nils-herrmann/clean_scirepeval huggingface dataset:
MGAN: Training Generative Adversarial Nets with Multiple Generators

We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapsing problem and delivering state-of-the-art results. A minimax formulation was able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN. Generators create samples that are intended to come from the same distribution as the training data, whilst the discriminator determines whether samples are true data or generated by generators, and the classifier specifies which generator a sample comes from. The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model. We term our method Mixture Generative Adversarial Nets (MGAN). We develop theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon divergence (JSD) between the mixture of generators’ distributions and the empirical data distribution is minimal, whilst the JSD among generators’ distributions is maximal, hence effectively avoiding the mode collapsing problem. By utilizing parameter sharing, our proposed model adds minimal computational cost to the standard GAN, and thus can also efficiently scale to large-scale datasets. We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by the generators.
Semi-Latent GAN: Learning to generate and modify facial images from attributes

Generating and manipulating human facial images using high-level attributal controls are important and interesting problems. The models proposed in previous work can solve one of these two problems (generation or manipulation), but not both coherently. This paper proposes a novel model that learns how to both generate and modify the facial image from high-level semantic attributes. Our key idea is to formulate a Semi-Latent Facial Attribute Space (SL-FAS) to systematically learn relationship between user-defined and latent attributes, as well as between those attributes and RGB imagery. As part of this newly formulated space, we propose a new model – SL-GAN which is a specific form of Generative Adversarial Network. Finally, we present an iterative training algorithm for SL-GAN. The experiments on recent CelebA and CASIA-WebFace datasets validate the effectiveness of our proposed framework. We will also make data, pre-trained models and code available.
Automatically Constructing Compositional and Recursive Learners

We consider learning to generalize and extrapolate with limited data to harder compositional problems than a learner has previously seen. We take steps toward this challenge by presenting a characterization, algorithm, and implementation of a learner that programs itself automatically to reflect the structure of the problem it faces. Our key ideas are (1) transforming representations with modular units of computation is a solution for decomposing problems in a way that reflects their subproblem structure; (2) learning the structure of a computation can be formulated as a sequential decision-making problem. Experiments on solving various multilingual arithmetic problems demonstrate that our method generalizes out of distribution to unseen problem classes and extrapolates to harder versions of the same problem. Our paper provides the first element of a framework for learning general-purpose, compositional and recursive programs that design themselves.
Working Memory in the Acquisition of Complex Cognitive Skills

This paper reports three experiments using the secondary task methodology of working memory, in the task analysis of a complex computer game, ‘SPACE FORTRESS. Unlike traditional studies of working memory, the primary task relies on perceptual-motor skills and accurate timing of responses as well as shortand long-term strategic decisions. In experiment 1, highly trained game performance was affected by the requirement to generate concurrent, paced responses and by concurrent loads on working memory, but not by the requirement to produce a vocal or a tapping response to a secondary stimulus. In experiment 2, expert performance was substantially affected by secondary tasks which had high v&o-spatial or verbal cognitive processing loads, but was not contingent upon the nature (verbal or visuo-spatial) of the processing requirement. In experiment 3, subjects were tested on dual-task performance after only 3 hours practice on Space Fortress, and again after a further five hours practice on the game. Early in training, paced generation of responses had very little effect on game performance. Game performance was affected by general working memory load, but an analysis of component measures showed that a wider range and rather different aspects of performance were disrupted by a visuo-spatial memory load than were affected by a secondary verbal load. With further training this pattern changed such that the differential nature of the disruption by a secondary visuo-spatial task was much reduced. Also, paced generation of responses had a small effect on game performance. However the disruption was not as dramatic as that shown for expert players. Subjective ratings of task difficulty were poor predictors of performance in all of the three experiments. These results suggested that general working memory load was an important aspect of performance at all levels
Extended Kalman Filtering Applied to a Two-Axis Robotic Arm with Flexible Links

