nils-herrmann/clean_scirepeval huggingface dataset:
Vision meets robotics: The KITTI dataset

We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as highresolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations and range from freeways over rural areas to innercity scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.
Large-scale retrieval for medical image analytics: A comprehensive review

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis.
Cybersecurity situational awareness taxonomy

Ensuring cost-efficient cybersecurity for a networked system is a challenging task. In this task, cybersecurity situational awareness is a cornerstone to ensure that systems are protected in a meaningful way. However, cybersecurity situational awareness can be built in various ways. Firstly, several monitoring and analysing techniques can be applied, and secondly, a time window for the usage of situational awareness varies from short-term operational to long-term strategic decision making. Understanding differences and purposes of these aspects is an essential part to research and develop cybersecurity situational awareness. In this paper, we build a taxonomy of cybersecurity situational awareness. The taxonomy categorises terminology, makes it possible to recognise missing areas, and to understand the area in a uniform way. Moreover, the taxonomy helps to select the most effective techniques to be used in a specific situation awareness implementation.
Advanced Engineering Electromagnetics (Balanis, C.A.; 2012) [Book Review]

Since 2012 the second edition of this book is available. This new edition has all the attractive features of the first one and in my opinion it contains some new features and additions that are very useful in the EMC playground.  A section on metamaterials (Section 5.7) is helpful for electromagnetic shielding oriented studies; A section on artificial impedance surfaces (AIS, EBG, PBG, HIS, AMC, PMC) (Section 8.8), is a solid base for any issue related to EBGs; and, in general, Additional smaller inserts throughout the book make it easier and enjoyable to understand; and new figures, photos, and tables have been added. As in the first edition, this second edition is a thorough and detailed engineer-oriented book. The analytical detail, rigor, and thoroughness allow many of the topics to be traced to their origin, and they are presented in sufficient detail so that the reader will follow the analytical developments. The material is presented in a methodical, sequential, and unified manner, and each chapter is subdivided into sections or subsections whose individual headings clearly identify the topics discussed, examined, or illustrated. An exhaustive list of references is included at the end of each chapter to allow the interested reader to trace each topic. A number of appendixes of mathematical identities and special functions, some represented also in tabular and graphical forms, are included. A very useful feature is a set of fortyeight basic MATLAB® computer programs developed and included in the website that is part of the book. They are an aid, also for the skilled reader, for a conceptual fruition of the results based upon the analytical formulations included throughout the book.
Solving optimization problems using black hole algorithm

Various meta-heuristic optimization approaches have been recently created and applied in different areas. Many of these approaches are inspired by swarm behaviors in the nature. This paper studies the solving optimization problems using Black Hole Algorithm (BHA) which is a population-based algorithm. Since the performance of this algorithm was not tested in mathematical functions, we have studied this issue using some standard functions. The results of the BHA are compared with the results of GA and PSO algorithms which indicate that the performance of BHA is better than the other two mentioned algorithms.
Automatic Image Alignment and Stitching of Medical Images with Seam Blending

This paper proposes an algorithm which automatically aligns and stitches the component medical images (fluoroscopic) with varying degrees of overlap into a single composite image. The alignment method is based on similarity measure between the component images. As applied here the technique is intensity based rather than feature based. It works well in domains where feature based methods have difficulty, yet more robust than traditional correlation. Component images are stitched together using the new triangular averaging based blending algorithm. The quality of the resultant image is tested for photometric inconsistencies and geometric misalignments. This method cannot correct rotational, scale and perspective artifacts. Histogram Matching, Image Alignment, Image Stitching, Medical Imaging.
General transformations for GPU execution of tree traversals

With the advent of programmer-friendly GPU computing environments, there has been much interest in offloading workloads that can exploit the high degree of parallelism available on modern GPUs. Exploiting this parallelism and optimizing for the GPU memory hierarchy is well-understood for regular applications that operate on dense data structures such as arrays and matrices. However, there has been significantly less work in the area of irregular algorithms and even less so when pointer-based dynamic data structures are involved. Recently, irregular algorithms such as Barnes-Hut and kd-tree traversals have been implemented on GPUs, yielding significant performance gains over CPU implementations. However, the implementations often rely on exploiting application-specific semantics to get acceptable performance. We argue that there are general-purpose techniques for implementing irregular algorithms on GPUs that exploit similarities in algorithmic structure rather than application-specific knowledge. We demonstrate these techniques on several tree traversal algorithms, achieving speedups of up to 38x over 32--thread CPU versions.
Sentence Fusion for Multidocument News Summarization

