nils-herrmann/clean_scirepeval huggingface dataset:
Beyond audio and video retrieval: towards multimedia summarization

Given the deluge of multimedia content that is becoming available over the Internet, it is increasingly important to be able to effectively examine and organize these large stores of information in ways that go beyond browsing or collaborative filtering. In this paper we review previous work on audio and video processing, and define the task of Topic-Oriented Multimedia Summarization (TOMS) using natural language generation: given a set of automatically extracted features from a video (such as visual concepts and ASR transcripts) a TOMS system will automatically generate a paragraph of natural language ("a recounting"), which summarizes the important information in a video belonging to a certain topic area, and provides explanations for why a video was matched and retrieved. We see this as a first step towards systems that will be able to discriminate visually similar, but semantically different videos, compare two videos and provide textual output or summarize a large number of videos at once. In this paper, we introduce our approach of solving the TOMS problem. We extract visual concept features and ASR transcription features from a given video, and develop a template-based natural language generation system to produce a textual recounting based on the extracted features. We also propose possible experimental designs for continuously evaluating and improving TOMS systems, and present results of a pilot evaluation of our initial system.
Localization using automotive laser scanners and local pattern matching

Autonomous driving requires vehicle positioning with accuracies of a few decimeters. Typical low-cost GNSS sensors, as they are commonly used for navigation systems, are limited to an accuracy of several meters. Also, they are restricted in reliability because of outages and multipath effects. To improve accuracy and reliability, 3D features can be used, such as pole-like objects and planes, measured by a laser scanner. These features have to be matched to the reference data, given by a landmark map. If we use a nearest neighbor approach to match the data, we will likely get wrong matches, especially at positions with a low initial accuracy. To reduce the number of wrong matches, we use feature patterns. These patterns describe the spatial relationship of a specific number of features and are determined for every possible feature combination, separated in reference and online features. Given these patterns, the correspondences of the measured features can be determined by finding the corresponding patterns in the reference data. We acquired reference data by a high precision Mobile Mapping System. In an area of 2.8 km2 we automatically extracted 1390 pole-like objects and 2006 building facades. A (second) vehicle equipped with an automotive laser scanner was used to generate features with lower accuracy and reliability. In every scan of the laser scanner we extracted landmarks (poles and planes) online. We then used our proposed feature matching to find correspondences. In this paper, we show the performance of the approach for different parameter settings and compare it to the nearest neighbor matching commonly used. Our experimental results show that, by using feature patterns, the rate of false matches can be reduced from about 80 % down to 20 %, compared to a nearest neighbor approach.
DATAFLASKS: An epidemic dependable key-value substrate

Recently, tuple-stores have become pivotal structures in many information systems. Their ability to handle large datasets makes them important in an era with unprecedented amounts of data being produced and exchanged. However, these tuple-stores typically rely on structured peer-to-peer protocols which assume moderately stable environments. Such assumption does not always hold for very large scale systems sized in the scale of thousands of machines. In this paper we present a novel approach to the design of a tuple-store. Our approach follows a stratified design based on an unstructured substrate. We focus on this substrate and how the use of epidemic protocols allow reaching high dependability and scalability.
A Realistic Dataset for Performance Evaluation of Document Layout Analysis

There is a significant need for a realistic dataset on which to evaluate layout analysis methods and examine their performance in detail. This paper presents a new dataset (and the methodology used to create it) based on a wide range of contemporary documents. Strong emphasis is placed on comprehensive and detailed representation of both complex and simple layouts, and on colour originals. In-depth information is recorded both at the page and region level. Ground truth is efficiently created using a new semi-automated tool and stored in a new comprehensive XML representation, the PAGE format. The dataset can be browsed and searched via a web-based front end to the underlying database and suitable subsets (relevant to specific evaluation goals) can be selected and downloaded.
Learning Affinity Functions for Image Segmentation: Combining Patch-based and Gradient-based Approaches

