nils-herrmann/clean_scirepeval huggingface dataset:
A Framework for Designing the Architectures of Deep Convolutional Neural Networks

Recent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult deep learning tasks. However, the success of a CNN depends on finding an architecture to fit a given problem. A hand-crafted architecture is a challenging, time-consuming process that requires expert knowledge and effort, due to a large number of architectural design choices. In this article, we present an efficient framework that automatically designs a high-performing CNN architecture for a given problem. In this framework, we introduce a new optimization objective function that combines the error rate and the information learnt by a set of feature maps using deconvolutional networks (deconvnet). The new objective function allows the hyperparameters of the CNN architecture to be optimized in a way that enhances the performance by guiding the CNN through better visualization of learnt features via deconvnet. The actual optimization of the objective function is carried out via the Nelder-Mead Method (NMM). Further, our new objective function results in much faster convergence towards a better architecture. The proposed framework has the ability to explore a CNN architecture’s numerous design choices in an efficient way and also allows effective, distributed execution and synchronization via web services. Empirically, we demonstrate that the CNN architecture designed with our approach outperforms several existing approaches in terms of its error rate. Our results are also competitive with state-of-the-art results on the MNIST dataset and perform reasonably against the state-of-the-art results on CIFAR-10 and CIFAR-100 datasets. Our approach has a significant role in increasing the depth, reducing the size of strides, and constraining some convolutional layers not followed by pooling layers in order to find a CNN architecture that produces a high recognition performance.
Face Recognition Under Varying Illumination

Face Recognition by a robot or machine is one of the challenging research topics in the recent years. It has become an active research area which crosscuts several disciplines such as image processing, pattern recognition, computer vision, neural networks and robotics. For many applications, the performances of face recognition systems in controlled environments have achieved a satisfactory level. However, there are still some challenging issues to address in face recognition under uncontrolled conditions. The variation in illumination is one of the main challenging problems that a practical face recognition system needs to deal with. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals (Adini et al., 1997). Various methods have been proposed to solve the problem. These methods can be classified into three categories, named face and illumination modeling, illumination invariant feature extraction and preprocessing and normalization. In this chapter, an extensive and state-of-the-art study of existing approaches to handle illumination variations is presented. Several latest and representative approaches of each category are presented in detail, as well as the comparisons between them. Moreover, to deal with complex environment where illumination variations are coupled with other problems such as pose and expression variations, a good feature representation of human face should not only be illumination invariant, but also robust enough against pose and expression variations. Local binary pattern (LBP) is such a local texture descriptor. In this chapter, a detailed study of the LBP and its several important extensions is carried out, as well as its various combinations with other techniques to handle illumination invariant face recognition under a complex environment. By generalizing different strategies in handling illumination variations and evaluating their performances, several promising directions for future research have been suggested. This chapter is organized as follows. Several famous methods of face and illumination modeling are introduced in Section 2. In Section 3, latest and representative approaches of illumination invariant feature extraction are presented in detail. More attentions are paid on quotient-image-based methods. In Section 4, the normalization methods on discarding low frequency coefficients in various transformed domains are introduced with details. In Section 5, a detailed introduction of the LBP and its several important extensions is presented, as well as its various combinations with other face recognition techniques. In Section 6, comparisons between different methods and discussion of their advantages and disadvantages are presented. Finally, several promising directions as the conclusions are drawn in Section 7.
Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks

While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps and show that the depth estimation task can be effectively tackled within an adversarial learning framework. Specifically, we propose a deep generative network that learns to predict the correspondence field (i.e. the disparity map) between two image views in a calibrated stereo camera setting. The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other. Extensive experiments on the publicly available datasets KITTI and Cityscapes demonstrate the effectiveness of the proposed model and competitive results with state of the art methods. The code is available at https://github.com/andrea-pilzer/unsup-stereo-depthGAN
Bilateral Multi-Perspective Matching for Natural Language Sentences

Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (wordby-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model. Given two sentences P and Q, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions P against Q and Q against P . In each matching direction, each time step of one sentence is matched against all timesteps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fixed-length matching vector. Finally, based on the matching vector, a decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks.
Plane-Wave Coupling to a Twisted-Wire Pair Above Ground

A transmission-line model is developed for predicting the response of a twisted-wire pair (TWP) circuit in the presence of a ground plane, illuminated by a plane-wave electromagnetic field. The twisted pair is modeled as an ideal bifilar helix, the total coupling is separated into differential- (DM) and common-mode (CM) contributions, and closed-form expressions are derived for the equivalent induced sources. Approximate upper bounds to the terminal response of electrically long lines are obtained, and a simplified low-frequency circuit model is used to explain the mechanism of field-to-wire coupling in a TWP above ground, as well as the role of load balancing on the DM and CM electromagnetic noise induced in the terminal loads.
The Architecture of Enterprise Hospital Information System

