nils-herrmann/clean_scirepeval huggingface dataset:
A Simple Domain-Independent Probabilistic Approach to Generation

We present a simple, robust generation system which performs content selection and surface realization in a unified, domain-independent framework. In our approach, we break up the end-to-end generation process into a sequence of local decisions, arranged hierarchically and each trained discriminatively. We deployed our system in three different domains—Robocup sportscasting, technical weather forecasts, and common weather forecasts, obtaining results comparable to state-ofthe-art domain-specific systems both in terms of BLEU scores and human evaluation.
Continuum Robot Arms Inspired by Cephalopods

In this paper, we describe our recent results in the development of a new class of soft, continuous backbone (“continuum”) robot manipulators. Our work is strongly motivated by the dexterous appendages found in cephalopods, particularly the arms and suckers of octopus, and the arms and tentacles of squid. Our ongoing investigation of these animals reveals interesting and unexpected functional aspects of their structure and behavior. The arrangement and dynamic operation of muscles and connective tissue observed in the arms of a variety of octopus species motivate the underlying design approach for our soft manipulators. These artificial manipulators feature biomimetic actuators, including artificial muscles based on both electro-active polymers (EAP) and pneumatic (McKibben) muscles. They feature a “clean” continuous backbone design, redundant degrees of freedom, and exhibit significant compliance that provides novel operational capacities during environmental interaction and object manipulation. The unusual compliance and redundant degrees of freedom provide strong potential for application to delicate tasks in cluttered and/or unstructured environments. Our aim is to endow these compliant robotic mechanisms with the diverse and dexterous grasping behavior observed in octopuses. To this end, we are conducting fundamental research into the manipulation tactics, sensory biology, and neural control of octopuses. This work in turn leads to novel approaches to motion planning and operator interfaces for the robots. The paper describes the above efforts, along with the results of our development of a series of continuum tentacle-like robots, demonstrating the unique abilities of biologically-inspired design.
On feature combination for multiclass object classification

A key ingredient in the design of visual object classification systems is the identification of relevant class specific aspects while being robust to intra-class variations. While this is a necessity in order to generalize beyond a given set of training images, it is also a very difficult problem due to the high variability of visual appearance within each class. In the last years substantial performance gains on challenging benchmark datasets have been reported in the literature. This progress can be attributed to two developments: the design of highly discriminative and robust image features and the combination of multiple complementary features based on different aspects such as shape, color or texture. In this paper we study several models that aim at learning the correct weighting of different features from training data. These include multiple kernel learning as well as simple baseline methods. Furthermore we derive ensemble methods inspired by Boosting which are easily extendable to several multiclass setting. All methods are thoroughly evaluated on object classification datasets using a multitude of feature descriptors. The key results are that even very simple baseline methods, that are orders of magnitude faster than learning techniques are highly competitive with multiple kernel learning. Furthermore the Boosting type methods are found to produce consistently better results in all experiments. We provide insight of when combination methods can be expected to work and how the benefit of complementary features can be exploited most efficiently.
QUANTIFYING AND COMPARING CENTRALITY MEASURES FOR NETWORK INDIVIDUALS AS APPLIED TO THE ENRON CORPUS

The ever increasing body of social networks creates an opportunity for extensive network analysis and investigations of communications, cliques, and network contributions. In this study, we focus our attention on the Enron email corpus and the corresponding network of employees, attempting to gather information from the email communications. Methods of data reduction on the email corpus were used to create a weighted adjacency matrix in which each i, j-entry corresponds to a weighted count of correspondences from employee i to employee j. While there are many ways to measure importance within a corporate network, of which job title constitutes one such measure, our study focuses on five primary measures: eigenvector centrality, row-sums of a topological overlap matrix, closeness, betweenness, and Opsahl metric. These network analysis metrics were applied to the weighted adjacency matrix to calculate the centrality measures for each individual employee, which were subsequently compiled into ordinally ranked lists of employees for each centrality measure based on decreasing importance. Additionally, the centrality data was visualized using the DataDriven Documents (D3) javascript library, allowing for network visualization in terms of department job title and number of emails sent. In applying the centrality measures to network data, we explore the differences inherent in each measure and work to compare them as well as the corresponding employee importance rankings for each. The metrics in our analysis determined individual importance of employees by applying significant weight to various aspects of the employees’ network roles. By identifying employees that are connected to a large number of individuals and simultaneously have extensive correspondences with those individuals, the Opsahl score combines the other measures, proving to be the most useful metric in exploring Enron’s inner-corporate structure.
Zone Controlled Induction Heating (ZCIH) A New Concept in Induction Heating