This paper studies the problem of combining region and boundary cues for natural image segmentation. We employ a large database of manually segmented images in order to learn an optimal affinity function between pairs of pixels. These pairwise affinities can then be used to cluster the pixels into visually coherent groups. Region cues are computed as the similarity in brightness, color, and texture between image patches. Boundary cues are incorporated by looking for the presence of an “intervening contour”, a large gradient along a straight line connecting two pixels. We first use the dataset of human segmentations to individually optimize parameters of the patch and gradient features for brightness, color, and texture cues. We then quantitatively measure the power of different feature combinations by computing the precision and recall of classifiers trained using those features. The mutual information between the output of the classifiers and the same-segment indicator function provides an alternative evaluation technique that yields identical conclusions. As expected, the best classifier makes use of brightness, color, and texture features, in both patch and gradient forms. We find that for brightness, the gradient cue outperforms the patch similarity. In contrast, using color patch similarity yields better results than using color gradients. Texture is the most powerful of the three channels, with both patches and gradients carrying significant independent information. Interestingly, the proximity of the two pixels does not add any information beyond that provided by the similarity cues. We also find that the convexity assumptions made by the intervening contour approach are supported by the ecological statistics of the dataset.
High-Dimensional Variable Selection for Survival Data

The minimal depth of a maximal subtree is a dimensionless order statistic measuring the predictiveness of a variable in a survival tree. We derive the distribution of the minimal depth and use it for high-dimensional variable selection using random survival forests. In big p and small n problems (where p is the dimension and n is the sample size), the distribution of the minimal depth reveals a “ceiling effect” in which a tree simply cannot be grown deep enough to properly identify predictive variables. Motivated by this limitation, we develop a new regularized algorithm, termed RSF-Variable Hunting. This algorithm exploits maximal subtrees for effective variable selection under such scenarios. Several applications are presented demonstrating the methodology, including the problem of gene selection using microarray data. In this work we focus only on survival settings, although our methodology also applies to other random forests applications, including regression and classification settings. All examples presented here use the R-software package randomSurvivalForest.
A comparison of adaptive radix trees and hash tables

With prices of main memory constantly decreasing, people nowadays are more interested in performing their computations in main memory, and leave high I/O costs of traditional disk-based systems out of the equation. This change of paradigm, however, represents new challenges to the way data should be stored and indexed in main memory in order to be processed efficiently. Traditional data structures, like the venerable B-tree, were designed to work on disk-based systems, but they are no longer the way to go in main-memory systems, at least not in their original form, due to the poor cache utilization of the systems they run on. Because of this, in particular, during the last decade there has been a considerable amount of research on index data structures for main-memory systems. Among the most recent and most interesting data structures for main-memory systems there is the recently-proposed adaptive radix tree ARTful (ART for short). The authors of ART presented experiments that indicate that ART was clearly a better choice over other recent tree-based data structures like FAST and B+-trees. However, ART was not the first adaptive radix tree. To the best of our knowledge, the first was the Judy Array (Judy for short), and a comparison between ART and Judy was not shown. Moreover, the same set of experiments indicated that only a hash table was competitive to ART. The hash table used by the authors of ART in their study was a chained hash table, but this kind of hash tables can be suboptimal in terms of space and performance due to their potentially high use of pointers. In this paper we present a thorough experimental comparison between ART, Judy, two variants of hashing via quadratic probing, and three variants of Cuckoo hashing. These hashing schemes are known to be very efficient. For our study we consider whether the data structures are to be used as a non-covering index (relying on an additional store), or as a covering index (covering key-value pairs). We consider both OLAP and OLTP scenarios. Our experiments strongly indicate that neither ART nor Judy are competitive to the aforementioned hashing schemes in terms of performance, and, in the case of ART, sometimes not even in terms of space.
Firefly Algorithms for Multimodal Optimization

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.
Fine-Grained Control-Flow Integrity Through Binary Hardening

Applications written in low-level languages without type or memory safety are prone to memory corruption. Attackers gain code execution capabilities through memory corruption despite all currently deployed defenses. Control-Flow Integrity (CFI) is a promising security property that restricts indirect control-flow transfers to a static set of well-known locations. We present Lockdown, a modular, fine-grained CFI policy that protects binary-only applications and libraries without requiring sourcecode. Lockdown adaptively discovers the control-flow graph of a running process based on the executed code. The sandbox component of Lockdown restricts interactions between different shared objects to imported and exported functions by enforcing fine-grained CFI checks using information from a trusted dynamic loader. A shadow stack enforces precise integrity for function returns. Our prototype implementation shows that Lockdown results in low performance overhead and a security analysis discusses any remaining gadgets.
Design Considerations for Multilevel CMOS/Nano Memristive Memory