Because of the complexity of the hospital environment, there exist a lot of medical information systems from different vendors with incompatible structures. In order to establish an enterprise hospital information system, the integration among these heterogeneous systems must be considered. Complete integration should cover three aspects: data integration, function integration and workflow integration. However most of the previous design of architecture did not accomplish such a complete integration. This article offers an architecture design of the enterprise hospital information system based on the concept of digital neural network system in hospital. It covers all three aspects of integration, and eventually achieves the target of one virtual data center with Enterprise Viewer for users of different roles. The initial implementation of the architecture in the 5-year Digital Hospital Project in Huzhou Central hospital of Zhejiang Province is also described
Improving zero-shot learning by mitigating the hubness problem

The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels. We show that the neighbourhoods of the mapped elements are strongly polluted by hubs, vectors that tend to be near a high proportion of items, pushing their correct labels down the neighbour list. After illustrating the problem empirically, we propose a simple method to correct it by taking the proximity distribution of potential neighbours across many mapped vectors into account. We show that this correction leads to consistent improvements in realistic zero-shot experiments in the cross-lingual, image labeling and image retrieval domains.
Multi-defense Mechanism against DDoS in SDN Based CDNi

Lately enhancing the capability of network services automatically and dynamically through SDN and CDN/CDNi networks has become a recent topic of research. While, in one hand, these systems can be very beneficial to control and optimize the overall network services that studies the topology, traffic paths, packet handling and such others, on the other hand, the servers in such architectures can also be a potential target for DoS and/or DDoS attacks. We, therefore, propose a mechanism for the SDN based CDNi networks to securely deliver services with a multi-defense strategy against DDoS attacks. Addition of ALTO like servers in such architectures enables mapping a very big network to provide a bird's eye view. We propose an additional marking path map in the ALTO server to trace the request packets. The next defense is a protection switch to protect the main servers. A Management Information Base (MIB) is also proposed in the SDN controller to compare and assess the request traffic coming to the protection switches.
Design of Ternary Logic Combinational Circuits Based on Quantum Dot Gate FETs

In this paper, we discuss logic circuit designs using the circuit model of three-state quantum dot gate field effect transistors (QDGFETs). QDGFETs produce one intermediate state between the two normal stable ON and OFF states due to a change in the threshold voltage over this range. We have developed a simplified circuit model that accounts for this intermediate state. Interesting logic can be implemented using QDGFETs. In this paper, we discuss the designs of various two-input three-state QDGFET gates, including NAND- and NOR-like operations and their application in different combinational circuits like decoder, multiplier, adder, and so on. Increased number of states in three-state QDGFETs will increase the number of bit-handling capability of this device and will help us to handle more number of bits at a time with less circuit elements.
Balance control of two-wheeled self-balancing robot based on Linear Quadratic Regulator and Neural Network

Two-wheeled self-balancing robot is a kind of unstable, nonlinear, strong coupling system. On the basis of analyzing the method of Linear Quadratic Regulator(LQR) and PID-BP-RBF, this paper proposed a new balance control method based on LQR and Neural Network(NN)(LQR-NN).In this method, the balance controller is designed as a LQR controller contained a neural network inside. The LQR's optimal parameters are used to initialize the neural network, which would make the network have the optimum initial values and converge fast. The new method can overcome the inaccuracy modeling because of system linearization based on LQR, and also has the self-turning mechanism without great computation load which the NN method brings. Experiments show that the balance controller based on LQR-NN has better balancing control to the robot and also improved the system's robustness significantly.
Face recognition by applying wavelet subband representation and kernel associative memory

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.
A fuzzy expert system for earthquake prediction, case study: the Zagros range

A methodology for the development of a fuzzy expert system (FES) with application to earthquake prediction is presented. The idea is to reproduce the performance of a human expert in earthquake prediction. To do this, at the first step, rules provided by the human expert are used to generate a fuzzy rule base. These rules are then fed into an inference engine to produce a fuzzy inference system (FIS) and to infer the results. In this paper, we have used a Sugeno type fuzzy inference system to build the FES. At the next step, the adaptive network-based fuzzy inference system (ANFIS) is used to refine the FES parameters and improve its performance. The proposed framework is then employed to attain the performance of a human expert used to predict earthquakes in the Zagros area based on the idea of coupled earthquakes. While the prediction results are promising in parts of the testing set, the general performance indicates that prediction methodology based on coupled earthquakes needs more investigation and more complicated reasoning procedure to yield satisfactory predictions.
Learning to Predict Severity of Software Vulnerability Using Only Vulnerability Description

Software vulnerabilities pose significant security risks to the host computing system. Faced with continuous disclosure of software vulnerabilities, system administrators must prioritize their efforts, triaging the most critical vulnerabilities to address first. Many vulnerability scoring systems have been proposed, but they all require expert knowledge to determine intricate vulnerability metrics. In this paper, we propose a deep learning approach to predict multi-class severity level of software vulnerability using only vulnerability description. Compared with intricate vulnerability metrics, vulnerability description is the "surface level" information about how a vulnerability works. To exploit vulnerability description for predicting vulnerability severity, discriminative features of vulnerability description have to be defined. This is a challenging task due to the diversity of software vulnerabilities and the richness of vulnerability descriptions. Instead of relying on manual feature engineering, our approach uses word embeddings and a one-layer shallow Convolutional Neural Network (CNN) to automatically capture discriminative word and sentence features of vulnerability descriptions for predicting vulnerability severity. We exploit large amounts of vulnerability data from the Common Vulnerabilities and Exposures (CVE) database to train and test our approach.
Parsing the WSJ Using CCG and Log-Linear Models

