nils-herrmann/clean_scirepeval huggingface dataset:
Decoding with Large-Scale Neural Language Models Improves Translation

We explore the application of neural language models to machine translation. We develop a new model that combines the neural probabilistic language model of Bengio et al., rectified linear units, and noise-contrastive estimation, and we incorporate it into a machine translation system both by reranking k-best lists and by direct integration into the decoder. Our large-scale, large-vocabulary experiments across four language pairs show that our neural language model improves translation quality by up to 1.1 Bleu.
A Linear MOS Transconductor Using Source Degeneration and Adaptive Biasing

This paper presents a new configuration for linear MOS voltage-to-current conversion (transconductance). The proposed circuit combines two previously reported linearization methods , . The topology achieves 60-dB linearity for a fully balanced input dynamic range up to 1 at a 3.3-V supply voltage, with slightly decreasing performance in the unbalanced case. The linearity is preserved during the tuning process for a moderate range of transconductance values. The approach is validated by both computer simulations and experiments.
Spectral Mapping Using Artificial Neural Networks for Voice Conversion

In this paper, we use artificial neural networks (ANNs) for voice conversion and exploit the mapping abilities of an ANN model to perform mapping of spectral features of a source speaker to that of a target speaker. A comparative study of voice conversion using an ANN model and the state-of-the-art Gaussian mixture model (GMM) is conducted. The results of voice conversion, evaluated using subjective and objective measures, confirm that an ANN-based VC system performs as good as that of a GMM-based VC system, and the quality of the transformed speech is intelligible and possesses the characteristics of a target speaker. In this paper, we also address the issue of dependency of voice conversion techniques on parallel data between the source and the target speakers. While there have been efforts to use nonparallel data and speaker adaptation techniques, it is important to investigate techniques which capture speaker-specific characteristics of a target speaker, and avoid any need for source speaker's data either for training or for adaptation. In this paper, we propose a voice conversion approach using an ANN model to capture speaker-specific characteristics of a target speaker and demonstrate that such a voice conversion approach can perform monolingual as well as cross-lingual voice conversion of an arbitrary source speaker.
Advantages of high frequency PWM in AC motor drive applications

There are many advantages of using high frequency PWM (in the range of 50 to 100 kHz) in motor drive applications. High motor efficiency, fast control response, lower motor torque ripple, close to ideal sinusoidal motor current waveform, smaller filter size, lower cost filter, etc. are a few of the advantages. However, higher frequency PWM is also associated with severe voltage reflection and motor insulation breakdown issues at the motor terminals. If standard Si IGBT based inverters are employed, losses in the switches make it difficult to overcome significant drop in efficiency of converting electrical power to mechanical power. Work on SiC and GaN based inverter has progressed and variable frequency drives (VFDs) can now be operated efficiently at carrier frequencies in the 50 to 200 kHz range, using these devices. Using soft magnetic material, the overall efficiency of filtering can be improved. The switching characteristics of SiC and GaN devices are such that even at high switching frequency, the turn on and turn off losses are minimal. Hence, there is not much penalty in increasing the carrier frequency of the VFD. Losses in AC motors due to PWM waveform are significantly reduced. All the above features put together improves system efficiency. This paper presents results obtained on using a 6-in-1 GaN module for VFD application, operating at a carrier frequency of 100 kHz with an output sine wave filter. Experimental results show the improvement in motor efficiency and system efficiency on using a GaN based VFD in comparison to the standard Si IGBT based VFD.
FooDD: Food Detection Dataset for Calorie Measurement Using Food Images

Food detection, classification, and analysis have been the topic of indepth studies for a variety of applications related to eating habits and dietary assessment. For the specific topic of calorie measurement of food portions with single and mixed food items, the research community needs a dataset of images for testing and training. In this paper we introduce FooDD: a Food Detection Dataset of 3000 images that offer variety of food photos taken from different cameras with different illuminations. We also provide examples of food detection using graph cut segmentation and deep learning algorithms.
Purine: A bi-graph based deep learning framework

Conv name: conv1 inputs: [ bottom, weight ] outputs: [ top ] location: ip: 127.0.0.1 device: 0 thread: 1 other fields ... Example Op defined in YAML Location: The location that the blob/op resides on, including: ● ip address of the target machine ● what device it is on (CPU/GPU) Thread: Thread is needed for op because both CPU and GPU can be multiple threaded (Streams in terms of NVIDIA GPU).
Texture-based fruit detection via images using the smooth patterns on the fruit