This paper proposes a new "zone controlled induction heating" (ZCIH) system. The ZCIH system consists of two or more sets of a high-frequency inverter and a split work coil, which adjusts the coil current amplitude in each zone independently. The ZCIH system has capability of controlling the exothermic distribution on the work piece to avoid the strain caused by a thermal expansion. As a result, the ZCIH system enables a rapid heating performance as well as an temperature uniformity. This paper proposes current phase control making the coil current in phase with each other, to adjust the coil current amplitude even when a mutual inductance exists between the coils. This paper presents operating principle, theoretical analysis, and experimental results obtained from a laboratory setup and a six-zone prototype for a semiconductor processing.
Large-Scale Parallel Collaborative Filtering for the Netflix Prize

Many recommendation systems suggest items to users by utilizing the techniques of collaborative filtering (CF) based on historical records of items that the users have viewed, purchased, or rated. Two major problems that most CF approaches have to resolve are scalability and sparseness of the user profiles. In this paper, we describe Alternating-Least-Squares with Weighted-λ-Regularization (ALS-WR), a parallel algorithm that we designed for the Netflix Prize, a large-scale collaborative filtering challenge. We use parallel Matlab on a Linux cluster as the experimental platform. We show empirically that the performance of ALS-WR monotonically increases with both the number of features and the number of ALS iterations. Our ALS-WR applied to the Netflix dataset with 1000 hidden features obtained a RMSE score of 0.8985, which is one of the best results based on a pure method. Combined with the parallel version of other known methods, we achieved a performance improvement of 5.91% over Netflix’s own CineMatch recommendation system. Our method is simple and scales well to very large datasets.
OCR of historical printings of Latin texts: problems, prospects, progress

This paper deals with the application of OCR methods to historical printings of Latin texts. Whereas the problem of recognizing historical printings of modern languages has been the subject of the IMPACT program, Latin has not yet been given any serious consideration despite the fact that it dominated literature production in Europe up to the 17th century. Using finite state tools and methods developed during the IMPACT program we show that efficent batch-oriented post-correction can work for Latin as well, and that a lexicon of historical Latin spelling variants can be constructed to aid in the correction phase. Initial experiments for the OCR engines Tesseract and OCRopus show that some training on historical fonts and the application of lexical resources raise character accuracies beyond those of Finereader and that accuracies above 90% may be expected even for 16th century material.
Snakes , Shapes and Gradient Vector Flow

A novel method for finding active contours, or snakes as developed by Xu and Prince  is presented in this paper. The approach uses a regularization based technique and calculus of variations to find what the authors call a Gradient Vector Field or GVF in binary-values or grayscale images. The GVF is in turn applied to ’pull’ the snake towards the required feature. The approach presented here differs from other snake algorithms in its ability to extend into object concavities and its robust initialization technique. Although their algorithm works better than existing active contour algorithms, it suffers from computational complexity and associated costs in execution, resulting in slow execution time.
URL normalization for de-duplication of web pages

Presence of duplicate documents in the World Wide Web adversely affects crawling, indexing and relevance, which are the core building blocks of web search. In this paper, we present a set of techniques to mine rules from URLs and utilize these learnt rules for de-duplication using just URL strings without fetching the content explicitly. Our technique is composed of mining the crawl logs and utilizing clusters of similar pages to extract specific rules from URLs belonging to each cluster. Preserving each mined rules for de-duplication is not efficient due to the large number of specific rules. We present a machine learning technique to generalize the set of rules, which reduces the resource footprint to be usable at web-scale. The rule extraction techniques are robust against web-site specific URL conventions. We demonstrate the effectiveness of our techniques through experimental evaluation.
Essentials of the self-organizing map