This paper describes and evaluates log-linear parsing models for Combinatory Categorial Grammar (CCG). A parallel implementation of the L-BFGS optimisation algorithm is described, which runs on a Beowulf cluster allowing the complete Penn Treebank to be used for estimation. We also develop a new efficient parsing algorithm for CCG which maximises expected recall of dependencies. We compare models which use all CCG derivations, including nonstandard derivations, with normal-form models. The performances of the two models are comparable and the results are competitive with existing wide-coverage CCG parsers.
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing

Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We conducted an online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the responses of the participants to our questions, highlighting common patterns and challenges. The participants’ responses revealed surprising facts about graph processing in practice, which we hope can guide future research.
Lane Recognition Using On-vehicle LIDAR

This paper presents an approach to the lane recognition using on-vehicle LIDAR. It detests the objects by 2D scanning and collects the range and reflectivity data in each scanning direction. We developed the lane recognition algorithm with these data, in which the lane curvature, yaw angle and offset are calculated by using the Hough transformation, and the lane width is calculated by statistical procedure. Next the lane marks are tracked by the extended Kalman filter. Then we test the performance of the lane recognition and the good results are achieved. Finally, we show the result of the road environment recognition applying the lane recognition by LIDAR
Intelligent Critic System for Architectural Design

This paper describes an intelligent computer-aided architectural design system (ICAAD) called ICADS. ICADS encapsulates different types of design knowledge into independent “critic” modules. Each “critic” module possesses expertise in evaluating an architect’s work in different areas of architectural design and can offer expert advice when needed. This research focuses on the representation of spatial information encoded in architectural floor plans and the representation of expert design knowledge. Described in this paper is our research in designing and developing two particular “critic” modules. The first module, FPDX, checks a residential apartment floor plan, verifies that the plan meets a set of government regulations, and offers suggestions for floor plan changes if regulations are not met. The second module, IDX, analyzes room and furniture layout according to a set of interior design guidelines and offers ideas on how furniture should be moved if the placement does not follow good design principles.
Multi-view reconstruction preserving weakly-supported surfaces

We propose a novel method for the multi-view reconstruction problem. Surfaces which do not have direct support in the input 3D point cloud and hence need not be photo-consistent but represent real parts of the scene (e.g. low-textured walls, windows, cars) are important for achieving complete reconstructions. We augmented the existing Labatut CGF 2009 method with the ability to cope with these difficult surfaces just by changing the t-edge weights in the construction of surfaces by a minimal s-t cut. Our method uses Visual-Hull to reconstruct the difficult surfaces which are not sampled densely enough by the input 3D point cloud. We demonstrate importance of these surfaces on several real-world data sets. We compare our improvement to our implementation of the Labatut CGF 2009 method and show that our method can considerably better reconstruct difficult surfaces while preserving thin structures and details in the same quality and computational time.
Mobile Robot Localisation and Mapping in Extensive Outdoor Environments

Tim Bailey Doctor of Philosophy The University of Sydney August 2002 Mobile Robot Localisation and Mapping in Extensive Outdoor Environments This thesis addresses the issues of scale for practical implementations of simultaneous localisation and mapping (SLAM) in extensive outdoor environments. Building an incremental map while also using it for localisation is of prime importance for mobile robot navigation but, until recently, has been confined to small-scale, mostly indoor, environments. The critical problems for large-scale implementations are as follows. First, data association— finding correspondences between map landmarks and robot sensor measurements—becomes difficult in complex, cluttered environments, especially if the robot location is uncertain. Second, the information required to maintain a consistent map using traditional methods imposes a prohibitive computational burden as the map increases in size. And third, the mathematics for SLAM relies on assumptions of small errors and near-linearity, and these become invalid for larger maps. In outdoor environments, the problems of scale are exacerbated by complex structure and rugged terrain. This can impede the detection of stable discrete landmarks, and can degrade the utility of motion estimates derived from wheel-encoder odometry. This thesis presents the following contributions for large-scale SLAM. First, a batch data association method called combined constraint data association (CCDA) is developed, which permits robust association in cluttered environments even if the robot pose is completely unknown. Second, an alternative to feature-based data association is presented, based on correlation of unprocessed sensor data with the map, for environments that don’t contain easily detectable discrete landmarks. Third, methods for feature management are presented to control the addition and removal of map landmarks, which facilitates map reliability and reduces computation. Fourth, a new map framework called network coupled feature maps (NCFM) is introduced, where the world is divided into a graph of connected submaps. This map framework is shown to solve the problems of consistency and tractability for very large-scale SLAM. The theoretical contributions of this thesis are demonstrated with a series of practical implementations using a scanning range laser in three different outdoor environments. These include: sensor-based dead reckoning, which is a highly accurate alternative to odometry for rough terrain; correlation-based localisation using particle filter methods; and NCFM SLAM over a region greater than 50000 square metres, and including trajectories with large loops.