nils-herrmann/clean_scirepeval huggingface dataset:
Bridging the semantic gaps of GPU acceleration for scale-out CNN-based big data processing: Think big, see small

Convolutional Neural Networks (CNNs) have substantially advanced the state-of-the-art accuracies of object recognition, which is the core function of a myriad of modern multimedia processing techniques such as image/video processing, speech recognition, and natural language processing. GPU-based accelerators gained increasing attention because a large amount of highly parallel neurons in CNN naturally matches the GPU computation pattern. In this work, we perform comprehensive experiments to investigate the performance bottlenecks and overheads of current GPU acceleration platform for scale-out CNN-based big data processing.
 In our characterization, we observe two significant semantic gaps: framework gap that lies between CNN-based data processing workflow and data processing manner in distributed framework; and the standalone gap that lies between the uneven computation loads at different CNN layers and fixed computing capacity provisioning of current GPU acceleration library. To bridge these gaps, we propose D3NN, a Distributed, Decoupled, and Dynamically tuned GPU acceleration framework for modern CNN architectures. In particular, D3NN features a novel analytical model that enables accurate time estimation of GPU accelerated CNN processing with only 5-10% error. Our evaluation results show the throughput of standalone processing node using D3NN gains up to 3.7X performance improvement over current standalone GPU acceleration platform. Our CNN-oriented GPU acceleration library with built-in dynamic batching scheme achieves up to 1.5X performance improvement over the non-batching scheme and outperforms the state-of-the-art deep learning library by up to 28% (performance mode) ~ 67% (memory-efficient mode).
Slackliner - An Interactive Slackline Training Assistant

In this paper we present Slackliner, an interactive slackline training assistant which features a life-size projection, skeleton tracking and real-time feedback. As in other sports, proper training leads to a faster buildup of skill and lessens the risk for injuries. We chose a set of exercises from slackline literature and implemented an interactive trainer which guides the user through the exercises and gives feedback if the exercise was executed correctly.
 A post analysis gives the user feedback about her performance. We conducted a user study to compare the interactive slackline training system with a classic approach using a personal trainer. No significant difference was found between groups regarding balancing time, number of steps and the walking distance on the line for the left and right foot. Significant main effects for the balancing time on line, without considering the group, have been found. User feedback acquired by questionnaires and semi-structured interviews was very positive. Overall, the results indicate that the interactive slackline training system can be used as an enjoyable and effective alternative to classic training methods.
Designing a mobile language learning system based on lightweight learning objects

This paper investigates the possibilities in the area of application of mobile technologies for language learning. The primary goal is to design a mobile system for learning of the Japanese language with a clear separation of content and presentation, and to leverage the learners’ interest in the Japanese language. Both the technical and the language learning perspectives are discussed. A reusable, lightweight model of learning objects with compact content and a reduced metadata set is presented. These objects are stored in a simple learning object repository that can deliver them to any client over the Internet. A mobile application is designed to use the learning object repository as its content provider, while defining its own method of presentation suitable for smaller screens of mobile devices. For the purpose of evaluation, an experiment was conducted within the e-learning system at the Faculty of Organizational Sciences, University of Belgrade. Research findings have indicated that the developed mobile application has a positive effect on the students’ interest in the subject matter.
Challenges Facing the Industrial Implementation of Fog Computing

Recently, the industries converge to the integration of the industry 4.0 paradigm to keep responding to the variable market demands. This integration is realized by the adoption of several components of the industry 4.0 such as IoT, Big Data and Cloud Computing, etc. Several difficulties concerning the integration of data management were encountered during first level of Industry 4.0 integration because of the unexpected quantity of data generated by IoT devices. The Fog computing can be considered as a new component of Industry 4.0 to resolve this kind of problem. However its implementation in the industrial field faces several challenges from different natures. This paper explains the role of Fog Computing solution to enhance the Cloud layer (distribution, low latency, real-time,. . . ) and studies its ability to be implemented in manufacturing systems. The Fog Manufacturing is introduced as the new industrial Fog vision. The challenges preventing the Fog Manufacturing implementation are studied and the links between each other are justified. A future use case is described to carry out the solutions given to satisfy the Fog Manufacturing challenges.
A physically-based approach to reflection separation

