nils-herrmann/clean_scirepeval huggingface dataset:
DeepFacade: A Deep Learning Approach to Facade Parsing

The parsing of building facades is a key component to the problem of 3D street scenes reconstruction, which is long desired in computer vision. In this paper, we propose a deep learning based method for segmenting a facade into semantic categories. Man-made structures often present the characteristic of symmetry. Based on this observation, we propose a symmetric regularizer for training the neural network. Our proposed method can make use of both the power of deep neural networks and the structure of man-made architectures. We also propose a method to refine the segmentation results using bounding boxes generated by the Region Proposal Network. We test our method by training a FCN-8s network with the novel loss function. Experimental results show that our method has outperformed previous state-of-the-art methods significantly on both the ECP dataset and the eTRIMS dataset. As far as we know, we are the first to employ end-to-end deep convolutional neural network on full image scale in the task of building facades parsing.
Demo: Turning a Mobile Device into a Mouse in the Air

A mouse has been one of the most successful user interfaces due to its intuitive use. As more devices are equipped with displays and offer rich options for users to choose from, a traditional mouse that requires a surface to operate is no longer sufficient. While different types of air mice are available in the market, they rely on accelerometers and gyroscopes, which significantly limit the accuracy and ease of use. In this paper, we develop a system that can accurately track hand movement to realize a mouse. A unique advantage of our scheme is that it achieves high tracking accuracy using the existing hardware already available in the mobile devices (e.g., smart phones and smart watches) and equipment to be controlled (e.g., smart TVs). More specifically, our approach sends inaudible sound pulses at a few selected frequencies, and uses the frequency shifts to estimate the speed and distance traveled. We then develop techniques to quickly calibrate the distance between speakers and narrow down the device’s initial position using its movement trajectory. Based on the information, we continuously track the device’s new position in real time. This is feasible because many devices, such as smart TVs, PCs, and laptops, already have multiple speakers. When only one speaker is available, we can leverage the frequency shift of sound along with the phase of received WiFi signal to enable tracking. Our evaluation and user study demonstrate that our system achieves high tracking accuracy (e.g., median error of around 1.4 cm) and ease of use.
L-Band Feed Horn and Orthogonal Mode Transducer for the KAT-7 Radio Telescope

The design, simulation and measured performance of a broad bandwidth L-band wide flare angle corrugated conical horn and its orthogonal mode transducer are presented. The feed horn is used on prime focus parabolic reflector antennas of a seven-element interferometer, which is part of the Karoo Array Telescope project. The horn was designed to maximize the sensitivity of the radio telescope over a frequency band of 47.6% relative bandwidth. The orthogonal mode transducer design has low insertion and transmission losses and good isolation, and because of its compact size requires a very small cryogenic cooling system.
Browser Fingerprinting as user tracking technology

The Web has become an indispensable part of our society and is currently most commonly used mode of information delivery. Millions of users access the free services provided by the websites on daily basis and while providing these free services websites track and profile their web users. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. The objective of this paper is to study about the increasingly common yet hardly discussed technique of identifying individual Web users and tracking them across multiple websites known as “Browser Fingerprinting”. A unique browser fingerprint is derived by the unique pattern of information visible whenever a computer visits a website. The permutations thus collected are sufficiently distinct that they can be used as a tool for tracking. Unlike cookies, Fingerprints are generated on server side and are difficult for a user to influence. The main objective of this research is study about how the fingerprinting was evolved, its positives and negatives, what threat it poses to users' online privacy and what countermeasures could be used to prevent it. This paper will also analyse which different properties the browsers send to the server, allowing a unique fingerprint of those browsers to be created.
The Impact of Motivation and Personality on Academic Performance in Online and Blended Learning Environments