An industrial robot today uses measurements of its joint positions and models of its kinematics and dynamics to estimate and control its end-effector position. Substantially better end-effector position estimation and control performance would be obtainable if direct measurements of its end-effector position were also used. The subject of this paper is extended Kalman filtering for precise estimation of the position of the end-effector of a robot using, in addition to the usual measurements of the joint positions, direct measurements of the end-effector position. The estimation performances of extended Kalman filters are compared in applications to a planar two-axis robotic arm with very flexible links. The comparisons shed new light on the dependence of extended Kalman filter estimation performance on the quality of the model of the arm dynamics that the extended Kalman filter operates with. extended Kalman filter, estimation, flexible links, robot
Mobile payment security, threats, and challenges

Mobile payment systems can be divided into five categories including mobile payment at the POS, mobile payment as the POS, mobile payment platform, independent mobile payment system, and direct carrier billing. Although mobile payment has gained its popularity in many regions due to its convenience, it also faces many threats and security challenges. In this paper, we present a mobile payment processing model and introduce each type of mobile payment systems. We summarize the security services desired in mobile payment systems and also the security mechanisms which are currently in place. We further identify and discuss three security threats, i.e., malware, SSL/TLS vulnerabilities, and data breaches, and four security challenges, i.e., malware detection, multi-factor authentication, data breach prevention, and fraud detection and prevention, in mobile payment systems.
Prevalence of Stunting and Associated Factors among School Age Children in Primary Schools of Haik Town, South Wollo Zone, North- Eastern Ethiopia, 2017

Under-nutrition is the major public health problem in the developing countries including Ethiopia. This study aimed to investigate the magnitude of stunting and associated factors among school age children.  A school based cross-sectional study was conducted on 414 school age children in Haiyk town Primary schools, North eastern Ethiopia in May 2017. In this study, Stunting was defined as a child whose height for age Z-scores is below -2SD. Descriptive statistics, bivariate analysis to identify associated factors and multivariable logistic regression analysis were employed to control the effect of potential confounders. Variables with a pvalue<0.05 in the multivariable model were identified as predictors of stunting.  The prevalence of stunting among school age children was 44 (11.3%) with Z-scores below-2SD and 83.7% of students were categorized under 16.5-18.5 body mass index. Multivariable logistic regression analysis showed that increased child level of education (AOR 4.028; 95% CI 1.72, 9.42), did not have additional food during study time (AOR 2.12; 95% CI 1.10, 4.12) and use of mixed food (AOR 0.20; 95% CI 0.06, 0.70) have been found significant associated with stunting.  The study revealed that magnitude of stunting among school age children was suboptimal. Therefore, interventions could focus on educating parents on the importance of timely feeding, balanced diet; economize use of the available resources. Further analytic studies should be conducted to investigate the causes of stunting among school children in the study area.
Sentence generation from a bag of words using N-gram model

We are presenting in this paper, a method of sentence generation from a given bag of words. The task of sentence generation has its usage in text summarization, question answering system etc. The focus of our task is to generate all possible correct sentences from a given bag of words. The technique that we have applied is N-gram language model. The N-gram model is trained by a text corpus to generate only candidate sequences from a given bag of words. For N input words, instead of considering all possible N! permuted orders as candidate sequence, we have generated only candidate sequences less then N! by applying DFS (Depth First Search) filtering technique at run time. We have two corpora namely text corpus and annotated corpus of POS tags. We have extracted all valid POS trigram tags from the annotated corpus. Each of the generated candidate sequence has a probability score. The candidate sequences were ranked by matching it with valid trigram POS tag signature and probability score. Preliminary experimental work carried out in this direction by using the above mentioned model shows promising results.
Short and Sparse Text Topic Modeling via Self-Aggregation

The overwhelming amount of short text data on social media and elsewhere has posed great challenges to topic modeling due to the sparsity problem. Most existing attempts to alleviate this problem resort to heuristic strategies to aggregate short texts into pseudo-documents before the application of standard topic modeling. Although such strategies cannot be well generalized to more general genres of short texts, the success has shed light on how to develop a generalized solution. In this paper, we present a novel model towards this goal by integrating topic modeling with short text aggregation during topic inference. The aggregation is founded on general topical affinity of texts rather than particular heuristics, making the model readily applicable to various short texts. Experimental results on real-world datasets validate the effectiveness of this new model, suggesting that it can distill more meaningful topics from short texts.
IT COMPETENCY AND FIRM PERFORMANCE : IS ORGANIZATIONAL LEARNING A MISSING LINK ?

