nils-herrmann/clean_scirepeval huggingface dataset:
Animated Exploration of Graphs with Radial Layout

We describe a new animation technique for supporting interactive exploration of a graph, building on the wellknown radial tree layout method. When a node is selected to become the center of interest, our visualization performs an animated transition to a new layout. Our approach is to linearly interpolate the polar coordinates of the nodes, while enforcing constraints on the layout to keep the transition easy to follow. We apply this technique to visualizations of social networks and of the Gnutella file-sharing network, and discuss our findings and usability results.
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms

Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classification accuracy, training time, and (in the case of trees) number of leaves. Classification accuracy is measured by mean error rate and mean rank of error rate. Both criteria place a statistical, spline-based, algorithm called POLYCLSSS at the top, although it is not statistically significantly different from twenty other algorithms. Another statistical algorithm, logistic regression, is second with respect to the two accuracy criteria. The most accurate decision tree algorithm is QUEST with linear splits, which ranks fourth and fifth, respectively. Although spline-based statistical algorithms tend to have good accuracy, they also require relatively long training times. POLYCLASS, for example, is third last in terms of median training time. It often requires hours of training compared to seconds for other algorithms. The QUEST and logistic regression algorithms are substantially faster. Among decision tree algorithms with univariate splits, C4.5, IND-CART, and QUEST have the best combinations of error rate and speed. But C4.5 tends to produce trees with twice as many leaves as those from IND-CART and QUEST.
Enhancing Communication, Facilitating Shared Understanding, and Creating Better Artifacts by Integrating Physical and Computational Media for Design

Frequently, the design of interactive systems focuses exclusively on the capabilities provided by the dynamic nature of computational media. Yet our experience includes many examples in which physical models provide certain strengths not found in computational models. Rather than viewing this as a dichotomy—where one must choose between one or the other—we are exploring the creation of computational environments that build on the strengths of combined physical and virtual approaches. Over the last decade, we have developed different design environments to support stakeholders engaged in design processes by enhancing communication, facilitating shared understanding, and creating better artifacts. Until a few years ago, our work explored physical and computational media separately. In this paper we present our efforts to develop integrated design environments linking physical and computational dimensions to attain the complementary synergies that these two worlds offer. Our purpose behind this integration is the development of systems that can enhance the movement from conceptual thinking to concrete representations using face-to-face interaction to promote the negotiation of meaning, the direct interaction with artifacts, and the possibility that diverse stakeholders can participate fully in the process of design. To this end, we analyze the strengths, affordances, weaknesses, and limitations of the two media used separately and illustrate with our most recent work the value added by integrating these environments.
Physis: An implicitly parallel programming model for stencil computations on large-scale GPU-accelerated supercomputers

This paper proposes a compiler-based programming framework that automatically translates user-written structured grid code into scalable parallel implementation code for GPU-equipped clusters. To enable such automatic translations, we design a small set of declarative constructs that allow the user to express stencil computations in a portable and implicitly parallel manner. Our framework translates the user-written code into actual implementation code in CUDA for GPU acceleration and MPI for node-level parallelization with automatic optimizations such as computation and communication overlapping. We demonstrate the feasibility of such automatic translations by implementing several structured grid applications in our framework. Experimental results on the TSUBAME2.0 GPU-based supercomputer show that the performance is comparable as hand-written code and good strong and weak scalability up to 256 GPUs.
Elastic functional coding of human actions: From vector-fields to latent variables

Human activities observed from visual sensors often give rise to a sequence of smoothly varying features. In many cases, the space of features can be formally defined as a manifold, where the action becomes a trajectory on the manifold. Such trajectories are high dimensional in addition to being non-linear, which can severely limit computations on them. We also argue that by their nature, human actions themselves lie on a much lower dimensional manifold compared to the high dimensional feature space. Learning an accurate low dimensional embedding for actions could have a huge impact in the areas of efficient search and retrieval, visualization, learning, and recognition. Traditional manifold learning addresses this problem for static points in ℝn, but its extension to trajectories on Riemannian manifolds is non-trivial and has remained unexplored. The challenge arises due to the inherent non-linearity, and temporal variability that can significantly distort the distance metric between trajectories. To address these issues we use the transport square-root velocity function (TSRVF) space, a recently proposed representation that provides a metric which has favorable theoretical properties such as invariance to group action. We propose to learn the low dimensional embedding with a manifold functional variant of principal component analysis (mfPCA). We show that mf-PCA effectively models the manifold trajectories in several applications such as action recognition, clustering and diverse sequence sampling while reducing the dimensionality by a factor of ~ 250×. The mfPCA features can also be reconstructed back to the original manifold to allow for easy visualization of the latent variable space.
Path to Purchase: A Mutually Exciting Point Process Model for Online Advertising and Conversion