This study investigates the impact of students’ motivation and personality traits on their academic performance in online and blended learning environments. It was conducted with students attending a mandatory introductory information technology course given in a university in Turkey. The Big Five Inventory and Motivated Strategies for Learning Questionnaire were completed by a total of 316 students. A learning management system (LMS) was used for online collaboration and accessing course materials. At the end of the course, information on the accessibility of LMS and students’ academic performance including their exam results was obtained. The Bayesian Structural Equation Modeling was used to examine academic performance in terms of its relationship with motivation and personality. In the online learning environment, the results showed that the conscientiousness trait was significantly related to LMS use whereas in blended learning, there were no significant relations between personality traits and LMS use. Self-efficacy was found to be the predictor of LMS use in the online environment while task value and test anxiety were the predictors in the blended learning environment. Conscientiousness and LMS use were significantly related to course grades in both learning environments. Finally, self-efficacy for learning performance was also associated with course grades in the online learning environment.
Face segmentation using skin-color map in videophone applications

This paper addresses our proposed method to automatically segment out the person s face from a given image that consists of a head and shoulders view of the per son and a complex background scene The method involves a fast reliable and e ective algorithm that exploits the spatial distribution characteristics of human skin color A univer sal skin color map is derived and used on the chrominance component of the input image to detect pixels with skin color appearance Then based on the spatial distribution of the detected skin color pixels and their corresponding luminance values the algorithm employs a set of novel reg ularization processes to reinforce regions of skin color pix els that are more likely to belong to the facial regions and eliminate those that are not The performance of the face segmentation algorithm is illustrated by some simulation re sults carried out on various head and shoulders test images The use of face segmentation for video coding in applica tion such as videotelephony is then presented We explain how the face segmentation results can be used to improve the perceptual quality of videophone sequence encoded by the H compliant coder
Model-based object tracking in monocular image sequences of road traffic scenes

Moving vehicles are detected and tracked automatically in monocular image sequences from road traffic scenes recorded by a stationary camera. In order to exploit the a priori knowledge about shape and motion of vehicles in traffic scenes, a parameterized vehicle model is used for an intraframe matching process and a recursive estimator based on a motion model is used for motion estimation. An interpretation cycle supports the intraframe matching process with a state MAP-update step. Initial model hypotheses are generated using an image segmentation component which clusters coherently moving image features into candidate representations of images of a moving vehicle. The inclusion of an illumination model allows taking shadow edges of the vehicle into account during the matching process. Only such an elaborate combination of various techniques has enabled us to track vehicles under complex illumination conditions and over long (over 400 frames) monocular image sequences. Results on various real-world road traffic scenes are presented and open problems as well as future work are outlined.
A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update.

Most current electroencephalography (EEG)-based brain-computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs.



We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons.



We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods.



This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.
Neural-network-based signature recognition for harmonic source identification

This paper proposes a neural-network (NN)-based approach to nonintrusive harmonic source identification. In this approach, NNs are trained to extract important features from the input current waveform to uniquely identify various types of devices using their distinct harmonic "signatures". Such automated, noninvasive device identification will be critical in future power-quality monitoring and enhancement systems. Several NN-based classification models including multilayer perceptron (MLP), radial basis function (RBF) network, and support vector machines (SVM) with linear, polynomial, and RBF kernels were developed for signature extraction and device identification. These models were trained and tested using spike train data gathered from the Fourier analysis of the input current waveform in the presence of multiple devices. The performance of these models was compared in terms of their accuracy, generalization ability, and noise tolerance limits. The results showed that MLPs and SVM were both able to determine the presence of devices based on their harmonic signatures with high accuracy. MLP was found to be the best signature identification method because of its low computational requirements and ability to extract the information necessary for highly accurate device identification.
Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles

The volume of obstacles encountered in the marine environment is rapidly increasing, which makes the development of collision avoidance systems more challenging. Several fuzzy ontology-based simulators have been proposed to provide a virtual platform for the analysis of maritime missions. However, due to the simulators’ limitations, ontologybased knowledge cannot be utilized to evaluate maritime robot algorithms and to avoid collisions. The existing simulators must be equipped with smart semantic domain knowledge to provide an efficient framework for the decision-making system of AUVs. This article presents type-2 fuzzy ontology-based semantic knowledge (T2FOBSK) and a simulator for marine users that will reduce experimental time and the cost of marine robots and will evaluate algorithms intelligently. The system reformulates the user’s query to extract the positions of AUVs and obstacles and convert them to a proper format for the simulator. The simulator uses semantic knowledge to calculate the degree of collision risk and to avoid obstacles. The available type-1 fuzzy ontology-based approach cannot extract intensively blurred data from the hazy marine environment to offer actual solutions. Therefore, we propose a type-2 fuzzy ontology to provide accurate information about collision risk and the marine environment during real-time marine operations. Moreover, the type-2 fuzzy ontology is designed using Protégé OWL-2 tools. The DL query and SPARQL query are used to evaluate the ontology. The distance to closest point of approach (DCPA), time to closest point of approach (TCPA) and variation of compass degree (VCD) are used to calculate the degree of collision risk between AUVs and obstacles. The experimental and simulation results show that the proposed architecture is highly efficient and highly productive for marine missions and the real-time decision-making system of AUVs.
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification

Convolutional Neural Network (CNN)-based machine learning systems have made breakthroughs in feature extraction and image recognition tasks in two dimensions (2D). Although there is significant ongoing work to apply CNN technology to domains involving complex 3D data, the success of such efforts has been constrained, in part, by limitations in data representation techniques. Most current approaches rely upon low-resolution 3D models, strategic limitation of scope in the 3D space, or the application of lossy projection techniques to allow for the use of 2D CNNs. To address this issue, we present a mapping algorithm that converts 3D structures to 2D and 1D data grids by mapping a traversal of a 3D space-filling curve to the traversal of corresponding 2D and 1D curves. We explore the performance of 2D and 1D CNNs trained on data encoded with our method versus comparable volumetric CNNs operating upon raw 3D data from a popular benchmarking dataset. Our experiments demonstrate that both 2D and 1D representations of 3D data generated via our method preserve a significant proportion of the 3D data’s features in forms learnable by CNNs. Furthermore, we demonstrate that our method of encoding 3D data into lower-dimensional representations allows for decreased CNN training time cost, increased original 3D model rendering resolutions, and supports increased numbers of data channels when compared to purely volumetric approaches. This demonstration is accomplished in the context of a structural biology classification task wherein we train 3D, 2D, and 1D CNNs on examples of two homologous branches within the Ras protein family. The essential contribution of this paper is the introduction of a dimensionality-reduction method that may ease the application of powerful deep learning tools to domains characterized by complex structural data.
Twitter games: how successful spammers pick targets

Online social networks, such as Twitter, have soared in popularity and in turn have become attractive targets of spam. In fact, spammers have evolved their strategies to stay ahead of Twitter's anti-spam measures in this short period of time. In this paper, we investigate the strategies Twitter spammers employ to reach relevant target audiences. Due to their targeted approaches to send spam, we see evidence of a large number of the spam accounts forming relationships with other Twitter users, thereby becoming deeply embedded in the social network.
 We analyze nearly 20 million tweets from about 7 million Twitter accounts over a period of five days. We identify a set of 14,230 spam accounts that manage to live longer than the other 73% of other spam accounts in our data set. We characterize their behavior, types of tweets they use, and how they target their audience. We find that though spam campaigns changed little from a recent work by Thomas et al., spammer strategies evolved much in the same short time span, causing us to sometimes find contradictory spammer behavior from what was noted in Thomas et al.'s work. Specifically, we identify four major strategies used by 2/3rd of the spammers in our data. The most popular of these was one where spammers targeted their own followers. The availability of various kinds of services that help garner followers only increases the popularity of this strategy. The evolution in spammer strategies we observed in our work suggests that studies like ours should be undertaken frequently to keep up with spammer evolution.
Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their timefrequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANNmodels have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.
MATLAB Based BackPropagation Neural Network for Automatic Speech Recognition