The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics. The most extensive applications, exemplified in this paper, can be found in the management of massive textual databases and in bioinformatics. The SOM is related to the classical vector quantization (VQ), which is used extensively in digital signal processing and transmission. Like in VQ, the SOM represents a distribution of input data items using a finite set of models. In the SOM, however, these models are automatically associated with the nodes of a regular (usually two-dimensional) grid in an orderly fashion such that more similar models become automatically associated with nodes that are adjacent in the grid, whereas less similar models are situated farther away from each other in the grid. This organization, a kind of similarity diagram of the models, makes it possible to obtain an insight into the topographic relationships of data, especially of high-dimensional data items. If the data items belong to certain predetermined classes, the models (and the nodes) can be calibrated according to these classes. An unknown input item is then classified according to that node, the model of which is most similar with it in some metric used in the construction of the SOM. A new finding introduced in this paper is that an input item can even more accurately be represented by a linear mixture of a few best-matching models. This becomes possible by a least-squares fitting procedure where the coefficients in the linear mixture of models are constrained to nonnegative values.
What do we need to build explainable AI systems for the medical domain?

Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. However, the central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles of such models they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation (GDPR and ISO/IEC 27001) entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. This is beneficial, e.g. for general understanding, for teaching, for learning, for research, and it can be helpful in court. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.
SYMMETRIC CIPHERS BASED ON TWO-DIMENSIONAL CHAOTIC

In this paper, methods are shown how to adapt invertible two-dimensional chaotic maps on a torus or on a square to create new symmetric block encryption schemes. A chaotic map is first generalized by introducing parameters and then discretized to a finite square lattice of points which represent pixels or some other data items. Although the discretized map is a permutation and thus cannot be chaotic, it shares certain properties with its continuous counterpart as long as the number of iterations remains small. The discretized map is further extended to three dimensions and composed with a simple diffusion mechanism. As a result, a symmetric block product encryption scheme is obtained. To encrypt an N × N image, the ciphering map is iteratively applied to the image. The construction of the cipher and its security is explained with the two-dimensional Baker map. It is shown that the permutations induced by the Baker map behave as typical random permutations. Computer simulations indicate that the cipher has good diffusion properties with respect to the plain-text and the key. A nontraditional pseudo-random number generator based on the encryption scheme is described and studied. Examples of some other two-dimensional chaotic maps are given and their suitability for secure encryption is discussed. The paper closes with a brief discussion of a possible relationship between discretized chaos and cryptosystems.
Analysis of Nonuniform ESD Current Distribution in Deep Submicron NMOS Transistors

This paper presents a detailed study of the nonuniform bipolar conduction phenomenon under electrostatic discharge (ESD) events in single-finger NMOS transistors and analyzes its implications for the design of ESD protection for deep-submicron CMOS technologies. It is shown that the uniformity of the bipolar current distribution under ESD conditions is severely degraded depending on device finger width ( ) and significantly influenced by the substrate and gate-bias conditions as well. This nonuniform current distribution is identified as a root cause of the severe reduction in ESD failure threshold current for the devices with advanced silicided processes. Additionally, the concept of an intrinsic second breakdown triggering current ( 2 ) is introduced, which is substrate-bias independent and represents the maximum achievable ESD failure strength for a given technology. With this improved understanding of ESD behavior involved in advanced devices, an efficient design window can be constructed for robust deep submicron ESD protection.
The Heeger & Bergen Pyramid Based Texture Synthesis Algorithm

This contribution deals with the Heeger-Bergen pyramid-based texture analysis/synthesis algorithm. It brings a detailed explanation of the original algorithm tested on many characteristic examples. Our analysis reproduces the original results, but also brings a minor improvement concerning non-periodic textures. Inspired by visual perception theories, Heeger and Bergen proposed to characterize a texture by its first-order statistics of both its color and its responses to multiscale and multi-orientation filters, namely the steerable pyramid. The Heeger-Bergen algorithm consists in the following procedure: starting from a white noise image, histogram matchings are performed to the image alternately in the image domain and the steerable pyramid domain, so that the corresponding output histograms match the ones of the input texture. Source Code An on-line demo1 of the Heeger-Bergen pyramid-based texture synthesis algorithm is available. The demo permits to upload a color image to extract a subimage and to run the texture synthesis algorithm on this subimage. The algorithm available in the demo is a slightly improved version treating non-periodic textures by a “periodic+smooth” decomposition [13]. The algorithm works with color textures and is able to synthesize textures with larger size than the input image. The original version of the Heeger-Bergen algorithm (where the boundaries are handled by mirror symmetrization) is optional in the source code. An ANSI C implementation is available for download here2. It is provided with: • An illustrated html documentation;  This code requires libpng, libfftw3, openmp, and getopt. Compilation and usage instructions are included in the README.txt file of the zip archive. The illustrated HTML documentation can be reproduced from the source code by using doxygen (see the README.txt file of the zip archive for details).
Probabilistic sentential decision diagrams: Learning with massive logical constraints