A system that can produce informative summaries, highlighting common information found in many online documents, will help Web users to pinpoint information that they need without extensive reading. In this article, we introduce sentence fusion, a novel text-to-text generation technique for synthesizing common information across documents. Sentence fusion involves bottom-up local multisequence alignment to identify phrases conveying similar information and statistical generation to combine common phrases into a sentence. Sentence fusion moves the summarization field from the use of purely extractive methods to the generation of abstracts that contain sentences not found in any of the input documents and can synthesize information across sources.
A Scalable and Explicit Event Delivery Mechanism for UNIX

UNIX applications not wishing to block when doing I/O often use the select() system call, to wait for events on multiple file descriptors. The select() mechanism works well for small-scale applications, but scales poorly as the number of file descriptors increases. Many modern applications, such as Internet servers, use hundreds or thousands of file descriptors, and suffer greatly from the poor scalability of select(). Previous work has shown that while the traditional implementation of select() can be improved, the poor scalability is inherent in the design. We present a new event-delivery mechanism, which allows the application to register interest in one or more sources of events, and to efficiently dequeue new events. We show that this mechanism, which requires only minor changes to applications, performs independently of the number of file descriptors.
FR4 PCB grid array antenna for millimeter-wave 5G mobile communications

Future broadband mobile communication networks such as 5G and beyond will most likely use millimeter-wave frequencies. There is, however, little knowledge about millimeter-wave antenna design for cellular mobile devices. This paper reports a microstrip grid array antenna on an FR4 substrate in a standard PCB technology. It is shown that the microstrip grid array antenna covers a footprint of 15×15 mm2, radiates a fixed beam in the boresight direction, and achieves the 10-dB impedance bandwidth of 7.16 GHz from 23.86 to 31.02 GHz and the 3-dB gain bandwidth of 4.79 GHz from 27.54 to 32.33 GHz with the maximal realized gain of 12.66 dBi at 29.2 GHz.
Combinatorial Pure Exploration of Multi-Armed Bandits

We study the combinatorial pure exploration (CPE) problem in the stochastic multi-armed bandit setting, where a learner explores a set of arms with the objective of identifying the optimal member of a decision class, which is a collection of subsets of arms with certain combinatorial structures such as size-K subsets, matchings, spanning trees or paths, etc. The CPE problem represents a rich class of pure exploration tasks which covers not only many existing models but also novel cases where the object of interest has a nontrivial combinatorial structure. In this paper, we provide a series of results for the general CPE problem. We present general learning algorithms which work for all decision classes that admit offline maximization oracles in both fixed confidence and fixed budget settings. We prove problem-dependent upper bounds of our algorithms. Our analysis exploits the combinatorial structures of the decision classes and introduces a new analytic tool. We also establish a general problem-dependent lower bound for the CPE problem. Our results show that the proposed algorithms achieve the optimal sample complexity (within logarithmic factors) for many decision classes. In addition, applying our results back to the problems of top-K arms identification and multiple bandit best arms identification, we recover the best available upper bounds up to constant factors and partially resolve a conjecture on the lower bounds.
Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences

Opinion question answering is a challenging task for natural language processing. In this paper, we discuss a necessary component for an opinion question answering system: separating opinions from fact, at both the document and sentence level. We present a Bayesian classifier for discriminating between documents with a preponderance of opinions such as editorials from regular news stories, and describe three unsupervised, statistical techniques for the significantly harder task of detecting opinions at the sentence level. We also present a first model for classifying opinion sentences as positive or negative in terms of the main perspective being expressed in the opinion. Results from a large collection of news stories and a human evaluation of 400 sentences are reported, indicating that we achieve very high performance in document classification (upwards of 97% precision and recall), and respectable performance in detecting opinions and classifying them at the sentence level as positive, negative, or neutral (up to 91% accuracy).
Detecting Faces in Images: A Survey

Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics

Deep learning has greatly improved visual recognition in recent years. However, recent research has shown that there exist many adversarial examples that can negatively impact the performance of such an architecture. This paper focuses on detecting those adversarial examples by analyzing whether they come from the same distribution as the normal examples. Instead of directly training a deep neural network to detect adversarials, a much simpler approach was proposed based on statistics on outputs from convolutional layers. A cascade classifier was designed to efficiently detect adversarials. Furthermore, trained from one particular adversarial generating mechanism, the resulting classifier can successfully detect adversarials from a completely different mechanism as well. The resulting classifier is non-subdifferentiable, hence creates a difficulty for adversaries to attack by using the gradient of the classifier. After detecting adversarial examples, we show that many of them can be recovered by simply performing a small average filter on the image. Those findings should lead to more insights about the classification mechanisms in deep convolutional neural networks.
Learning and Collective Knowledge Construction With Social Media: A Process-Oriented Perspective

