nils-herrmann/clean_scirepeval huggingface dataset:
A Data-Based Approach to Social Influence Maximization

Influence maximization is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the network. Most of the literature on this topic has focused exclusively on the social graph, overlooking historical data, i.e., traces of past action propagations. In this paper, we study influence maximization from a novel data-based perspective. In particular, we introduce a new model, which we call credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread. Our approach also learns the different levels of influenceability of users, and it is time-aware in the sense that it takes the temporal nature of influence into account. We show that influence maximization under the credit distribution model is NP-hard and that the function that defines expected spread under our model is submodular. Based on these, we develop an approximation algorithm for solving the influence maximization problem that at once enjoys high accuracy compared to the standard approach, while being several orders of magnitude faster and more scalable.
Cross-Lingual Entity Linking for Web Tables

This paper studies the problem of linking string mentions from web tables in one language to the corresponding named entities in a knowledge base written in another language, which we call the cross-lingual table linking task. We present a joint statistical model to simultaneously link all mentions that appear in one table. The framework is based on neural networks, aiming to bridge the language gap by vector space transformation and a coherence feature that captures the correlations between entities in one table. Experimental results report that our approach improves the accuracy of cross-lingual table linking by a relative gain of 12.1%. Detailed analysis of our approach also shows a positive and important gain brought by the joint framework and coherence feature.
Lie Access Neural Turing Machine

Following the recent trend in explicit neural memory structures, we present a new design of an external memory, wherein memories are stored in an Euclidean key space R. An LSTM controller performs read and write via specialized read and write heads. It can move a head by either providing a new address in the key space (aka random access) or moving from its previous position via a Lie group action (aka Lie access). In this way, the “L” and “R” instructions of a traditional Turing Machine are generalized to arbitrary elements of a fixed Lie group action. For this reason, we name this new model the Lie Access Neural Turing Machine, or LANTM. We tested two different configurations of LANTM against an LSTM baseline in several basic experiments. We found the right configuration of LANTM to outperform the baseline in all of our experiments. In particular, we trained LANTM on addition of k-digit numbers for 2 ≤ k ≤ 16, but it was able to generalize almost perfectly to 17 ≤ k ≤ 32, all with the number of parameters 2 orders of magnitude below the LSTM baseline.
Robust motion detection using histogram of oriented gradients for illumination variations

This paper proposes a robust motion detection method for illumination variations which uses histogram of oriented gradients. The detection process is divided into two phases: coarse detection and refinement. In the coarse detection phase, first, a texture-based background model is built which implements a group of adaptive histograms of oriented gradients; then, by comparing the histogram of oriented gradients of each pixel between current frame and background model, a foreground is segmented based on texture feature which is not susceptible to illumination variations, whereas some missing foreground regions exist; finally, the result based on texture is optimized by combining the pixel-wise detection result produced by Gaussian Mixture Model (GMM) algorithm, which greatly improves the detection performance by incorporating efficient morphological operations. In the refinement phase, the above detection result is refined based on the distinction in color feature to eliminate errors like shadows, noises, redundant contour, etc. Experimental results show the effectiveness and robustness of our approach in detecting moving objects in varying illumination conditions.
CVSscan: visualization of code evolution

During the life cycle of a software system, the source code is changed many times. We study how developers can be enabled to get insight in these changes, in order to understand the status, history and structure better, as well as for instance the roles played by various contributors. We present CVSscan, an integrated multiview environment for this. Central is a line-oriented display of the changing code, where each version is represented by a column, and where the horizontal direction is used for time, Separate linked displays show various metrics, as well as the source code itself. A large variety of options is provided to visualize a number of different aspects. Informal user studies demonstrate the efficiency of this approach for real world use cases.
Blind inpainting using the fully convolutional neural network

Most of existing inpainting techniques require to know beforehandwhere those damaged pixels are, i.e., non-blind inpainting methods. However, in many applications, such information may not be readily available. In this paper, we propose a novel blind inpainting method based on a fully convolutional neural network. We term this method as blind inpainting convolutional neural network (BICNN). It purely cascades three convolutional layers to directly learn an end-to-end mapping between a pre-acquired dataset of corrupted/ground truth subimage pairs. Stochastic gradient descent with standard backpropagation is used to train the BICNN. Once the BICNN is learned, it can automatically identify and remove the corrupting patterns from a corrupted image without knowing the specific regions. The learned BICNN takes a corrupted image of any size as input and directly produces a clean output by only one pass of forward propagation. Experimental results indicate that the proposed method can achieve a better inpainting performance than the existing inpainting methods for various corrupting patterns.
Review of an Improved Audio Steganographic Technique over LSB through Random Based Approach