We propose a physically-based approach to separate reflection using multiple polarized images with a background scene captured behind glass. The input consists of three polarized images, each captured from the same view point but with a different polarizer angle separated by 45 degrees. The output is the high-quality separation of the reflection and background layers from each of the input images. A main technical challenge for this problem is that the mixing coefficient for the reflection and background layers depends on the angle of incidence and the orientation of the plane of incidence, which are spatially-varying over the pixels of an image. Exploiting physical properties of polarization for a double-surfaced glass medium, we propose an algorithm which automatically finds the optimal separation of the reflection and background layers. Thorough experiments, we demonstrate that our approach can generate superior results to those of previous methods.
Neural network regularization and ensembling using multi-objective evolutionary algorithms

Regularization is an essential technique to improve generalization of neural networks. Traditionally, regularization is conducted by including an additional term in the cost function of a learning algorithm. One main drawback of these regularization techniques is that a hyperparameter that determines to which extension the regularization influences the learning algorithm must be determined beforehand. This paper addresses the neural network regularization problem from a multi-objective optimization point of view. During the optimization, both structure and parameters of the neural network will be optimized. A slightly modified version of two multi-objective optimization algorithms, the dynamic weighted aggregation (DWA) method and the elitist non-dominated sorting genetic algorithm (NSGA-II) are used and compared. An evolutionary multi-objective approach to neural network regularization has a number of advantages compared to the traditional methods. First, a number of models with a spectrum of model complexity can be obtained in one optimization run instead of only one single solution. Second, an efficient new regularization term can be introduced, which is not applicable to gradient-based learning algorithms. As a natural by-product of the multi-objective optimization approach to neural network regularization, neural network ensembles can be easily constructed using the obtained networks with different levels of model complexity. Thus, the model complexity of the ensemble can be adjusted by adjusting the weight of each member network in the ensemble. Simulations are carried out on a test function to illustrate the feasibility of the proposed ideas.
Modelling uncertainty in deep learning for camera relocalization

We present a robust and real-time monocular six degree of freedom visual relocalization system. We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGB image. It is trained in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking under 6ms to compute. It obtains approximately 2m and 6° accuracy for very large scale outdoor scenes and 0.5m and 10° accuracy indoors. Using a Bayesian convolutional neural network implementation we obtain an estimate of the model's relocalization uncertainty and improve state of the art localization accuracy on a large scale outdoor dataset. We leverage the uncertainty measure to estimate metric relocalization error and to detect the presence or absence of the scene in the input image. We show that the model's uncertainty is caused by images being dissimilar to the training dataset in either pose or appearance.
Improved Single Image Dehazing Using Dark Channel Prior and Multi-scale Retinex

In this paper, we propose an improved image dehazing algorithm using dark channel prior and Multi-Scale Retinex. Main improvement lies in automatic and fast acquisition of transmission map of the scene. We implement the Multi-scale Retinex algorithm on the luminance component in YCbCr space, obtain a pseudo transmission map whose function is similar to the transmission map in original approach. Combining with the haze image model and the dark channel prior, we can recover a high quality haze-free image. Compared with the original method, our algorithm has two main advantages: (i) no user interaction is needed, and (ii) restoring the image much faster while maintaining comparable dehazing performance.
FloodGuard: A DoS Attack Prevention Extension in Software-Defined Networks

This paper addresses one serious SDN-specific attack, i.e., data-to-control plane saturation attack, which overloads the infrastructure of SDN networks. In this attack, an attacker can produce a large amount of table-miss packet_in messages to consume resources in both control plane and data plane. To mitigate this security threat, we introduce an efficient, lightweight and protocol-independent defense framework for SDN networks. Our solution, called FloodGuard, contains two new techniques/modules: proactive flow rule analyzer and packet migration. To preserve network policy enforcement, proactive flow rule analyzer dynamically derives proactive flow rules by reasoning the runtime logic of the SDN/OpenFlow controller and its applications. To protect the controller from being overloaded, packet migration temporarily caches the flooding packets and submits them to the OpenFlow controller using rate limit and round-robin scheduling. We evaluate FloodGuard through a prototype implementation tested in both software and hardware environments. The results show that FloodGuard is effective with adding only minor overhead into the entire SDN/OpenFlow infrastructure.
California Verbal Learning Test: performance by patients with focal frontal and non-frontal lesions.

