex-math notation="LaTeX">$50\times 30$ </tex-math></inline-formula> mm<sup>2</sup>. F-shaped stubs are introduced in the shared ground plane of the proposed antenna to produce high isolation between the MIMO antenna elements. The designed MIMO antenna has very low mutual coupling of (S<sub>21</sub> < −20 dB), low envelop correlation coefficient (ECC <0.04), high diversity gain (DG >7.4 dB), high multiplexing efficiency (<inline-formula> <tex-math notation="LaTeX">${\eta }_}> -3.5$ </tex-math></inline-formula>), and high peak gain over the entire UWB frequencies. The antenna performance is studied in terms of S-Parameters, radiation properties, peak gain, diversity gain, envelop correlation coefficient, and multiplexing efficiency. A good agreement between the simulated and measured results is observed.
Robust Physical-World Attacks on Machine Learning Models

Deep neural network-based classifiers are known to be vulnerable to adversarial examples that can fool them into misclassifying their input through the addition of small-magnitude perturbations. However, recent studies have demonstrated that such adversarial examples are not very effective in the physical world—they either completely fail to cause misclassification or only work in restricted cases where a relatively complex image is perturbed and printed on paper. In this paper we propose a new attack algorithm—Robust Physical Perturbations (RP2)— that generates perturbations by taking images under different conditions into account. Our algorithm can create spatiallyconstrained perturbations that mimic vandalism or art to reduce the likelihood of detection by a casual observer. We show that adversarial examples generated by RP2 achieve high success rates under various conditions for real road sign recognition by using an evaluation methodology that captures physical world conditions. We physically realized and evaluated two attacks, one that causes a Stop sign to be misclassified as a Speed Limit sign in 100% of the testing conditions, and one that causes a Right Turn sign to be misclassified as either a Stop or Added Lane sign in 100% of the testing conditions.
A driver fatigue detection method based on multi-sensor signals

Fatigue during long-time driving threatens the safety of drivers and transportation. In this paper, we provide an effective method based on multi-sensor signals collected from Kinect2.0 camera and PPG pulse sensor to build a driver fatigue detection system. Unlike most traditional works, we define the transitional process of fatigue and elaborate its effect on training classifiers. The simulation experiments are then designed and 15 groups of data are collected. Our method works in the following steps: 1) feature extraction and fusion, 2) sample labelling and 3) SVM classifier designing. The 10-fold cross-validation accuracy of the classifier is 90.10% and the test accuracy is 83.82%. Experimental results verify that our method to deal with samples in transitional process is universal and more accurate than traditional methods. Moreover, our method based on multi-sensor works better than those dealing with single-sensor.
Constructivism and Computer-Mediated Communication in Distance Education

The fields of learning theory and instructional design are in the midst of a scientific revolution in which their objectivist philosophical foundations are being replaced by a constructivist epistemology. This article describes the assumptions of a constructivist epistemology, contrasts them with objectivist assumptions, and then describes instructional systems that can support constructive learning at a distance Limitations of Distance Learning Technologies In an effort to supplement or replace live face-to-face instruction, technologicany mediated distance learning has more often than not merely replicated the ineffective methods that limit learning in face-to-face classrooms (Turoff 1995). Too often, potentially interactive technologies are used to present one-way lectures to students in remote locations. However, we believe that the most valuable activity in a classroom of any kind is the opportunity for students to work and interact together and to build and become part of a community of scholars and practitioners (Selfe and Eilola 1989; Bates 1990; Seaton 1993; Nalley 1995). A good learning experience is one in which a student can "master new knowledge and skills, criticany examine assumptions and beliefs, and engage in an invigorating, collaborative quest for wisdom and personal, holistic development" (Eastmond and Ziegahn 1995, 59). Technology used in distance education should facilitate these "good learning experiences" in an "extended classroom model" rather than broadcast teacher-centered lectures and demonstrations (Burge and Roberts 1993). A significant impediment to this goal is the fact that many teachers and instructional designers come to distance education from traditional backgrounds, bringing with them assumptions about teaching and learning that are not theory-based and do not translate well to technologicany mediated instruction (Schieman, Taere, and McLaren 1992).
Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition

Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain-computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity, low signal-to-noise ratio, and closely spaced frequency bands of interest, we investigate the effectiveness of recently introduced multivariate extensions of empirical mode decomposition (MEMD) in motor imagery BCI. We show that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation. Comparative analysis with other state of the art methods on both synthetic benchmark examples and a well established BCI motor imagery dataset support the analysis.
PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network

Music creation is typically composed of two parts: composing the musical score, and then performing the score with instruments to make sounds. While recent work has made much progress in automatic music generation in the symbolic domain, few attempts have been made to build an AI model that can render realistic music audio from musical scores. Directly synthesizing audio with sound sample libraries often leads to mechanical and deadpan results, since musical scores do not contain performance-level information, such as subtle changes in timing and dynamics. Moreover, while the task may sound like a text-to-speech synthesis problem, there are fundamental differences since music audio has rich polyphonic sounds. To build such an AI performer, we propose in this paper a deep convolutional model that learns in an end-toend manner the score-to-audio mapping between a symbolic representation of music called the pianorolls and an audio representation of music called the spectrograms. The model consists of two subnets: the ContourNet, which uses a U-Net structure to learn the correspondence between pianorolls and spectrograms and to give an initial result; and the TextureNet, which further uses a multi-band residual network to refine the result by adding the spectral texture of overtones and timbre. We train the model to generate music clips of the violin, cello, and flute, with a dataset of moderate size. We also present the result of a user study that shows our model achieves higher mean opinion score (MOS) in naturalness and emotional expressivity than a WaveNet-based model and two offthe-shelf synthesizers. We open our source code at https: //github.com/bwang514/PerformanceNet