This paper studies the e ects of various types of online advertisements on purchase conversion by capturing the dynamic interactions among advertisement clicks themselves. It is motivated by the observation that certain advertisement clicks may not result in immediate purchases, but they stimulate subsequent clicks on other advertisements which then lead to purchases. We develop a stochastic model based on mutually exciting point processes, which model advertisement clicks and purchases as dependent random events in continuous time. We incorporate individual random e ects to account for consumer heterogeneity and cast the model in the Bayesian hierarchical framework. We propose a new metric of conversion probability to measure the conversion e ects of online advertisements. Simulation algorithms for mutually exciting point processes are developed to evaluate the conversion probability and for out-of-sample prediction. Model comparison results show the proposed model outperforms the benchmark model that ignores exciting e ects among advertisement clicks. We nd that display advertisements have relatively low direct e ect on purchase conversion, but they are more likely to stimulate subsequent visits through other advertisement formats. We show that the commonly used measure of conversion rate is biased in favor of search advertisements and underestimates the conversion e ect of display advertisements the most. Our model also furnishes a useful tool to predict future purchases and clicks on online
Fast Pedestrian Detection Based on HOG-PCA and Gentle AdaBoost

Pedestrian detection is a major difficulty in the field of object detection. In order to achieve a balance between speed and accuracy, we propose a new framework in pedestrian detection based on HOG-PCA and Gentle AdaBoost. Firstly, each block-based feature of the image is encoded using the histograms of oriented gradients (HOG), then Principal Components Analysis (PCA) is used to reduce the dimensions of the HOG feature set. In the end, Gentle AdaBoost is used to classify the fabric defects. HOG-PCA descriptors can reduce the complexity of computation in contrast to the state-of-the-art algorithms. The Gentle AdaBoost is used to train the pedestrian classifier can improve the efficiency of training. Experimental results demonstrate the robust of our proposed algorithm.
Generative Moment Matching Networks

We consider the problem of learning deep generative models from data. We formulate a method that generates an independent sample via a single feedforward pass through a multilayer preceptron, as in the recently proposed generative adversarial networks (Goodfellow et al., 2014). Training a generative adversarial network, however, requires careful optimization of a difficult minimax program. Instead, we utilize a technique from statistical hypothesis testing known as maximum mean discrepancy (MMD), which leads to a simple objective that can be interpreted as matching all orders of statistics between a dataset and samples from the model, and can be trained by backpropagation. We further boost the performance of this approach by combining our generative network with an auto-encoder network, using MMD to learn to generate codes that can then be decoded to produce samples. We show that the combination of these techniques yields excellent generative models compared to baseline approaches as measured on MNIST and the Toronto Face Database.
Advanced dicing technology for semiconductor wafer -Stealth Dicing

"Stealth Dicing (SD) " was developed to solve such inherent problems of dicing process as debris contaminants and unnecessary thermal damage on work wafer. In SD, laser beam power of transmissible wavelength is absorbed only around focal point in the wafer by utilizing temperature dependence of absorption coefficient of the wafer. And these absorbed power forms modified layer in the wafer, which functions as the origin of separation in followed separation process. Since only the limited interior region of a wafer is processed by laser beam irradiation, damages and debris contaminants can be avoided in SD. Besides characteristics of devices will not be affected. Completely dry process of SD is another big advantage over other dicing methods.
Drone sound detection

Nowadays small size UAV (Unmanned Aerial Vehicle) systems are really popular. We can find so many of them on video sharing portals and nowadays there are many articles on the media with the advantages and disadvantages of them. We can use “drones” for so many useful task, but on the other hand the risk is increasing with the growing popularity. We can found articles about the unethical and unlawful usages of them. They can be used for smuggling, observation, violation of privacy etc. As a consequence of the growing popularity, the price of these things is quickly becoming lower. High quality cameras are also widespread and it's also easy to install one onto a commercially available drone. We have to admit that these factors are become increasingly critical.
Image Retrieval for Image-Based Localization Revisited