Social media are increasingly being used for educational purposes. The first part of this article briefly reviews literature that reports on educational applications of social media tools. The second part discusses theories that may provide a basis for analyzing the processes that are relevant for individual learning and collective knowledge construction. We argue that a systems-theoretical constructivist approach is appropriate to examine the processes of educational social media use, namely, self-organization, the internalization of information, the externalization of knowledge, and the interplay of externalization and internalization providing the basis of a co-evolution of cognitive and social systems. In the third part we present research findings that illustrate and support this systems-theoretical framework. Concluding, we discuss the implications for educational design and for future research on learning and collective knowledge construction with social media.
Model Learning for Look-ahead Exploration in Continuous Control

We propose an exploration method that incorporates look-ahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies . Our skills are multi-goal policies learned in isolation in simpler environments using existing multigoal RL formulations, analogous to options or macroactions. Coarse skill dynamics, i.e., the state transition caused by a (complete) skill execution, are learnt and are unrolled forward during lookahead search. Policy search benefits from temporal abstraction during exploration, though itself operates over low-level primitive actions, and thus the resulting policies does not suffer from suboptimality and inflexibility caused by coarse skill chaining. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parametrized skills as building blocks of the policy itself, as opposed to guiding exploration. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parameterized skills as building blocks of the policy itself, as opposed to guiding exploration.
Expectation Particle Belief Propagation

We propose an original particle-based implementation of the Loopy Belief Propagation (LPB) algorithm for pairwise Markov Random Fields (MRF) on a continuous state space. The algorithm constructs adaptively efficient proposal distributions approximating the local beliefs at each note of the MRF. This is achieved by considering proposal distributions in the exponential family whose parameters are updated iterately in an Expectation Propagation (EP) framework. The proposed particle scheme provides consistent estimation of the LBP marginals as the number of particles increases. We demonstrate that it provides more accurate results than the Particle Belief Propagation (PBP) algorithm of  at a fraction of the computational cost and is additionally more robust empirically. The computational complexity of our algorithm at each iteration is quadratic in the number of particles. We also propose an accelerated implementation with sub-quadratic computational complexity which still provides consistent estimates of the loopy BP marginal distributions and performs almost as well as the original procedure.
Artificial fishes: physics, locomotion, perception, behavior

This paper proposes a framework for animation that can achieve the intricacy of motion evident in certain natural ecosystems with minimal input from the animator. The realistic appearance, movement, and behavior of individual animals, as well as the patterns of behavior evident in groups of animals fall within the scope of the framework. Our approach to emulating this level of natural complexity is to model each animal holistically as an autonomous agent situated in its physical world. To demonstrate the approach, we develop a physics-based, virtual marine world. The world is inhabited by artificial fishes that can swim hydrodynamically in simulated water through the motor control of internal muscles that motivates fins. Their repertoire of behaviors relies on their perception of the dynamic environment. As in nature, the detailed motions of artificial fishes in their virtual habitat are not entirely predictable because they are not scripted.
Relating Personality Types with User Preferences in Multiple Entertainment Domains

We present a preliminary study on the relations between personality types and user preferences in multiple entertainment domains, namely movies, TV shows, music, and books. We analyze a total of 53,226 Facebook user profiles composed of both personality scores (openness, conscientiousness, extraversion, agreeableness, neuroticism) from the Five Factor model, and explicit interests about 16 genres in each of the above domains. As a result of our analysis, we extract personality-based user stereotypes and association rules for some of the considered domain genres, and infer similarities of personality types related to genres in different domains.
Graph2Seq: Scalable Learning Dynamics for Graphs

Neural networks have been shown to be an effective tool for learning algorithms over graph-structured data. However, graph representation techniques—that convert graphs to real-valued vectors for use with neural networks—are still in their infancy. Recent works have proposed several approaches (e.g., graph convolutional networks), but these methods have difficulty scaling and generalizing to graphs with different sizes and shapes. We present GRAPH2SEQ, a new technique that represents vertices of graphs as infinite time-series. By not limiting the representation to a fixed dimension, GRAPH2SEQ scales naturally to graphs of arbitrary sizes and shapes. GRAPH2SEQ is also reversible, allowing full recovery of the graph structure from the sequences. By analyzing a formal computational model for graph representation, we show that an unbounded sequence is necessary for scalability. Our experimental results with GRAPH2SEQ show strong generalization and new state-of-the-art performance on a variety of graph combinatorial optimization problems.
Efficiently Summarising Event Sequences with Rich Interleaving Patterns

Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose SQUISH, a novel greedy MDLbased method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show SQUISH is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values.