With technology migration into nano and molecular scales several hybrid CMOS/nano logic and memory architectures have been proposed that aim to achieve high device density with low power consumption. The discovery of the memristor has further enabled the realization of denser nanoscale logic and memory systems by facilitating the implementation of multilevel logic. This work describes the design of such a multilevel nonvolatile memristor memory system, and the design constraints imposed in the realization of such a memory. In particular, the limitations on load, bank size, number of bits achievable per device, placed by the required noise margin for accurately reading and writing the data stored in a device are analyzed. Also analyzed are the nondisruptive read and write methodologies for the hybrid multilevel memristor memory to program and read the memristive information without corrupting it. This work showcases two write methodologies that leverage the best traits of memristors when used in either linear (low power) or nonlinear drift (fast speeds) modes. The system can therefore be tailored depending on the required performance parameters of a given application for a fast memory or a slower but very energy-efficient system. We propose for the first time, a hybrid memory that aims to incorporate the area advantage provided by the utilization of multilevel logic and nanoscale memristive devices in conjunction with CMOS for the realization of a high density nonvolatile multilevel memory.
A predictive model of menu performance

Menus are a primary control in current interfaces, but there has been relatively little theoretical work to model their performance. We propose a model of menu performance that goes beyond previous work by incorporating components for Fitts' Law pointing time, visual search time when novice, Hick-Hyman Law decision time when expert, and for the transition from novice to expert behaviour. The model is able to predict performance for many different menu designs, including adaptive split menus, items with different frequencies and sizes, and multi-level menus. We tested the model by comparing predictions for four menu designs (traditional menus, recency and frequency based split menus, and an adaptive 'morphing' design) with empirical measures. The empirical data matched the predictions extremely well, suggesting that the model can be used to explore a wide range of menu possibilities before implementation.
AI Planning: Systems and Techniques

that can describe a set of actions. .. that can be expected to allow the system to reach a desired goal. A long-standing problem in the field of automated reasoning is designing systems that can describe a set of actions (or a plan) that can be expected to allow the system to reach a desired goal. Ideally, this set of actions is then passed to a robot, a m a n u f a c t u r i n g system, or some other form of effec-tor, which can follow the plan and produce the desired result. The design of such planners has been with AI since its earliest days, and a large number of techniques have been introduced in progressively more ambitious systems over a long period. In addition , planning research has introduced many problems to the field of AI. Some examples are the representation and the reasoning about time, causality, and intentions; physical or other constraints on suitable solutions; uncertainty in the execution of plans; sensation and perception of the real world and the holding of beliefs about it; and multiple agents who might cooperate or interfere. Planning problems, like most AI topics, have been attacked in two major ways: approaches that try to understand and solve the general problem without the use of domain-specific knowledge and approaches that directly use domain heuristics. In planning , these approaches are often referred to as domain dependent (those that use domain-specific heuristics to control the planner's operation) and domain independent (those in which the planning knowledge representation and algorithms are expected to work for a reasonably large variety of application domains). The issues involved in the design of domain-dependent planners are those generally found in applied approaches to AI: the need to justify solutions, the difficulty of knowledge acquisition, and the fact that the design principles might not map well from one application domain to another. Work in domain-independent planning has formed the bulk of AI research in planning. The long history of these efforts (figure 1) has led to the discovery of many recurring problems as well as to certain standard solutions. In addition, there have been a number of This article reviews research in the development of plan generation systems. Our goal is to familiarize the reader with some of the important problems that have arisen in the design of planning systems and to discuss some …
Exploiting Category-Specific Information for Multi-Document Summarization

We show that by making use of information common to document sets belonging to a common category, we can improve the quality of automatically extracted content in multi-document summaries. This simple property is widely applicable in multi-document summarization tasks, and can be encapsulated by the concept of category-specific importance (CSI). Our experiments show that CSI is a valuable metric to aid sentence selection in extractive summarization tasks. We operationalize the computation CSI of sentences through the introduction of two new features that can be computed without needing any external knowledge. We also generalize this approach, showing that when manually-curated document-to-category mappings are unavailable, performing automatic categorization of document sets also improves summarization performance. We have incorporated these features into a simple, freely available, open-source extractive summarization system, called SWING. In the recent TAC-2011 guided summarization task, SWING outperformed all other participant summarization systems as measured by automated ROUGE measures.
Art and brain: insights from neuropsychology, biology and evolution.