Speech interface to computer is the next big step that the technology needs to take for general users. Automatic speech recognition (ASR) will play an important role in taking technology to the people. There are numerous applications of speech recognition such as direct voice input in aircraft, data entry, speech-to-text processing, voice user interfaces such as voice dialling. ASR system can be divided into two different parts, namely feature extraction and feature recognition. In this paper we present MATLAB based feature recognition using backpropagation neural network for ASR. The objective of this research is to explore how neural networks can be employed to recognize isolated-word speech as an alternative to the traditional methodologies. The general techniques developed here can be further extended to other applications such as sonar target recognition, missile tracking and classification of underwater acoustic signals. Back-propagation neural network algorithm uses input training samples and their respective desired output values to learn to recognize specific patterns, by modifying the activation values of its nodes and weights of the links connecting its nodes. Such a trained network is later used for feature recognition in ASR systems.
Malware Detection Using Integrated Static and Dynamic Analysis .

An alarming trend in malware attack’s are that they are equipped with advance sophisticated techniques to breakout from security countermeasures. Detecting a malware is challenging but combining different detection models together increases the chances by exploring malicious activity from different viewpoints, this paper proposes a collaborative static and dynamic analysis for spotting malicious behaviors of files. Malware detector is the essential tool in protecting against malware’s. The results of such detector are determined by the methodologies it uses. therefore, it is essential to investigate malware detection techniques and identify their strengths and boundaries.
Central and Peripheral Routes to Advertising Effectiveness : The Moderating Role of Involvement

Undergraduates expressed their attitudes about a product after being exposed to a magazme ad under conditions of either high or low product involvement. The ad contained either strong or weak arguments for the product and featured either prominent sports celebrities or average citizens as endorsers. The manipulation of argument quality had a greater impact on attitudes under high than low involvement, but the manipulation of product endorser had a greater impact under low than high involvement. These results are consistent with the view that there are two relatively distinct routes to persuasion.
On the Quality of the Initial Basin in Overspecified Neural Networks

Deep learning, in the form of artificial neural networks, has achieved remarkable practical success in recent years, for a variety of difficult machine learning applications. However, a theoretical explanation for this remains a major open problem, since training neural networks involves optimizing a highly non-convex objective function, and is known to be computationally hard in the worst case. In this work, we study the geometric structure of the associated non-convex objective function, in the context of ReLU networks and starting from a random initialization of the network parameters. We identify some conditions under which it becomes more favorable to optimization, in the sense of (i) High probability of initializing at a point from which there is a monotonically decreasing path to a global minimum; and (ii) High probability of initializing at a basin (suitably defined) with a small minimal objective value. A common theme in our results is that such properties are more likely to hold for larger (“overspecified”) networks, which accords with some recent empirical and theoretical observations.
NVIDIA cuda software and gpu parallel computing architecture

In the past, graphics processors were special purpose hardwired application accelerators, suitable only for conventional rasterization-style graphics applications. Modern GPUs are now fully programmable, massively parallel floating point processors. This talk will describe NVIDIA's massively multithreaded computing architecture and CUDA software for GPU computing. The architecture is a scalable, highly parallel architecture that delivers high throughput for data-intensive processing. Although not truly general-purpose processors, GPUs can now be used for a wide variety of compute-intensive applications beyond graphic.
On-line signature verification using point pattern matching algorithm

Authentication of persons has been known as a paramount part in society. Security requirements have given biometrics much attention. Verification of the signature is one of the biometric methods used in recognition systems. This paper presents a new method to verify an online signature of a person. In features extraction step, local parameters are extracted as time functions of different dynamic properties. In recognition phase the Discrete to Continuous Algorithm is applied on features to verify the signature claimed by a person. The results obtained by the proposed algorithm show a good accuracy rate.