nils-herrmann/clean_scirepeval huggingface dataset:
Detecting People Using Mutually Consistent Poselet Activations

Bourdev and Malik (ICCV 09) introduced a new notion of parts, poselets, constructed to be tightly clustered both in the configuration space of keypoints, as well as in the appearance space of image patches. In this paper we develop a new algorithm for detecting people using poselets. Unlike that work which used 3D annotations of keypoints, we use only 2D annotations which are much easier for naive human annotators. The main algorithmic contribution is in how we use the pattern of poselet activations. Individual poselet activations are noisy, but considering the spatial context of each can provide vital disambiguating information, just as object detection can be improved by considering the detection scores of nearby objects in the scene. This can be done by training a two-layer feed-forward network with weights set using a max margin technique. The refined poselet activations are then clustered into mutually consistent hypotheses where consistency is based on empirically determined spatial keypoint distributions. Finally, bounding boxes are predicted for each person hypothesis and shape masks are aligned to edges in the image to provide a segmentation. To the best of our knowledge, the resulting system is the current best performer on the task of people detection and segmentation with an average precision of 47.8% and 40.5% respectively on PASCAL VOC 2009.
Extractive Summarization using Continuous Vector Space Models

Automatic summarization can help users extract the most important pieces of information from the vast amount of text digitized into electronic form everyday. Central to automatic summarization is the notion of similarity between sentences in text. In this paper we propose the use of continuous vector representations for semantically aware representations of sentences as a basis for measuring similarity. We evaluate different compositions for sentence representation on a standard dataset using the ROUGE evaluation measures. Our experiments show that the evaluated methods improve the performance of a state-of-the-art summarization framework and strongly indicate the benefits of continuous word vector representations for automatic summarization.
Tuberculosis Disease Diagnosis Using Artificial Neural Networks

Tuberculosis is an infectious disease, caused in most cases by microorganisms called Mycobacterium tuberculosis. Tuberculosis is a great problem in most low income countries; it is the single most frequent cause of death in individuals aged fifteen to forty-nine years. Tuberculosis is important health problem in Turkey also. In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. A general regression neural network (GRNN) was also performed to realize tuberculosis diagnosis for the comparison. Levenberg-Marquardt algorithms were used for the training of the multilayer neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on tuberculosis diseases diagnosis. The tuberculosis dataset were taken from a state hospital’s database using patient’s epicrisis reports.
The case for the reduced instruction set computer

One of the primary goals of computer architects is to design computers that are more costeffective than their predecessors. Cost-effectiveness includes the cost of hardware to manufacture the machine, the cost of programming, and costs incurred related to the architecture in debugging both the initial hardware and subsequent programs. If we review the history of computer families we find that the most common architectural change is the trend toward ever more complex machines. Presumably this additional complexity has a positive tradeoff with regard to the costeffectiveness of newer models. In this paper we propose that this trend is not always cost-effective, and in fact, may even do more harm than good. We shall examine the case for a Reduced Instruction Set Computer (RISC) being as cost-effective as a Complex Instruction Set Computer (CISC). This paper will argue that the next generation of VLSI computers may be more effectively implemented as RISC's than CISC's.
Landmark based radar SLAM using graph optimization

On the way to achieving higher degrees of autonomy for vehicles in complicated, ever changing scenarios, the localization problem poses a very important role. Especially the Simultaneous Localization and Mapping (SLAM) problem has been studied greatly in the past. For an autonomous system in the real world, we present a very cost-efficient, robust and very precise localization approach based on GraphSLAM and graph optimization using radar sensors. We are able to prove on a dynamically changing parking lot layout that both mapping and localization accuracy are very high. To evaluate the performance of the mapping algorithm, a highly accurate ground truth map generated from a total station was used. Localization results are compared to a high precision DGPS/INS system. Utilizing these methods, we can show the strong performance of our algorithm.
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

Designing agile locomotion for quadruped robots often requires extensive expertise and tedious manual tuning. In this paper, we present a system to automate this process by leveraging deep reinforcement learning techniques. Our system can learn quadruped locomotion from scratch using simple reward signals. In addition, users can provide an open loop reference to guide the learning process when more control over the learned gait is needed. The control policies are learned in a physics simulator and then deployed on real robots. In robotics, policies trained in simulation often do not transfer to the real world. We narrow this reality gap by improving the physics simulator and learning robust policies. We improve the simulation using system identification, developing an accurate actuator model and simulating latency. We learn robust controllers by randomizing the physical environments, adding perturbations and designing a compact observation space. We evaluate our system on two agile locomotion gaits: trotting and galloping. After learning in simulation, a quadruped robot can successfully perform both gaits in the real world.
Theory of the backpropagation neural network