This paper describes a keypoint detection algorithm to accurately detect round fruits in high resolution imagery. The significant challenge associated with round fruits such as grapes and apples is that the surface is smooth and lacks definition and contrasting features, the contours of the fruit may be partially occluded, and the color of the fruit often blends with background foliage. We propose a fruit detection algorithm that utilizes the gradual variation of intensity and gradient orientation on the surface of the fruit. Candidate fruit locations, or “seed points” are tested for both monotonically decreasing intensity and gradient orientation profiles. Candidate fruit locations that pass the initial filter are classified using modified histogram of oriented gradients combined with a pairwise intensity comparison texture descriptor and random forest classifier. We analyse the performance of the fruit detection algorithm on image datasets of grapes and apples using human labeled images as ground truth. Our method to detect candidate fruit locations is scale invariant, robust to partial occlusions and more accurate than existing methods. We achieve overall F1 accuracy score of 0.82 for grapes and 0.80 for apples. We demonstrate our method is more accurate than existing methods.
Memory Safety for Low-Level Software/Hardware Interactions

Systems that enforce memory safety for today’s operating system kernels and other system software do not account for the behavior of low-level software/hardware interactions such as memory-mapped I/O, MMU configuration, and context switching. Bugs in such low-level interactions can lead to violations of the memory safety guarantees provided by a safe execution environment and can lead to exploitable vulnerabilities in system software . In this work, we present a set of program analysis and run-time instrumentation techniques that ensure that errors in these low-level operations do not violate the assumptions made by a safety checking system. Our design introduces a small set of abstractions and interfaces for manipulating processor state, kernel stacks, memory mapped I/O objects, MMU mappings, and self modifying code to achieve this goal, without moving resource allocation and management decisions out of the kernel. We have added these techniques to a compiler-based virtual machine called Secure Virtual Architecture (SVA), to which the standard Linux kernel has been ported previously. Our design changes to SVA required only an additional 100 lines of code to be changed in this kernel. Our experimental results show that our techniques prevent reported memory safety violations due to low-level Linux operations and that these violations are not prevented by SVA without our techniques . Moreover, the new techniques in this paper introduce very little overhead over and above the existing overheads of SVA. Taken together, these results indicate that it is clearly worthwhile to add these techniques to an existing memory safety system.
Conceptualizing e-Governance

In this paper, we aimed at conceptualizing the notion of e-governance, as there is a need to explore the new configurations and knowledge issues having emerged out of such pressures as globalization, economic competitiveness, the evolution of the State and the development of information and communication technologies (ICTs). In fact, we postulate that there is a constant and necessary readjustment to be made between the trend towards technological innovation and the need for regulation and institutional acknowledgement and transformation. This co-evolutionary process goes far beyond the mere extension of government action onto non -State actors, as e-governance is too often understood. We see this process rather as a collective problem-solving dynamics, involving several types of stakeholders, among which, often, the State and its diverse representatives, but not only and by far, have to operate in a multi-level and open-ended space ICTs, in that type of process, are multi-faceted, sometimes just tools for productivity or communication, but sometimes also an industrial sector of its own, that has to be supported and regulated, as part of a critical series of structural change factors impacting our society. In this evolution, administrative services have been thought of, too often, as the main objective of e-governance, but we see now the need for an enhanced capability framework in policy-making and regulation, involving a broader mindset when it comes to use and regulate ICTs.
Learning to bid in bridge

Bridge bidding is considered to be one of the most difficult problems for game-playing programs. It involves four agents rather than two, including a cooperative agent. In addition, the partial observability of the game makes it impossible to predict the outcome of each action. In this paper we present a new decision-making algorithm that is capable of overcoming these problems. The algorithm allows models to be used for both opponent agents and partners, while utilizing a novel model-based Monte Carlo sampling method to overcome the problem of hidden information. The paper also presents a learning framework that uses the above decision-making algorithm for co-training of partners. The agents refine their selection strategies during training and continuously exchange their refined strategies. The refinement is based on inductive learning applied to examples accumulated for classes of states with conflicting actions. The algorithm was empirically evaluated on a set of bridge deals. The pair of agents that co-trained significantly improved their bidding performance to a level surpassing that of the current state-of-the-art bidding algorithm.
Assembling the sculptures of the Parthenon

