nils-herrmann/clean_scirepeval huggingface dataset:
Domain Ontology Induction Using Word Embeddings

Ontology, the shared formal conceptualization of domain information, has been shown to have multiple applications in modeling, processing and understanding natural language text. In this work, we use distributed word vectors out of various recent language models from Deep Learning for semi-automated domain ontology creation for closed domains. We cover all major aspects of Domain Ontology Induction or Learning like concept identification, attribute identification, taxonomical and non-taxonomical relationship identification using the distributed word vectors. Preliminary results show that simple clustering based methods using distributed word vectors from these language models outperforms methods using models like LSI in ontology learning for closed domains.
Integrating learning and reasoning services for explainable information fusion

We present a distributed information fusion system able to integrate heterogeneous information processing services based on machine learning and reasoning approaches. We focus on higher (semantic) levels of information fusion, and highlight the requirement for the component services, and the system as a whole, to generate explanations of its outputs. Using a case study approach in the domain of traffic monitoring, we introduce component services based on (i) deep neural network approaches and (ii) heuristic-based reasoning. We examine methods for explanation generation in each case, including both transparency (e.g, saliency maps, reasoning traces) and post-hoc methods (e.g, explanation in terms of similar examples, identification of relevant semantic objects). We consider trade-offs in terms of the classification performance of the services and the kinds of available explanations, and show how service integration offers more robust performance and explainability.
MixTrain: Scalable Training of Formally Robust Neural Networks

There is an arms race to defend neural networks against adversarial examples. Notably, adversarially robust training and verifiably robust training are the most promising defenses. The adversarially robust training scales well but cannot provide provable robustness guarantee for the absence of attacks. We present an Interval Attack that reveals fundamental problems about the threat model used by adversarially robust training. On the contrary, verifiably robust training achieves sound guarantee, but it is computationally expensive and sacrifices accuracy, which prevents it being applied in practice. In this paper, we propose two novel techniques for verifiably robust training, stochastic output approximation and dynamic mixed training, to solve the aforementioned challenges. They are based on two critical insights: (1) soundness is only needed in a subset of training data; and (2) verifiable robustness and test accuracy are conflicting to achieve after a certain point of verifiably robust training. On both MNIST and CIFAR datasets, we are able to achieve similar test accuracy and estimated robust accuracy against PGD attacks within 14× less training time compared to state-of-theart adversarially robust training techniques. In addition, we have up to 95.2% verified robust accuracy as a bonus. Also, to achieve similar verified robust accuracy, we are able to save up to 5× computation time and offer 9.2% test accuracy improvement compared to current state-of-the-art verifiably robust training techniques.
Deriving HD maps for highly automated driving from vehicular probe data

High definition (HD) map data is a key feature to enable highly automated driving. With the advent of highly automated vehicles, car makers and map suppliers investigate new approaches to create and maintain HD maps by using on-board sensor data of series vehicles. While state-of-the-art-approaches focus on position and speed data analysis, the consideration of additional vehicle sensor data allows for novel approaches in the context of HD maps. By 2020, more than 30 million connected vehicles are expected to be sold per year, which will generate millions of terabytes of vehicular probe data. One of the major upcoming research issues is to find methods to exploit that probe data to generate and maintain HD maps. In this paper, we address how to develop such methods. We introduce a scalable infrastructure, which supports the ingestion, management and analysis of huge amounts of probe data. It supports an iterative process to develop, assess and tune methods for generating HD maps from probe data. We present a metric to assess methods regarding resulting map precision. As a proof of concept, we present an approach to derive road geometry of highways from location and sensor information.
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging

In this paper we study a first-order primal-dual algorithm for non-smooth convex optimization problems with known saddle-point structure. We prove convergence to a saddle-point with rate O(1/N) in finite dimensions for the complete class of problems. We further show accelerations of the proposed algorithm to yield improved rates on problems with some degree of smoothness. In particular we show that we can achieve O(1/N 2) convergence on problems, where the primal or the dual objective is uniformly convex, and we can show linear convergence, i.e. O(ω N ) for some ω∈(0,1), on smooth problems. The wide applicability of the proposed algorithm is demonstrated on several imaging problems such as image denoising, image deconvolution, image inpainting, motion estimation and multi-label image segmentation.
How do people solve the "weather prediction" task?: individual variability in strategies for probabilistic category learning.