Image-based localization is the task of determining the exact location from which a query photo was taken. In this paper, we consider imagebased localization relative to a 3D point cloud of a scene, obtained from Structure-from-Motion, which allows an accurate estimate of the full camera pose from correspondences between 2D features and 3D points. To quickly establish the required 2D-to-3D matches, Irschara et al. use image retrieval methods  to find database images (used for the reconstruction) similar to the query image . Since the relation between 2D features and 3D points is known for the database images, the correspondences for the query image can be computed by feature matching between images. Recent work has demonstrated that directly matching the features against the points outperforms retrieval-based methods in terms of the number of images that can be localized successfully [4]. Yet, direct matching is inherently less scalable than retrieval-based approaches since it needs to keep SIFT descriptors  in memory at all times. In this paper, we therefore analyze the algorithmic factors that cause the gap in registration performance. We show that using selective voting schemes enable retrieval methods to outperform state-of-the-art direct matching methods and explore how both selective voting and correspondence search can be accelerated by using a Hamming embedding .
Industry 4.0 - Potentials for Creating Smart Products: Empirical Research Results

Industry 4.0 combines the strengths of traditional industries with cutting edge internet technologies. It embraces a set of technologies enabling smart products integrated into intertwined digital and physical processes. Therefore, many companies face the challenge to assess the diversity of developments and concepts summarized under the term industry 4.0. The paper presents the result of a study on the potential of industry 4.0. The use of current technologies like Big Data or cloud-computing are drivers for the individual potential of use of Industry 4.0. Furthermore mass customization as well as the use of idle data and production time improvement are strong influence factors to the potential of Industry 4.0. On the other hand business process complexity has a negative influence.
Joint Semantic Segmentation and 3D Reconstruction from Monocular Video

Output of our system is a 3D semantic+occupancy map. However due to lack of ground truth in that form, we need to evaluate using indirect approaches. To evaluate the segmentation accuracy, we evaluated it with standard 2D semantic segmentation methods for which human annotated ground truth exists. The 2D segmentation is obtained by back-projecting our 3D map to the camera images. However these kind of evaluation negatively harms our scores for the following reasons:
CiteSeer: An Autonous Web Agent for Automatic Retrieval and Identification of Interesting Publications

Research papers available on the World Wide Web (WWW or Web) areoften poorly organized, often exist in forms opaque to searchengines (e.g. Postscript), and increase in quantity daily.Significant amounts of time and effort are typically needed inorder to find interesting and relevant publications on the Web. Wehave developed a Web based information agent that assists the userin the process of performing a scientific literature search. Givena set of keywords, the agent uses Web search engines and heuristicsto locate and download papers. The papers are parsed in order toextract information features such as the abstract and individuallyidentified citations. The agents Web interface can be used to findrelevant papers in the database using keyword searches, or bynavigating the links between papers formed by the citations. Linksto both citing and cited publications can be followed. In additionto simple browsing and keyword searches, the agent can find paperswhich are similar to a given paper using word information and byanalyzing common citations made by the papers.
Collaborative factorization for recommender systems

Recommender system has become an effective tool for information filtering, which usually provides the most useful items to users by a top-k ranking list. Traditional recommendation techniques such as Nearest Neighbors (NN) and Matrix Factorization (MF) have been widely used in real recommender systems. However, neither approaches can well accomplish recommendation task since that: (1) most NN methods leverage the neighbor's behaviors for prediction, which may suffer the severe data sparsity problem; (2) MF methods are less sensitive to sparsity, but neighbors' influences on latent factors are not fully explored, since the latent factors are often used independently. To overcome the above problems, we propose a new framework for recommender systems, called collaborative factorization. It expresses the user as the combination of his own factors and those of the neighbors', called collaborative latent factors, and a ranking loss is then utilized for optimization. The advantage of our approach is that it can both enjoy the merits of NN and MF methods. In this paper, we take the logistic loss in RankNet and the likelihood loss in ListMLE as examples, and the corresponding collaborative factorization methods are called CoF-Net and CoF-MLE. Our experimental results on three benchmark datasets show that they are more effective than several state-of-the-art recommendation methods.
ModChecker: Kernel Module Integrity Checking in the Cloud Environment