Although frontal lobe lesions do not cause classic amnesia, they may disrupt learning and memory in a number of ways. To investigate in finer detail the regions of frontal injury that are associated with impaired learning and to define the cognitive processing deficits specific to each region that disrupt memory, we compared 33 patients with focal frontal injury with patients with non-frontal injury and with normal controls on a standard neuropsychological instrument, the California Verbal Learning Test (CVLT). Subgroups of patients with distinct lesion site profiles were compared in a number of learning measures. All of the subgroups of patients with frontal lesions (with one exception) had inefficient learning due to poor implementation of a strategy of subjective organization. Despite this organizational deficiency, the performance of patients with frontopolar lesions normalized across trials. Only the subgroups with lesions centred either on the left posterior dorsolateral frontal region or the posterior medial frontal region had overall impaired learning and recall. The left posterior dorsolateral frontal group was most significantly impaired on all measures. This recall impairment was secondary to a mild lexical-semantic deficit. A recognition memory deficit in the same group was due to an abnormal response bias. Several groups had a modest increase in perseverative recalls; the underlying mechanisms differed. Disruption of different cognitive processes associated with specific frontal regions underlies the varied patterns of memory impairment. This study has demonstrated even finer differentiations within the frontal region than previously known.
Perceptual Control Theory 1 Perceptual Control Theory A Model for Understanding the Mechanisms and Phenomena of Control

Perceptual Control Theory (PCT) provides a general theory of functioning for organisms. At the conceptual core of the theory is the observation that living things control the perceived environment by means of their behavior. Consequently, the phenomenon of control takes center stage in PCT, with observable behavior playing an important but supporting role. The first part of the paper explains how the PCT model works. This explanation includes a definition of “control” as well as the basic equations from which one can see what is required for control to be possible. The second part of the paper describes demonstrations that the reader can download from the Internet and run, so as to learn the basics of control by experiencing and verifying the phenomenon directly. The third part of the paper shows examples of the application of PCT to different areas of psychological research including learning, developmental psychology, social psychology, and psychotherapy. This summary of the current state of the field celebrates the 50th Anniversary of the first major publication in PCT (Powers, Clark & MacFarland, 1960).
RFID Coverage Extension Using Microstrip-Patch Antenna Array [Wireless Corner]

In this paper, a UHF-band 2 times 2 microstrip phased-array antenna is designed and implemented to extend the coverage of an RFID reader system. The phased-array antenna has four microstrip-patch antennas, three Wilkinson power dividers, and a transmission-line phase shifter. These are printed on a dielectric substrate with a dielectric constant of 4.5. The array has dimensions of 34 cm times 45 cm, operating at a frequency of 867 MHz, as specified in RFID Gen2 protocol European standards. The phased-array antenna has a measured directivity of 12.1 dB, and the main-beam direction can be steered to angles of plusmn 40deg, with a HPBW of 90deg. The phased-array antenna is used as the receiving antenna in a commercial reader system. Experimental results indicate that the coverage of the RFID system with the phased-array antenna is superior to the coverage with a conventional broader-beamwidth microstrip-patch antenna. The proposed system can also be used for a wireless positioning system.
Simplified high-accuracy calculation of eddy-current loss in round-wire windings

It has recently been shown that the most commonly used methods for calculating high-frequency eddy-current loss in round-wire windings can have substantial error, exceeding 60%. Previous work includes a formula based on a parametric set of finite-element analysis (FEA) simulations that gives proximity-effect loss for a large range of frequencies, using the parameters from a lookup table based on winding geometry. We improve the formula by decreasing the number of parameters in the formula and also, more importantly, by using simple functions to get the parameters from winding geometry such that a large lookup table is not needed. The function we present is exact in the low frequency limit (diameter much smaller than skin depth) and has error less than 4% at higher frequencies. We make our new model complete by examining the field expression needed to get the total proximity-effect loss and by including the skin-effect loss. We also present experimental results confirming the validity of the model and its superiority to standard methods.
Efficiently indexing shortest paths by exploiting symmetry in graphs

Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries on-the-fly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest paths in a graph of N vertices takes O(N2) space. In this paper, we tackle the problem of indexing shortest paths and online answering shortest path queries. As many large real graphs are shown richly symmetric, the central idea of our approach is to use graph symmetry to reduce the index size while retaining the correctness and the efficiency of shortest path query answering. Technically, we develop a framework to index a large graph at the orbit level instead of the vertex level so that the number of breadth-first search trees materialized is reduced from O(N) to O(|Δ|), where |Δ| ≤ N is the number of orbits in the graph. We explore orbit adjacency and local symmetry to obtain compact breadth-first-search trees (compact BFS-trees). An extensive empirical study using both synthetic data and real data shows that compact BFS-trees can be built efficiently and the space cost can be reduced substantially. Moreover, online shortest path query answering can be achieved using compact BFS-trees.
Zero moment point-measurements from a human walker wearing robot feet as shoes