The author presents a survey of the basic theory of the backpropagation neural network architecture covering architectural design, performance measurement, function approximation capability, and learning. The survey includes previously known material, as well as some new results, namely, a formulation of the backpropagation neural network architecture to make it a valid neural network (past formulations violated the locality of processing restriction) and a proof that the backpropagation mean-squared-error function exists and is differentiable. Also included is a theorem showing that any L/sub 2/ function from (0, 1)/sup n/ to R/sup m/ can be implemented to any desired degree of accuracy with a three-layer backpropagation neural network. The author presents a speculative neurophysiological model illustrating how the backpropagation neural network architecture might plausibly be implemented in the mammalian brain for corticocortical learning between nearby regions of the cerebral cortex.<<ETX>>
Fast Image Tagging

Automatic image annotation is a difficult and highly relevant machine learning task. Recent advances have significantly improved the state-of-the-art in retrieval accuracy with algorithms based on nearest neighbor classification in carefully learned metric spaces. But this comes at a price of increased computational complexity during training and testing. We propose FastTag, a novel algorithm that achieves comparable results with two simple linear mappings that are co-regularized in a joint convex loss function. The loss function can be efficiently optimized in closed form updates, which allows us to incorporate a large number of image descriptors cheaply. On several standard real-world benchmark data sets, we demonstrate that FastTag matches the current state-of-the-art in tagging quality, yet reduces the training and testing times by several orders of magnitude and has lower asymptotic complexity.
Measuring Fairness in Ranked Outputs

Ranking and scoring are ubiquitous. We consider the setting in which an institution, called a ranker, evaluates a set of individuals based on demographic, behavioral or other characteristics. The final output is a ranking that represents the relative quality of the individuals. While automatic and therefore seemingly objective, rankers can, and often do, discriminate against individuals and systematically disadvantage members of protected groups. This warrants a careful study of the fairness of a ranking scheme, to enable data science for social good applications, among others.
 In this paper we propose fairness measures for ranked outputs. We develop a data generation procedure that allows us to systematically control the degree of unfairness in the output, and study the behavior of our measures on these datasets. We then apply our proposed measures to several real datasets, and detect cases of bias. Finally, we show preliminary results of incorporating our ranked fairness measures into an optimization framework, and show potential for improving fairness of ranked outputs while maintaining accuracy.
 The code implementing all parts of this work is publicly available at https://github.com/DataResponsibly/FairRank.
A krill herd algorithm for efficient text documents clustering

Recently, due to the huge growth of web pages, social media and modern applications, text clustering technique has emerged as a significant task to deal with a huge amount of text documents. Some web pages are easily browsed and tidily presented via applying the clustering technique in order to partition the documents into a subset of homogeneous clusters. In this paper, two novel text clustering algorithms based on krill herd (KH) algorithm are proposed to improve the web text documents clustering. In the first method, the basic KH algorithm with all its operators is utilized while in the second method, the genetic operators in the basic KH algorithm are neglected. The performance of the proposed KH algorithms is analyzed and compared with the k-mean algorithm. The experiments were conducted using four standard benchmark text datasets. The results showed that the proposed KH algorithms outperformed the k-mean algorithm in term of clusters quality that is evaluated using two common clustering measures, namely, Purity and Entropy.
Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems

Classification in imbalanced domains is a recent challenge in data mining. We refer to imbalanced classification when data presents many examples from one class and few from the other class, and the less representative class is the one which has more interest from the point of view of the learning task. One of the most used techniques to tackle this problem consists in preprocessing the data previously to the learning process. This preprocessing could be done through under-sampling; removing examples, mainly belonging to the majority class; and over-sampling, by means of replicating or generating new minority examples. In this paper, we propose an under-sampling procedure guided by evolutionary algorithms to perform a training set selection for enhancing the decision trees obtained by the C4.5 algorithm and the rule sets obtained by PART rule induction algorithm. The proposal has been compared with other under-sampling and over-sampling techniques and the results indicate that the new approach is very competitive in terms of accuracy when comparing with over-sampling and it outperforms standard under-sampling. Moreover, the obtained models are smaller in terms of number of leaves or rules generated and they can considered more interpretable. The results have been contrasted through non-parametric statistical tests over multiple data sets.
Designing information gathering robots for human-populated environments