We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and canonical representation of probability distributions defined over the models of a given propositional theory. Each parameter of a PSDD can be viewed as the (conditional) probability of making a decision in a corresponding Sentential Decision Diagram (SDD). The SDD itself is a recently proposed complete and canonical representation of propositional theories. PSDDs are tractable representations, and further, the parameters of a PSDD can be efficiently estimated, in closed form, from complete data. We empirically evaluate the quality of PSDDs learned from data, when we have knowledge, a priori, of the domain logical constraints.
Ku-band dielectric-loaded SIW horn for vertically-polarized multi-sector antennas

This paper presents a compact and lightweight Ku-band dielectric-loaded H-plane sectoral horn antenna, using the substrate integrated waveguide (SIW) technology, suitable to be utilized as an element of a steerable multi-sector antenna with the ability of scanning a full 360° on the horizon. A dielectric loading together with metallic strips in front of the SIW horn aperture are utilized to optimize the performances in terms of bandwidth, gain and front-to-back ratio (FTBR). To experimentally demonstrate the performance of the proposed design, a three-element prototype, intended later to be a part of an eight-sector antenna, has been fabricated. The measurement process of different characteristics of the prototype is discussed in detail and a comparison between simulated and measured results is given.
Efficient C-space and cost function updates in 3D for unmanned aerial vehicles

When operating in partially-known environments, autonomous vehicles must constantly update their maps and plans based on new sensor information. Much focus has been placed on developing efficient incremental planning algorithms that are able to efficiently replan when the map and associated cost function changes. However, much less attention has been placed on efficiently updating the cost function used by these planners, which can represent a significant portion of the time spent replanning. In this paper, we present the Limited Incremental Distance Transform algorithm, which can be used to efficiently update the cost function used for planning when changes in the environment are observed. Using this algorithm it is possible to plan paths in a completely incremental way starting from a list of changed obstacle classifications. We present results comparing the algorithm to the Euclidean distance transform and a mask-based incremental distance transform algorithm. Computation time is reduced by an order of magnitude for a UAV application. We also provide example results from an autonomous micro aerial vehicle with on-board sensing and computing.
A saturating lumped-parameter model for an interior PM synchronous machine

This paper presents a lumped-parameter magnetic model for an interior permanent-magnet synchronous machine. The model accounts for the effects of saturation through a nonlinear reluctance-element network used to estimate the-axis inductance. The magnetic model is used to calculate inductance and torque in the presence of saturation. Furthermore, these calculations are compared to those from finite-element analysis with good agreement.
Bearingless slice motor concepts without permanent magnets in the rotor

Bearingless permanent magnet motors are very compact and favorable for high speed designs. Especially the bearingless slice motor (using a permanent magnet excited rotor disc) features a mechanically simple and very cost effective fully magnetically levitated system by stabilizing some degrees of freedom by permanent magnetic reluctance forces. This work focuses on constructional possibilities to apply the slice rotor principle with its passive stabilization without any permanent magnets in the rotor part. As a matter of fact, the permanent magnets are necessary and are therefore located in the stator. Possible stator compositions are outlined and separated into homopolar and heteropolar permanent magnet air gap flux types. The definition of performance parameters allows evaluating the operational behavior of the two considered bearingless reluctance slice motor types (one with hompolar and one with heteropolar air gap flux distribution). The composition, optimization and construction of a prototype are outlined in the last section. Measurements are conducted to show the proper functionality of the bearingless reluctance slice motor prototype.