Although the Parthenon has stood on the Athenian Acropolis for nearly 2,500 years, its sculptural decorations have been scattered to museums around the world. Many of its sculptures have been damaged or lost. Fortunately, most of the decoration survives through drawings, descriptions, and casts. A component of our Parthenon Project has been to assemble digital models of the sculptures and virtually reunite them with the Parthenon. This sketch details our effort to digitally record the Parthenon sculpture collection in the Basel Skulpturhalle museum, which exhibits plaster casts of almost all of the existing pediments, metopes, and frieze. Our techniques have been designed to work as quickly as possible and at low cost.
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads

In processing human produced text using natural language processing (NLP) techniques, two fundamental subtasks that arise are (i) segmentation of the plain text into meaningful subunits (e.g., entities), and (ii) dependency parsing, to establish relations between subunits. Such structural interpretation of text provides essential building blocks for upstream expert system tasks: e.g., from interpreting textual real estate ads, one may want to provide an accurate price estimate and/or provide selection filters for end users looking for a particular property — which all could rely on knowing the types and number of rooms, etc. In this paper we develop a relatively simple and effective neural joint model that performs both segmentation and dependency parsing together, instead of one after the other as in most state-of-the-art works. We will focus in particular on the real estate ad setting, aiming to convert an ad to a structured description, which we name property tree, comprising the tasks of (1) identifying important entities of a property (e.g., rooms) from classifieds and (2) structuring them into a tree format. In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation that would arise from the subtasks one after the other in a pipelined fashion, and (ii) exploiting the interactions between the subtasks. For this purpose, we perform an extensive comparative study of the pipeline methods and the new proposed ∗Corresponding author Email addresses: giannis.bekoulis@ugent.be (Giannis Bekoulis), johannes.deleu@ugent.be (Johannes Deleu), thomas.demeester@ugent.be (Thomas Demeester), chris.develder@ugent.be (Chris Develder) Preprint submitted to Expert Systems with Applications February 23, 2018 joint model, reporting an improvement of over three percentage points in the overall edge F1 score of the property tree. Also, we propose attention methods, to encourage our model to focus on salient tokens during the construction of the property tree. Thus we experimentally demonstrate the usefulness of attentive neural architectures for the proposed joint model, showcasing a further improvement of two percentage points in edge F1 score for our application. While the results demonstrated are for the particular real estate setting, the model is generic in nature, and thus could be equally applied to other expert system scenarios requiring the general tasks of both (i) detecting entities (segmentation) and (ii) establishing relations among them (dependency parsing).
Identifying At-Risk Employees: Modeling Psychosocial Precursors of Potential Insider Threats

In many insider crimes, managers and other coworkers observed that the offenders had exhibited signs of stress, disgruntlement, or other issues, but no alarms were raised. Barriers to using such psychosocial indicators include the inability to recognize the signs and the failure to record the behaviors so that they can be assessed. A psychosocial model was developed to assess an employee's behavior associated with an increased risk of insider abuse. The model is based on case studies and research literature on factors/correlates associated with precursor behavioral manifestations of individuals committing insider crimes. To test the model's agreement with human resources and management professionals, we conducted an experiment with positive results. If implemented in an operational setting, the model would be part of a set of management tools for employee assessment to identify employees who pose a greater insider threat.
Automatic retina exudates segmentation without a manually labelled training set

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step; therefore, they do not require labelled lesion training sets which are time consuming to create, difficult to obtain and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. We evaluate our algorithm with this dataset and compare our results with two recent exudate segmentation algorithms. In all of our tests, our algorithms perform better or comparable with an order of magnitude reduction in computational time.
Intent-aware long-term prediction of pedestrian motion

We present a method to predict long-term motion of pedestrians, modeling their behavior as jump-Markov processes with their goal a hidden variable. Assuming approximately rational behavior, and incorporating environmental constraints and biases, including time-varying ones imposed by traffic lights, we model intent as a policy in a Markov decision process framework. We infer pedestrian state using a Rao-Blackwellized filter, and intent by planning according to a stochastic policy, reflecting individual preferences in aiming at the same goal.
Recent advances in efficient computation of deep convolutional neural networks

Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems. At the same time, the computational complexity and resource consumption of these networks continue to increase. This poses a significant challenge to the deployment of such networks, especially in real-time applications or on resource-limited devices. Thus, network acceleration has become a hot topic within the deep learning community. As for hardware implementation of deep neural networks, a batch of accelerators based on a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) have been proposed in recent years. In this paper, we provide a comprehensive survey of recent advances in network acceleration, compression, and accelerator design from both algorithm and hardware points of view. Specifically, we provide a thorough analysis of each of the following topics: network pruning, low-rank approximation, network quantization, teacher–student networks, compact network design, and hardware accelerators. Finally, we introduce and discuss a few possible future directions.
A scored AUC Metric for Classifier Evaluation and Selection

The area under the ROC (Receiver Operating Characteristic) curve, or simply AUC, has been widely used to measure model performance for binary classification tasks. It can be estimated under parametric, semiparametric and nonparametric assumptions. The non-parametric estimate of the AUC, which is calculated from the ranks of predicted scores of instances, does not always sufficiently take advantage of the predicted scores. This problem is tackled in this paper. On the basis of the ranks and the original values of the predicted scores, we introduce a new metric, called a scored AUC or sAUC. Experimental results on 20 UCI data sets empirically demonstrate the validity of the new metric for classifier evaluation and selection.
Understanding the Elusive Black Box of Dynamic Capabilities

A major challenge for managers in turbulent environments is to make sound decisions quickly. Dynamic capabilities have been proposed as a means for addressing turbulent environments by helping managers extend, modify, and reconfigure existing operational capabilities into new ones that better match the environment. However, because dynamic capabilities have been viewed as an elusive black box, it is difficult for managers to make sound decisions in turbulent environments if they cannot effectively measure dynamic capabilities. Therefore, we first seek to propose a measurable model of dynamic capabilities by conceptualizing, operationalizing, and measuring dynamic capabilities. Specifically, drawing upon the dynamic capabilities literature, we identify a set of capabilities—sensing the environment, learning, coordinating, and integrating— that help reconfigure existing operational capabilities into new ones that better match the environment. Second, we propose a structural model where dynamic capabilities influence performance by reconfiguring existing operational capabilities in the context of new product development (NPD). Data from 180 NPD units support both the measurable model of dynamic capabilities and also the structural model by which dynamic capabilities influence performance in NPD by reconfiguring operational capabilities, particularly in higher levels of environmental turbulence. The study’s implications for managerial decision making in turbulent environments by capturing the elusive black box of dynamic capabilities are discussed.  Decision Making in Turbulent Environments, Dynamic Capabilities, Environmental Turbulence, New Product Development, and Operational Capabilities.
Music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data

Music21 is an object-oriented toolkit for analyzing, searching, and transforming music in symbolic (scorebased) forms. The modular approach of the project allows musicians and researchers to write simple scripts rapidly and reuse them in other projects. The toolkit aims to provide powerful software tools integrated with sophisticated musical knowledge to both musicians with little programming experience (especially musicologists) and to programmers with only modest music theory skills. This paper introduces the music21 system, demonstrating how to use it and the types of problems it is wellsuited toward advancing. We include numerous examples of its power and flexibility, including demonstrations of graphing data and generating annotated musical scores.
Rolling shutter motion deblurring

Although motion blur and rolling shutter deformations are closely coupled artifacts in images taken with CMOS image sensors, the two phenomena have so far mostly been treated separately, with deblurring algorithms being unable to handle rolling shutter wobble, and rolling shutter algorithms being incapable of dealing with motion blur. We propose an approach that delivers sharp and undistorted output given a single rolling shutter motion blurred image. The key to achieving this is a global modeling of the camera motion trajectory, which enables each scanline of the image to be deblurred with the corresponding motion segment. We show the results of the proposed framework through experiments on synthetic and real data.
Inverse Rendering of Faces with a 3D Morphable Model

In this paper, we present a complete framework to inverse render faces with a 3D Morphable Model (3DMM). By decomposing the image formation process into geometric and photometric parts, we are able to state the problem as a multilinear system which can be solved accurately and efficiently. As we treat each contribution as independent, the objective function is convex in the parameters and a global solution is guaranteed. We start by recovering 3D shape using a novel algorithm which incorporates generalization error of the model obtained from empirical measurements. We then describe two methods to recover facial texture, diffuse lighting, specular reflectance, and camera properties from a single image. The methods make increasingly weak assumptions and can be solved in a linear fashion. We evaluate our findings on a publicly available database, where we are able to outperform an existing state-of-the-art algorithm. We demonstrate the usability of the recovered parameters in a recognition experiment conducted on the CMU-PIE database.