Many companies have developed strategies that include investing heavily in information technology (IT) in order to enhance their performance. Yet, this investment pays off for some companies but not others. This study proposes that organization learning plays a significant role in determining the outcomes of IT. Drawing from resource theory and IT literature, the authors develop the concept of IT competency. Using structural equations modeling with data collected from managers in 271 manufacturing firms, they show that organizational learning plays a significant role in mediating the effects of IT competency on firm performance.
Model predictive control of the interleaved DC-DC boost converter with coupled inductors

This paper proposes a model predictive control (MPC) scheme for the interleaved dc-dc boost converter with coupled inductors. The main control objectives are the regulation of the output voltage to its reference value, despite changes in the input voltage and the load, and the equal sharing of the load current by the two circuit inductors. An inner control loop, using MPC, regulates the input current to its reference that is provided by the outer loop, which is based on a load observer. Simulation results are provided to highlight the performance of the proposed control scheme.
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks

There is an increasing interest on accelerating neural networks for real-time applications. We study the studentteacher strategy, in which a small and fast student network is trained with the auxiliary information learned from a large and accurate teacher network. We propose to use conditional adversarial networks to learn the loss function to transfer knowledge from teacher to student. The proposed method is particularly effective for relatively small student networks. Moreover, experimental results show the effect of network size when the modern networks are used as student. We empirically study the trade-off between inference time and classification accuracy, and provide suggestions on choosing a proper student network.
A Convolutional Architecture for Word Sequence Prediction

We propose a convolutional neural network, named genCNN, for word sequence prediction. Different from previous work on neural networkbased language modeling and generation (e.g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector. Instead, we use a convolutional neural network to predict the next word with the history of words of variable length. Also different from the existing feedforward networks for language modeling, our model can effectively fuse the local correlation and global correlation in the word sequence, with a convolution-gating strategy specifically designed for the task. We argue that our model can give adequate representation of the history, and therefore can naturally exploit both the short and long range dependencies. Our model is fast, easy to train, and readily parallelized. Our extensive experiments on text generation and n-best re-ranking in machine translation show that genCNN outperforms the state-ofthe-arts with big margins.
BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition

We are dealing with the problem of fine-grained vehicle make&model recognition and verification. Our contribution is showing that extracting additional data from the video stream - besides the vehicle image itself - and feeding it into the deep convolutional neural network boosts the recognition performance considerably. This additional information includes: 3D vehicle bounding box used for "unpacking" the vehicle image, its rasterized low-resolution shape, and information about the 3D vehicle orientation. Experiments show that adding such information decreases classification error by 26% (the accuracy is improved from 0.772 to 0.832) and boosts verification average precision by 208% (0.378 to 0.785) compared to baseline pure CNN without any input modifications. Also, the pure baseline CNN outperforms the recent state of the art solution by 0.081. We provide an annotated set "BoxCars" of surveillance vehicle images augmented by various automatically extracted auxiliary information. Our approach and the dataset can considerably improve the performance of traffic surveillance systems.
Self-adaptive systems: A survey of current approaches, research challenges and applications

Self-adaptive software is capable of evaluating and changing its own behavior, whenever the evaluation shows that the software is not accomplishing what it was intended to do, or when better functionality or performance may be possible. The topic of system adaptivity has been widely studied since the mid-60s and, over the past decade, several application areas and technologies relating to self-adaptivity have assumed greater importance. In all these initiatives, software has become the common element that introduces self-adaptability. Thus, the investigation of systematic software engineering approaches is necessary, in order to develop self-adaptive systems that may ideally be applied across multiple domains. The main goal of this study is to review recent progress on self-adaptivity from the standpoint of computer sciences and cybernetics, based on the analysis of state-of-the-art approaches reported in the literature. This review provides an over-arching, integrated view of computer science and software engineering foundations. Moreover, various methods and techniques currently applied in the design of self-adaptive systems are analyzed, as well as some European research initiatives and projects. Finally, the main bottlenecks for the effective application of self-adaptive technology, as well as a set of key research issues on this topic, are precisely identified, in order to overcome current constraints on the effective application of self-adaptivity in its emerging areas of application.
Analysis, Modeling, and Implementation of a Multidevice Interleaved DC/DC Converter for Fuel Cell Hybrid Electric Vehicles