In this digital world, huge amount information exchange takes place due to enhanced facilities of networking. Therefore it is necessary to secure the information which we transmit. The need for secured communication introduces the concept of “Steganography”. Steganography, the word itself indicates that information within information; it is the best technique to hide the secret information by using cover objects. Secret information may be a text, image or an audio file. But as per secret information format there are different steganographic techniques are available. This paper proposes a method of audio steganographic system that provides a unique platform to hide the secret information in audio file though the information is in text, image or in an audio format. So there is no need to go for different techniques of steganography as per information format. Many steganographic methods follow the LSB insertion technique to hide the secret information. But there are many statistical techniques available to determine if a stego object has been subjected to LSB Embedding. The proposed system hides secret information in audio file through random based approach and provides security by using PKE algorithm. This paper focuses on combining the strengths of cryptography and steganography for secured communication.
Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website

This paper describes an approach and our experiences from the development, deployment and usability testing of a Natural Language Processing (NLP) and Information Retrieval system that supports the moderation of user comments on a large newspaper website. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods. We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial aspects. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting. Data and experiment code related to this paper are available for download at https://ofai.github.io/million-post-corpus.
Design of unequal H-plane waveguide-power dividers for array applications

We present design data for H-plane waveguide power dividers. Such a power divider is generally used in the feed networks of waveguide antenna arrays. The power divider is a H-plane waveguide T-junction with two irises, the first to split the incoming power from port 1 to ports 2 and 3, and the second to match the input port. An integral equation moment method analysis procedure for such a structure was developed, and then used to generate some design data. Although H-plane T-junctions have been analyzed previously, no design data, to the authors' knowledge, has yet been published for the double-iris H-plane T-junction configuration.
Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.
Towards a Deeper Understanding of Adversarial Losses

Recent work has proposed various adversarial losses for training generative adversarial networks. Yet, it remains unclear what certain types of functions are valid adversarial loss functions, and how these loss functions perform against one another. In this paper, we aim to gain a deeper understanding of adversarial losses by decoupling the effects of their component functions and regularization terms. We first derive some necessary and sufficient conditions of the component functions such that the adversarial loss is a divergence-like measure between the data and the model distributions. In order to systematically compare different adversarial losses, we then propose DANTest—a new, simple framework based on discriminative adversarial networks. With this framework, we evaluate an extensive set of adversarial losses by combining different component functions and regularization approaches. This study leads to some new insights into the adversarial losses. For reproducibility, all source code is available at https: //github.com/salu133445/dan.
Vibrotactile Display: Perception, Technology, and Applications

This paper reviews the technology and applications of vibrotactile display, an effective information transfer modality for the emerging area of haptic media. Our emphasis is on summarizing foundational knowledge in this area and providing implementation guidelines for application designers who do not yet have a background in haptics. Specifically, we explain the relevant human vibrotactile perceptual capabilities, detail the main types of commercial vibrotactile actuators, and describe how to build both monolithic and localized vibrotactile displays. We then identify exemplary vibrotactile display systems in application areas ranging from the presentation of physical object properties to broadcasting vibrotactile media content.
Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses

Detecting objects in cluttered scenes and estimating articulated human body parts from 2D images are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g., playing tennis), where the relevant objects tend to be small or only partially visible and the human body parts are often self-occluded. We observe, however, that objects and human poses can serve as mutual context to each other-recognizing one facilitates the recognition of the other. In this paper, we propose a mutual context model to jointly model objects and human poses in human-object interaction activities. In our approach, object detection provides a strong prior for better human pose estimation, while human pose estimation improves the accuracy of detecting the objects that interact with the human. On a six-class sports data set and a 24-class people interacting with musical instruments data set, we show that our mutual context model outperforms state of the art in detecting very difficult objects and estimating human poses, as well as classifying human-object interaction activities.
Reward Systems in the Brain and Nutrition.

The taste cortex in the anterior insula provides separate and combined representations of the taste, temperature, and texture of food in the mouth independently of hunger and thus of reward value and pleasantness. One synapse on, in the orbitofrontal cortex, these sensory inputs are combined by associative learning with olfactory and visual inputs for some neurons, and these neurons encode food reward value in that they respond to food only when hunger is present and in that activations correlate linearly with subjective pleasantness. Cognitive factors, including word-level descriptions and selective attention to affective value, modulate the representation of the reward value of taste, olfactory, and flavor stimuli in the orbitofrontal cortex and a region to which it projects, the anterior cingulate cortex. These food reward representations are important in the control of appetite and food intake. Individual differences in reward representations may contribute to obesity, and there are age-related differences in these reward representations. Implications of how reward systems in the brain operate for understanding, preventing, and treating obesity are described.
Using the focus group method in software engineering: obtaining practitioner and user experiences

This paper reflects on three cases where the focus group method was used to obtain feedback and experiences from software engineering practitioners and application users. The focus group method and its background are presented, the method's weaknesses and strengths are discussed, and guidelines are provided for how to use the method in the software engineering context. Furthermore, the results of the three studies conducted are highlighted and the paper concludes in a discussion on the applicability of the method for this type of research. In summary, the focus group method is a cost-effective and quick empirical research approach for obtaining qualitative insights and feedback from practitioners. It can be used in several phases and types of research. However, a major limitation of the method is that it is useful only in studying concepts that can be understood by participants in a limited time. We also recommend that in the software engineering context, the method should be used with sufficient empirical rigor.
Network-based wireless location: challenges faced in developing techniques for accurate wireless location information

Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety feature of cellular systems in response to an order issued by the Federal Communications Commission (FCC) in 1996. The FCC mandate aims to solve a serious public safety problem caused by the fact that, at present, a large proportion of all 911 calls originate from mobile phones, the location of which cannot be determined with the existing technology. However, many difficulties intrinsic to the wireless environment make meeting the FCC objective challenging. These challenges include channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions. In addition to emergency services, there are many other applications for wireless location technology, including monitoring and tracking for security reasons, location sensitive billing, fraud protection, asset tracking, fleet management, intelligent transportation systems, mobile yellow pages, and even cellular system design and management. This article provides an overview of wireless location challenges and techniques with a special focus on network-based technologies and applications.
Advanced Gate Drive Unit With Closed-Loop $di_{{C}}/dt$  Control

This paper describes the design and the experimental investigation of a gate drive unit with closed-loop control of the collector current slope diC/dt for multichip insulated-gate bipolar transistors (IGBTs). Compared to a pure resistive gate drive, the proposed diC/dt control offers the ability to adjust the collector current slope freely which helps to find an optimized relation between switching losses and secure operation of the freewheeling diode for every type of IGBT. Based on the description of IGBT's switching behavior, the design and the realization of the gate drive are presented. The test setup and the comparison of switching tests with and without the proposed diC/dt control are discussed.
Object Detection Using the Statistics of Parts

In this paper we describe a trainable object detector and its instantiations for detecting faces and cars at any size, location, and pose. To cope with variation in object orientation, the detector uses multiple classifiers, each spanning a different range of orientation. Each of these classifiers determines whether the object is present at a specified size within a fixed-size image window. To find the object at any location and size, these classifiers scan the image exhaustively. Each classifier is based on the statistics of localized parts. Each part is a transform from a subset of wavelet coefficients to a discrete set of values. Such parts are designed to capture various combinations of locality in space, frequency, and orientation. In building each classifier, we gathered the class-conditional statistics of these part values from representative samples of object and non-object images. We trained each classifier to minimize classification error on the training set by using Adaboost with Confidence-Weighted Predictions (Shapire and Singer, 1999). In detection, each classifier computes the part values within the image window and looks up their associated class-conditional probabilities. The classifier then makes a decision by applying a likelihood ratio test. For efficiency, the classifier evaluates this likelihood ratio in stages. At each stage, the classifier compares the partial likelihood ratio to a threshold and makes a decision about whether to cease evaluation—labeling the input as non-object—or to continue further evaluation. The detector orders these stages of evaluation from a low-resolution to a high-resolution search of the image. Our trainable object detector achieves reliable and efficient detection of human faces and passenger cars with out-of-plane rotation.
Automatic Driving on Ill-defined Roads: An Adaptive, Shape-constrained, Color-based Method

Autonomous following of ill-defined roads is an important part of visual navigation systems. This paper presents an adaptive method that uses a statistical model of the colour of the road surface within a trapezoidal shape that approximately corresponds to the projection of the road on the image plane. The method does not perform an explicit segmentation of the images but instead expands the shape sideways until the match between shape and road worsens, simultaneously computing the colour statistics. Results show that the method is capable of reactively following roads, at driving speeds typical of the robots used, in a variety of situations while coping with variable conditions of the road such as surface type, puddles and shadows. We extensively evaluate the proposed method using a large number of datasets with ground truth (that will be made public on our server once the paper is published). We moreover evaluate many colour spaces in the context of road following and find that the colour spaces that separate luminance from colour information perform best, especially if the luminance information is discarded.
Improving risk-adjusted performance in high-frequency trading: the role of fuzzy logic systems

In recent years, high-frequency and algorithmic trading have been the subject of increasing risk concerns. A general theme that we adopt in this thesis is that trading practitioners are predominantly interested in risk-adjusted performance. Likewise, regulators are demanding stricter risk controls. First, we scrutinise different AI model design approaches with the aim to increase the time-varying risk-adjusted performance of trading systems. We show that accounting for transaction costs and using risk-return objective functions improve out-of-sample results. Our experiments identify that neuro-fuzzy logic models exhibit superior performance stability across multiple risk regimes when compared to other common models in AI literature. Moreover, we propose an innovative ensemble model approach which combines multiple risk-adjusted objective functions and dynamically adapts risk-tolerance according to timevarying intraday risk. Next, we extend our initial findings to the money management aspects of trading algorithms. We propose an innovative fuzzy logic approach which dynamically discriminates across different regions in the trend and volatility space. The model prioritises higher performing regions at an intraday level and adapts capital allocation policies with the objective to maximise global risk-adjusted performance without penalising profitability, rather increase it. Finally, we explore viable trading improvements that can be attained by advancing our type-1 fuzzy logic ideas to higher order fuzzy systems in view of the increased noise