Probabilistic category learning is often assumed to be an incrementally learned cognitive skill, dependent on nondeclarative memory systems. One paradigm in particular, the weather prediction task, has been used in over half a dozen neuropsychological and neuroimaging studies to date. Because of the growing interest in using this task and others like it as behavioral tools for studying the cognitive neuroscience of cognitive skill learning, it becomes especially important to understand how subjects solve this kind of task and whether all subjects learn it in the same way. We present here new experimental and theoretical analyses of the weather prediction task that indicate that there are at least three different strategies that describe how subjects learn this task. (1) An optimal multi-cue strategy, in which they respond to each pattern on the basis of associations of all four cues with each outcome; (2) a one-cue strategy, in which they respond on the basis of presence or absence of a single cue, disregarding all other cues; or (3) a singleton strategy, in which they learn only about the four patterns that have only one cue present and all others absent. This variability in how subjects approach this task may have important implications for interpreting how different brain regions are involved in probabilistic category learning.
Parametrization of General Catmull-Clark Subdivision Surfaces and its Applications

A new parametrization technique and its applications for general Catmull-Clark subdivision surfaces are presented. The new technique extends J. Stam's work by redefining all the eigen basis functions in the parametric representation for general Catmull-Clark subdivision surfaces and giving each of them an explicit form. The entire eigenstructure of the subdivision matrix and its inverse are computed exactly and explicitly with no need to precompute anything. Therefore, the new representation can be used not only for evaluation purpose, but for analysis purpose as well. The new approach is based on an Ω-partition of the parameter space and a detoured subdivision path. This results in a block diagonal matrix with constant size diagonal blocks (7×7) for the corresponding subdivision process. Consequently, eigen decomposition of the matrix is always possible and is simpler and more efficient. Furthermore, since the number of eigen basis functions required in the new approach is only one half of the previous approach, the new parametrization is also more efficient for evaluation purpose. This is demonstrated by several applications of the new techniques.
Playful public participation in urban planning: A case study for online serious games

The aim of this paper is to study the implementation of online games to encourage public participation in urban planning. Its theoretical foundations are based on previous work in public participatory geographical information systems (PP GISs), play and games, with a special focus on serious games. Serious games aim to support learning processes in a new, more playful way. We developed the concept of playful public participation in urban planning, including playful elements such as storytelling, walking and moving, sketching, drawing, and games. A group of students designed an online serious public participatory game entitled NextCampus. The case study used in NextCampus was taken from the real-world question of a possible move of a university campus to a new location in the city of Hamburg, Germany. The development of the serious public participatory game NextCampus resulted in a physical prototype, user interface design, and a computational model of the game. The NextCampus game was tested with the help of two groups of urban planning students and presented to three external experts who provided valuable recommendations for further development. The critical comments questioned the level of complexity involved in such games. The positive comments included recognition of the potential for joy and the play-fulness a game like NextCampus could evoke. Public participatory online applications aim to attract citizens to discuss current issues related to their environment and to improve the process of public participation in general. An integration of geographic information systems (GISs) with public participatory tools represents one of the latest innovations in this area. ways of integrating the new applications into participatory processes and considers which new functionalities and technical characteristics could offer the most benefit to users. In the past, these technologies and other map-based applications were frequently criticized as being too complex for the majority of potential users (Steinmann, Krek, & Blaschke, 2004). New forms of collaboration and technical solutions emerged during the Web 2.0 era. For example , Google Maps and Google Earth can be used by lay users and non-experts without intense training. Recent research on collabo-rative mapping also known as ''geography without geographers'' prior work in PP GIS to include much wider, distributed participation (Hardy, 2008). Despite these new forms of collaboration and innovative technologies , Moody (2007) demonstrates that the use of GIS technology to involve citizens in participatory urban planning does not seem to empower citizens. An important factor in such findings …
Using space and time to encode vibrotactile information: toward an estimate of the skin’s achievable throughput