Advances in mobile robotics have enabled robots that can autonomously operate in human-populated environments. Although primary tasks for such robots might be fetching, delivery, or escorting, they present an untapped potential as information gathering agents that can answer questions for the community of co-inhabitants. In this paper, we seek to better understand requirements for such information gathering robots (InfoBots) from the perspective of the user requesting the information. We present findings from two studies: (i) a user survey conducted in two office buildings and (ii) a 4-day long deployment in one of the buildings, during which inhabitants of the building could ask questions to an InfoBot through a web-based interface. These studies allow us to characterize the types of information that InfoBots can provide for their users.
Does the Directivity of a Virtual Agent's Speech Influence the Perceived Social Presence?

When interacting and communicating with virtual agents in immersive environments, the agents’ behavior should be believable and authentic. Thereby, one important aspect is a convincing auralization of their speech. In this work-in-progress paper a study design to evaluate the effect of adding directivity to speech sound source on the perceived social presence of a virtual agent is presented. Therefore, we describe the study design and discuss first results of a prestudy as well as consequential improvements of the design.
Robotic abdominal surgery.

As a whole, abdominal surgeons possess excellent videoendoscopic surgical skills. However, the limitations of laparoscopy-such as reduced range of motion and instrument dexterity and 2-dimensional view of the operative field-have inspired even the most accomplished laparoscopists to investigate the potential of surgical robotics to broaden their application of the minimally invasive surgery paradigm. This review discusses data obtained from articles indexed in the MEDLINE database written in English and mapped to the following key words: "surgical robotics," "robotic surgery," "robotics," "computer-assisted surgery," "da Vinci," "Zeus," "fundoplication," "morbid obesity," "hepatectomy," "pancreatectomy," "small intestine," "splenectomy," "colectomy," "adrenalectomy," and "pediatric surgery." A limited subset of 387 publications was reviewed to determine article relevance to abdominal robotic surgery. Particular emphasis was placed on reports that limited their discussion to human applications and surgical outcomes. Included are comments about the initial 202 robotic abdominal surgery cases performed at Johns Hopkins University Hospital (Baltimore, MD) from August 2000 to January 2004. Surgical robotic systems are being used to apply laparoscopy to the surgical treatment of diseases in virtually every abdominal organ. Procedures demanding superior visualization or requiring complex reconstruction necessitating extensive suturing obtain the greatest benefit from robotics over conventional laparoscopy. Whereas advanced surgical robotic systems offer the promise of a unique combination of advantages over open and conventional laparoscopic approaches, clinical data demonstrating improved outcomes are lacking for robotic surgical applications within the abdomen. Outcomes data for surgical robotics are essential given the exorbitant costs associated with the use of these tools.
Range Fixes: Interactive Error Resolution for Software Configuration

To prevent ill-formed configurations, highly configurable software often allows defining constraints over the available options. As these constraints can be complex, fixing a configuration that violates one or more constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix or a very long fix list, difficult for the user to identify the desirable fix; and (2) they do not fully support non-Boolean constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a violated constraint. We have evaluated our approach with three different strategies for handling constraint interactions, on data from nine open source projects over two configuration platforms. The evaluation shows that our notion of range fix leads to mostly simple yet complete sets of fixes, and our algorithm is able to generate fixes within one second for configuration systems with a few thousands options and constraints.
Speech Interfaces to Virtual Reality

In this paper, we consider how speech interfaces can be combined with a direct manipulation interface to virtual reality. We outline the beneets of adding a speech interface, the requirements it imposes on speech recognition, language processing and interaction design. We describe the multimodal DIVERSE system which provides a speech interface to virtual worlds modelled in DIVE. This system can be extended to provide better management of the interaction as well as innovative functionality which allow users to talk directly to agents in the virtual world.
Serotonergic function, two-mode models of self-regulation, and vulnerability to depression: what depression has in common with impulsive aggression.