The anthropomorphic biped robot Bip is equipped with sensors for measuring the ground/feet forces in order to localize the center of pressure (CoP) and zero moment point (ZMP). This paper focuses on experimental results regarding the evolution of the ground contact forces, obtained from a human walker wearing the robot feet as shoes. First, one determines the influence of heavy and rigid metallic shoes on the gait of the subject, during experiments carried out on flat ground. Second, the evolution of the contact forces is studied while walking on parallel planes with different elevations (stairs), then one addresses the case where the feet are supported by two nonparallel planes (uneven terrain). The corresponding analysis is founded on the concepts of virtual supporting surface and pseudo-CoP-ZMP introduced in a companion paper, discussing the theoretical aspects of CoP and ZMP (Sardain and Bessonnet, 2004). Beyond the academic contribution, the data analyzed in this paper could constitute an incitement to design truly anthropomorphic feet, for Bip as well as for other biped robots, because all the current robot feet are functionally rather poor.
Hemi-cylinder unwrapping algorithm of fish-eye image based on equidistant projection model

This paper presents a novel fish-eye image unwrapping algorithm based on equidistant projection mode. We discuss a framework to estimate the image distortion center using vanishing points extraction. Then we propose a fish-eye image unwrapping algorithm using hemi-cylinder projection in 3D space. Experimental results show that our algorithm is efficient and effective. In particular, the hemi-cylinder unwrapping results do not reduce the horizontal field of view which is very useful for panoramic surveillance with applications in important sites safety compared with other fish-eye image correction methods.
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models

Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy (“torques”) from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for longterm predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques.
Weakly supervised named entity classification

In this paper, we describe a new method for the problem of named entity classification for specialized or technical domains, using distant supervision. Our approach relies on a simple observation: in some specialized domains, named entities are almost unambiguous. Thus, given a seed list of names of entities, it is cheap and easy to obtain positive examples from unlabeled texts using a simple string match. Those positive examples can then be used to train a named entity classifier, by using the PU learning paradigm, which is learning from positive and unlabeled examples. We introduce a new convex formulation to solve this problem, and apply our technique in order to extract named entities from financial reports corresponding to healthcare companies.
Massive MIMO Has Unlimited Capacity

The capacity of cellular networks can be improved by the unprecedented array gain and spatial multiplexing offered by Massive MIMO. Since its inception, the coherent interference caused by pilot contamination has been believed to create a finite capacity limit, as the number of antennas goes to infinity. In this paper, we prove that this is incorrect and an artifact from using simplistic channel models and suboptimal precoding/combining schemes. We show that with multicell MMSE precoding/combining and a tiny amount of spatial channel correlation or large-scale fading variations over the array, the capacity increases without bound as the number of antennas increases, even under pilot contamination. More precisely, the result holds when the channel covariance matrices of the contaminating users are asymptotically linearly independent, which is generally the case. If also the diagonals of the covariance matrices are linearly independent, it is sufficient to know these diagonals (and not the full covariance matrices) to achieve an unlimited asymptotic capacity.
Design and Describe REST API without Violating REST: A Petri Net Based Approach

As REST architectural style gains popularity in the web service community, there is a growing concern and debate on how to design Restful web services (REST API) in a proper way. We attribute this problem to lack of a standard model and language to describe a REST API that respects all the REST constraints. As a result, many web services that claim to be REST API are not hypermedia driven as prescribed by REST. This situation may lead to REST APIs that are not as scalable, extensible, and interoperable as promised by REST. To address this issue, this paper proposes REST Chart as a model and language to design and describe REST API without violating the REST constraints. REST Chart models a REST API as a special type of Colored Petri Net whose topology defines the REST API and whose token markings define the representational state space of user agents using that API. We demonstrate REST Chart with an example REST API. We also show how REST Chart can support efficient content negotiation and reuse hybrid representations to broaden design choices. Furthermore, we argue that the REST constraints, such as hypermedia driven and statelessness, can either be enforced naturally or checked automatically in REST Chart.