Touch receptors in the skin can relay various forms of abstract information, such as words (Braille), haptic feedback (cell phones, game controllers, feedback for prosthetic control), and basic visual information such as edges and shape (sensory substitution devices). The skin can support such applications with ease: They are all low bandwidth and do not require a fine temporal acuity. But what of high-throughput applications? We use sound-to-touch conversion as a motivating example, though others abound (e.g., vision, stock market data). In the past, vibrotactile hearing aids have demonstrated improvement in speech perceptions in the deaf. However, a sound-to-touch sensory substitution device that works with high efficacy and without the aid of lipreading has yet to be developed. Is this because skin simply does not have the capacity to effectively relay high-throughput streams such as sound? Or is this because the spatial and temporal properties of skin have not been leveraged to full advantage? Here, we begin to address these questions with two experiments. First, we seek to determine the best method of relaying information through the skin using an identification task on the lower back. We find that vibrotactile patterns encoding information in both space and time yield the best overall information transfer estimate. Patterns encoded in space and time or “intensity” (the coupled coding of vibration frequency and force) both far exceed performance of only spatially encoded patterns. Next, we determine the vibrotactile two-tacton resolution on the lower back—the distance necessary for resolving two vibrotactile patterns. We find that our vibratory motors conservatively require at least 6 cm of separation to resolve two independent tactile patterns (>80 % correct), regardless of stimulus type (e.g., spatiotemporal “sweeps” versus single vibratory pulses). Six centimeter is a greater distance than the inter-motor distances used in Experiment 1 (2.5 cm), which explains the poor identification performance of spatially encoded patterns. Hence, when using an array of vibrational motors, spatiotemporal sweeps can overcome the limitations of vibrotactile two-tacton resolution. The results provide the first steps toward obtaining a realistic estimate of the skin’s achievable throughput, illustrating the best ways to encode data to the skin (using as many dimensions as possible) and how far such interfaces would need to be separated if using multiple arrays in parallel.
Modeling Multibody Dynamic Systems With Uncertainties . Part II : Numerical Applications

This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper “Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects”. In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set. The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties.
Are Coherence Protocol States Vulnerable to Information Leakage?

Most commercial multi-core processors incorporate hardware coherence protocols to support efficient data transfers and updates between their constituent cores. While hardware coherence protocols provide immense benefits for application performance by removing the burden of software-based coherence, we note that understanding the security vulnerabilities posed by such oft-used, widely-adopted processor features is critical for secure processor designs in the future. In this paper, we demonstrate a new vulnerability exposed by cache coherence protocol states. We present novel insights into how adversaries could cleverly manipulate the coherence states on shared cache blocks, and construct covert timing channels to illegitimately communicate secrets to the spy. We demonstrate 6 different practical scenarios for covert timing channel construction. In contrast to prior works, we assume a broader adversary model where the trojan and spy can either exploit explicitly shared read-only physical pages (e.g., shared library code), or use memory deduplication feature to implicitly force create shared physical pages. We demonstrate how adversaries can manipulate combinations of coherence states and data placement in different caches to construct timing channels. We also explore how adversaries could exploit multiple caches and their associated coherence states to improve transmission bandwidth with symbols encoding multiple bits. Our experimental results on commercial systems show that the peak transmission bandwidths of these covert timing channels can vary between 700 to 1100 Kbits/sec. To the best of our knowledge, our study is the first to highlight the vulnerability of hardware cache coherence protocols to timing channels that can help computer architects to craft effective defenses against exploits on such critical processor features.
IntEx: A Syntactic Role Driven Protein-Protein Interaction Extractor for Bio-Medical Text

In this paper, we present a fully automated extraction system, named IntEx, to identify gene and protein interactions in biomedical text. Our approach is based on first splitting complex sentences into simple clausal structures made up of syntactic roles. Then, tagging biological entities with the help of biomedical and linguistic ontologies. Finally, extracting complete interactions by analyzing the matching contents of syntactic roles and their linguistically significant combinations. Our extraction system handles complex sentences and extracts multiple and nested interactions specified in a sentence. Experimental evaluations with two other state of the art extraction systems indicate that the IntEx system achieves better performance without the labor intensive pattern engineering requirement.
Analysis of LSB based image steganography techniques