Kernel modules are an integral part of most operating systems (OS) as they provide flexible ways of adding new functionalities (such as file system or hardware support) to the kernel without the need to recompile or reload the entire kernel. Aside from providing an interface between the user and the hardware, these modules maintain system security and reliability. Malicious kernel level exploits (e.g. code injections) provide a gateway to a system's privileged level where the attacker has access to an entire system. Such attacks may be detected by performing code integrity checks. Several commodity operating systems (such as Linux variants and MS Windows) maintain signatures of different pieces of kernel code in a database for code integrity checking purposes. However, it quickly becomes cumbersome and time consuming to maintain a database of legitimate dynamic changes in the code, such as regular module updates. In this paper we present Mod Checker, which checks in-memory kernel modules' code integrity in real time without maintaining a database of hashes. Our solution applies to virtual environments that have multiple virtual machines (VMs) running the same version of the operating system, an environment commonly found in large cloud servers. Mod Checker compares kernel module among a pool of VMs within a cloud. We thoroughly evaluate the effectiveness and runtime performance of Mod Checker and conclude that Mod Checker is able to detect any change in a kernel module's headers and executable content with minimal or no impact on the guest operating systems' performance.
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition

Temporal information has useful features for recognizing facial expressions. However, to manually design useful features requires a lot of effort. In this paper, to reduce this effort, a deep learning technique, which is regarded as a tool to automatically extract useful features from raw data, is adopted. Our deep network is based on two different models. The first deep network extracts temporal appearance features from image sequences, while the other deep network extracts temporal geometry features from temporal facial landmark points. These two models are combined using a new integration method in order to boost the performance of the facial expression recognition. Through several experiments, we show that the two models cooperate with each other. As a result, we achieve superior performance to other state-of-the-art methods in the CK+ and Oulu-CASIA databases. Furthermore, we show that our new integration method gives more accurate results than traditional methods, such as a weighted summation and a feature concatenation method.
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world applications, such as image captioning and news video analysis. Associated with this task, we design and provide concrete measurement set, evaluation protocol, as well as training data. We also present in details our experiment setup and report promising baseline results. Our benchmark task could lead to one of the largest classification problems in computer vision. To the best of our knowledge, our training dataset, which contains 10M images in version 1, is the largest publicly available one in the world.
An innovation - diffusion view of implementation of enterprise resource planning (ERP) systems and development of a research model

Firms around the world have been implementing enterprise resource planning (ERP) systems since the 1990s to have an uniform information system in their respective organizations and to reengineer their business processes. Through a case type analysis conducted in six manufacturing firms that have one of the widely used ERP systems, various contextual factors that influenced these firms to implement this technology were understood using the six-stage model proposed by Kwon and Zmud. Three types of ERP systems, viz. SAP, Baan and Oracle ERP were studied in this research. Implementation of ERP systems was found to follow the stage model. The findings from the process model were used to develop the items for the causal model and in identifying appropriate constructs to group those items. In order to substantiate that the constructs developed to measure the causal model were congruent with the findings based on qualitative analysis, i.e. that the instrument appropriately reflects the understanding of the case interview; ‘triangulation’ technique was used. The findings from the qualitative study and the results from the quantitative study were found to be equivalent, thus, ensuring a fair assessment of the validity and reliability of the instrument developed to test the causal model. The quantitative measures done only at these six firms are not statistically significant but the samples were used as a part of the triangulation method to collect data from multiple sources, to verify the respondents’ understanding of the scales and as an initial measure to see if my understanding from the qualitative studies were accurately reflected by the instrument. This instrument will be pilot tested first and administered to a large sample of firms.
AddressSanitizer: A Fast Address Sanity Checker

Memory access bugs, including buffer overflows and uses of freed heap memory, remain a serious problem for programming languages like C and C++. Many memory error detectors exist, but most of them are either slow or detect a limited set of bugs, or both. This paper presents AddressSanitizer, a new memory error detector. Our tool finds out-of-bounds accesses to heap, stack, and global objects, as well as use-after-free bugs. It employs a specialized memory allocator and code instrumentation that is simple enough to be implemented in any compiler, binary translation system, or even in hardware. AddressSanitizer achieves efficiency without sacrificing comprehensiveness. Its average slowdown is just 73% yet it accurately detects bugs at the point of occurrence. It has found over 300 previously unknown bugs in the Chromium browser and many bugs in other software.