Evidence from diverse literatures supports the viewpoint that two modes of self-regulation exist, a lower-order system that responds quickly to associative cues of the moment and a higher-order system that responds more reflectively and planfully; that low serotonergic function is linked to relative dominance of the lower-order system; that how dominance of the lower-order system is manifested depends on additional variables; and that low serotonergic function therefore can promote behavioral patterns as divergent as impulsive aggression and lethargic depression. Literatures reviewed include work on two-mode models; studies of brain function supporting the biological plausibility of the two-mode view and the involvement of serotonergic pathways in functions pertaining to it; and studies relating low serotonergic function to impulsiveness, aggression (including extreme violence), aspects of personality, and depression vulnerability. Substantial differences between depression and other phenomena reviewed are interpreted by proposing that depression reflects both low serotonergic function and low reward sensitivity. The article closes with brief consideration of the idea that low serotonergic function relates to even more diverse phenomena, whose natures depend in part on sensitivities of other systems.
The lean gap: A review of lean approaches to large-scale software systems development

Lean approaches to product development (LPD) have had a strong influence on many industries and in recent years there have been many proponents for lean in software development as it can support the increasing industry need of scaling agile software development. With it's roots in industrial manufacturing and, later, industrial product development, it would seem natural that LPD would adapt well to large-scale development projects of increasingly software-intensive products, such as in the automotive industry. However, it is not clear what kind of experience and results have been reported on the actual use of lean principles and practices in software development for such large-scale industrial contexts. This was the motivation for this study as the context was an ongoing industry process improvement project at Volvo Car Corporation and Volvo Truck Corporation. The objectives of this study are to identify and classify state of the art in large-scale software development influenced by LPD approaches and use this established knowledge to support industrial partners in decisions on a software process improvement (SPI) project, and to reveal research gaps and proposed extensions to LPD in relation to its well-known principles and practices. For locating relevant state of the art we conducted a systematic mapping study, and the industrial applicability and relevance of results and said extensions to LPD were further analyzed in the context of an actual, industrial case. A total of 10,230 papers were found in database searches, of which 38 papers were found relevant. Of these, only 42 percent clearly addressed large-scale development. Furthermore, a majority of papers (76 percent) were non-empirical and many lacked information about study design, context and/or limitations. Most of the identified results focused on eliminating waste and creating flow in the software development process, but there was a lack of results for other LPD principles and practices. Overall, it can be concluded that research in the much hyped field of lean software development is in its nascent state when it comes to large scale development. There is very little support available for practitioners who want to apply lean approaches for improving large-scale software development, especially when it comes to inter-departmental interactions during development. This paper explicitly maps the area, qualifies available research, and identifies gaps, as well as suggests extensions to lean principles relevant for large scale development of software intensive systems.
Design Pattern Recovery in Object-Oriented Software

An approach to recover object oriented design patterns from design and code is presented. The pattern recovery process is based on a multi-stage filtering strategy to avoid combinatorial explosion on large software systems. To maintain independence from the language and the case tools adopted in developing software, both design and code are mapped into an intermediate representation. The multi-stage searching strategy allows to safely determine pattern candidates. To assess the effectiveness of the pattern recovery process a portable environment written in Java has been developed. Based on this environment, experimental results on public domain and industrial software were obtained and are discussed in the paper. Evidence is shown that, by exploiting information about method calls as a further constraint beyond the structural ones, the number of false positives is reduced.
A study of enterprise resource planning (ERP) system performance measurement using the quantitative balanced scorecard approach

Enterprise resource planning (ERP) systems have been used in integrating information and accelerating its distribution across functions and departments with the aim to increase organizations’ operational performance. Thus, it is worth measuring ERP system performance based on its impact to critical performance of an organization: this requires a systematic method that bridges ERP performance measurement and key organizational performance. The hierarchical balanced scorecard (HBSC) model with respect to multiple criteria decision-making is such a systematic approach to ERP performance measurement. An ERP evaluation framework that integrates the balanced scorecard dimensions, linguistic variables, and non-additive fuzzy integral provides an objective approach to measuring both the performance level of the ERP system and its contribution to the strategic objectives of high-tech firms. Taking Taiwan’s high-tech firms as an example, this study demonstrates the effectiveness of this integrated approach to measure the performance of ERP systems at the post-implementation stage under evaluators’ subjective, uncertainty, and vagueness judgments.