But practical implementation of the definition is not feasible, so pragmatic approach would be to make the algorithm as strong as possible. Steganography is most widely formulated in terms of the prisoner’s problem where Alice and Bob are two inmates who wish to communicate in order to hatch an escape plan. However, all communication between them is examined by the warden, Wendy, who will put them in solitary confinement at the slightest suspicion of trouble. Specifically, in the general model for steganography, we have Alice wishing to send a secret message M to Bob. In order to do so she ”embeds” M into a cover-object C, to obtain the stego-object S. The stego-object S is then sent through a public channel. The warden Wendy who is free to examine all messages exchanged between Alice and Bob can be passive or active. A passive warden simply examines the message and tries to determine if it potentially contains a hidden message. If it appears that it does, then she takes appropriate action else she lets the message through without alteration. An active warden on the other hand can alter messages deliberately, even though she does not see any trace of a hidden message, in order to foil any secret communication that can nevertheless be occurring between Alice and Bob.
A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models

In this paper, we propose the new fixedsize ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFE can model the word order in a sequence using a simple ordinally-forgetting mechanism according to the positions of words. In this work, we have applied FOFE to feedforward neural network language models (FNN-LMs). Experimental results have shown that without using any recurrent feedbacks, FOFE based FNNLMs can significantly outperform not only the standard fixed-input FNN-LMs but also the popular recurrent neural network (RNN) LMs.
A Hierarchical Approach for Fast and Robust Ellipse Extraction

This paper presents a hierarchical approach for fast and robust ellipse extraction from images. At the lowest level, the image is described as a set of edge pixels, from which line segments are extracted. Then, line segments that are potential candidates of elliptic arcs are linked to form arc segments according to connectivity and curvature relations. After that, arc segments that belong to the same ellipse are grouped together. Finally, a robust statistical method, namely RANSAC, is applied to fit ellipses. This method does not need a high dimensional parameter space like Hough transform based algorithms, and so it reduces the computation and memory requirements. Experiments on both synthetic and real images demonstrate that the proposed method has excellent performance in handling occlusion and overlapping ellipses.
Design and fabrication of closed loop Two-Phase Interleaved Boost Converter with Type-III controller

This paper presents design, fabrication and testing of a Two-Phase Interleaved Boost Converter (IBC) with Type-III controller by utilizing voltage mode control. The small-signal modeling of IBC is derived in continuous conduction mode (CCM) of operation. A right half plane (RHP) zero is appeared in the control to output plant transfer function of the IBC. Due to non-minimum phase problem, the Type-III controller is designed by using `k' factor approach and is implemented for closed loop operation of IBC for achieving better dynamic performance and stability. The closed loop IBC with designed Type-III controller is fabricated and tested in the laboratory. Simulation and experimental results have been produced to show the efficacy of the proposed converter control system.
Investigating Personalized Search in E-Commerce

Personalized recommendations have become a common feature of many modern online services. In particular on ecommerce sites, one value of such recommendations is that they help consumers find items of interest in large product assortments more quickly. Many of today’s sites take advantage of modern recommendation technologies to create personalized item suggestions for consumers navigating the site. However, limited research exists on the use of personalization and recommendation technology when consumers rely on the site’s catalog search functionality to discover relevant items. In this work we explore the value of personalizing search results on e-commerce sites using recommendation technology. We design and evaluate different personalization strategies using log data of an online retail site. Our results show that considering several item relevance signals within the recommendation process in parallel leads to the best ranking of the search results. Specifically, the factors taken into account include the users’ general interests, their most recent browsing behavior, as well as the consideration of current sales trends.
Automatic photo-from-panorama for Google Maps

We introduce a technique for extracting interesting photographs from 360° panoramas. We build on the success of convolutional neural networks for classification to train a model that scores a given view, using this score to find a best view. Training data for this classification model is generated automatically from landmark detections within Street View panoramas. We validate that our selected views are often preferred over manually chosen ones and have experienced an increase in user interaction when automatically selected views are shown on Google Maps.