Art is a uniquely human activity associated fundamentally with symbolic and abstract cognition. Its practice in human societies throughout the world, coupled with seeming non-functionality, has led to three major brain theories of art. (1) The localized brain regions and pathways theory links art to multiple neural regions. (2) The display of art and its aesthetics theory is tied to the biological motivation of courtship signals and mate selection strategies in animals. (3) The evolutionary theory links the symbolic nature of art to critical pivotal brain changes in Homo sapiens supporting increased development of language and hierarchical social grouping. Collectively, these theories point to art as a multi-process cognition dependent on diverse brain regions and on redundancy in art-related functional representation.
Employee Turnover-a Study of its Causes and Effects to Different Industries in Bangladesh

Productivity is very important issue for an industry or organization. There are several factors on which productivity of an organization mostly depends upon. Employee’s turnover is one of them which is considered to be one of the challenging issues in business nowadays. The impact of turnover has received considerable attention by senior management, human resources professionals and industrial psychologists. It has proven to be one of the most costly and seemingly intractable human resource challenges confronting by several organizations globally. The purpose of this research is therefore, to find out the actual reasons behind turnover and its damaging affects on the productivity of different industries in Bangladesh. The authors of this paper were visited and studied several local industries both from government and non-government sectors in Bangladesh and observed the suffering for turnover problems. The objective of these case studies were to find out the actual reasons of turnover, its negative effects and possible recommendations that could be helpful to the local industries for their productivity and market share.
Automating image segmentation verification and validation by learning test oracles

An image segmentation algorithm delineates (an) object(s) of interest in an image. Its output is referred to as a segmentation. Developing these algorithms is a manual, iterative process involving repetitive verification and validation tasks. This process is time-consuming and depends on the availability of experts, who may be a scarce resource (e.g., medical experts). We propose a framework referred to as Image Segmentation Automated Oracle (ISAO) that uses machine learning to construct an oracle, which can then be used to automatically verify the correctness of image segmentations, thus saving substantial resources and making the image segmentation verification and validation task significantly more efficient. The framework also gives informative feedback to the developer as the segmentation algorithm evolves and provides a systematic means of testing different parametric configurations of the algorithm. During the initial learning phase, segmentations from the first few (optimally two) versions of the segmentation algorithm are manually verified by experts. The similarity of successive segmentations of the same images is also measured in various ways. This information is then fed to a machine learning algorithm to construct a classifier that distinguishes between consistent and inconsistent segmentation pairs (as determined by an expert) based on the values of the similarity measures associated with each segmentation pair. Once the accuracy of the classifier is deemed satisfactory to support a consistency determination, the classifier is then used to determine whether the segmentations that are produced by subsequent versions of the algorithm under test, are (in)consistent with already verified segmentations from previous versions. This information is then used to automatically draw conclusions about the correctness of the segmentations. We have successfully applied this approach to 3D segmentations of the cardiac left ventricle obtained from CT scans and have obtained promising results (accuracies of 95%). Even though more experiments are needed to quantify the effectiveness of the approach in real-world applications, ISAO shows promise in increasing the quality and testing efficiency of image segmentation
Automatic, fast, online calibration between depth and color cameras

Automatic camera calibration has remained a hard topic in computer vision since its inception due to its reliance on the image corresp ondence problem. This problem becomes even more pronounced when calibrating a depth image with a color image due to a lack of simple corresp ondences between the two modalities. In this work, we develop a completely automatic, very fast, online algorithm that demonstrates how a consumer-grade depth camera can be calibrated with a color camera with minimal user interaction.
Speech segmentation and word discovery: a computational perspective

The segmentation and word discovery problem arises because speech does not contain any reliable acoustic analog of the blank spaces between words of printed English. As a result, children must segment the utterances they hear in order to discover the sound patterns of individual words in their language. A number of computational models have been proposed to explain how children segment speech and discover words, including ten new models in the last five years. This paper reviews all proposed models and organizes them according to their fundamental segmentation strategies, their processing characteristics, and the ways in which they use memory. All proposed models are found to use one of three fundamental strategies: the utterance-boundary strategy, the predictability strategy, or the word-recognition strategy. Selected predictions of the models are explained, their performance in computer simulations is summarized, and behavioral evidence bearing on them is discussed. Finally, ideas about how these diverse models might be synthesized into one comprehensive model are offered.