Multiphase converter topologies for use in high-performance applications have received increasing interest in recent years. This paper proposes a novel multidevice interleaved boost converter (MDIBC) that interfaces the fuel cell with the powertrain of hybrid electric vehicles. In this research, a multidevice structure with interleaved control is proposed to reduce the input current ripples, the output voltage ripples, and the size of passive components with high efficiency compared with the other topologies. In addition, low EMI and low stress in the switches are expected. The proposed dc/dc converter is compared to other converter topologies such as conventional boost converter (BC), multidevice boost converter (MDBC), and two-phase interleaved boost converter (IBC) to verify its dynamic performance. Furthermore, a generalized small-signal model is derived for these dc/dc converters, which has not been previously discussed. A digital dual-loop control is designed to achieve the proper regulator for the converters with fast transient response. The dc/dc converter topologies and their controller are designed and investigated by using MATLAB/Simulink. Finally, the proposed converter (MDIBC) is experimentally validated with results obtained from a 30-kW prototype that has been built and tested in our laboratory based on TMS320F2808 DSP. The simulation and experimental results have demonstrated that the proposed converter is more efficient than other dc/dc converter topologies in achieving high performance and reliability for high-power dc/dc converters.
Real-time multiple vehicle detection and tracking from a moving vehicle

Abstract. A real-time vision system has been developed that analyzes color videos taken from a forward-looking video camera in a car driving on a highway. The system uses a combination of color, edge, and motion information to recognize and track the road boundaries, lane markings and other vehicles on the road. Cars are recognized by matching templates that are cropped from the input data online and by detecting highway scene features and evaluating how they relate to each other. Cars are also detected by temporal differencing and by tracking motion parameters that are typical for cars. The system recognizes and tracks road boundaries and lane markings using a recursive least-squares filter. Experimental results demonstrate robust, real-time car detection and tracking over thousands of image frames. The data includes video taken under difficult visibility conditions.
A Survey on Sensor-Cloud: Architecture, Applications, and Approaches

Many organizations desired to operate their businesses, works and services in a mobile (i.e. just in time and anywhere), dynamic, and knowledge-oriented fashion. Activities like e-learning, environmental learning, remote inspection, health-care, home security and safety mechanisms etc. requires a special infrastructure that might provide continuous, secured, reliable and mobile data with proper information/ knowledge management system in context to their confined environment and its users. An indefinite number of sensor networks for numerous healthcare applications has been designed and implemented but they all lacking extensibility, fault-tolerance, mobility, reliability and openness. Thus, an open, flexible and rearrangeable infrastructure is proposed for healthcare monitoring applications. Where physical sensors are virtualized as virtual sensors on cloud computing by this infrastructure and virtual sensors are provisioned automatically to end users whenever they required. In this paper we reviewed some approaches to hasten the service creations in field of healthcare and other applications with Cloud-Sensor architecture. This architecture provides services to end users without being worried about its implementation details. The architecture allows the service requesters to use the virtual sensors by themselves or they may create other new services by extending virtual sensors.
EVO: A Geometric Approach to Event-Based 6-DOF Parallel Tracking and Mapping in Real Time

We present EVO, an event-based visual odometry algorithm. Our algorithm successfully leverages the outstanding properties of event cameras to track fast camera motions while recovering a semidense three-dimensional (3-D) map of the environment. The implementation runs in real time on a standard CPU and outputs up to several hundred pose estimates per second. Due to the nature of event cameras, our algorithm is unaffected by motion blur and operates very well in challenging, high dynamic range conditions with strong illumination changes. To achieve this, we combine a novel, event-based tracking approach based on image-to-model alignment with a recent event-based 3-D reconstruction algorithm in a parallel fashion. Additionally, we show that the output of our pipeline can be used to reconstruct intensity images from the binary event stream, though our algorithm does not require such intensity information. We believe that this work makes significant progress in simultaneous localization and mapping by unlocking the potential of event cameras. This allows us to tackle challenging scenarios that are currently inaccessible to standard cameras.