nils-herrmann/clean_scirepeval huggingface dataset:
Assessing bias in case-control studies. Proper selection of cases and controls.

Case-control studies are retrospective investigations in which a diseased group (cases) and a disease-free group (controls) are compared with the aim of uncovering risk factors that differ between the groups. Many times, cause-effect relationships between the risk factor and disease are inferred from the results. Many important risk factors have been identified through the use of a casecontrol design, including the relationship of smoking to lung cancer, the use of tampons and toxic shock syndrome, and the link between vaginal cancer and maternal use of diethylstilbestrol. Case-control studies can be executed quickly and at a relatively low cost, even when the disease of interest is rare. Such advantages have made the case-control design popular, resulting in a progressive increase in its use. The validity of a casecontrol study, however, is dependent on representative selection of both the case and control groups. Overrepresentation or underrepresentation of either of these groups in the study sample results in a systematic error referred to as bias. Bias can cause an inaccurate assessment of the relationship between the risk factor and the disease. Although all studies can be affected by bias, casecontrol studies are particularly susceptible because of the retrospective nature of the data and the resulting lack of control the investigator has over many items of interest. Case-control studies done in a clinical setting are even further prone to bias because the factors that bring patients to the clinical setting are often related to the disease or risk factor of interest. Investigators who embark on case-control studies must maintain a constant awareness of sources of potential bias that could result in invalid conclusions from the study data. This can be a difficult task because of the numerous and insidious ways that bias can exist. The purpose of this paper is to illustrate how bias can result from improper selection of study patients. Specific types of bias illustrated are those particularly applicable to clinically based research.
Real-time obstacle avoidance using harmonic potential functions

This paper presents a new formulation of the artificial potential approach to the obstacle avoidance problem for a mobile robot or a manipulator in a known environment. Previous formulations of artificial potentials, for obstacle avoidance, have exhibited local minima in a cluttered environment. To build an artificial potential field, we use harmonic functions which completely eliminate local minima even for a cluttered environment. We use the panel method to represent arbitrarily shaped obstacles and to derive the potential over the whole space. Based on this potential function, we propose an elegant conml strategy for the real-time control of a robot. We test the harmonic potential, the panel method and the control strategy with a bar-shaped mobile robot and a 3 dof planar redundant manipulator.
Extended technology acceptance model of Internet utilization behavior

Based on the belief–attitude–performance chain, this study combines Davis’s technology acceptance model (TAM) and the information behavior model to develop an extended TAM for Internet use. The theoretical model was tested via a questionnaire survey of 203 Taiwanese office workers. The empirical results not only confirmed TAM, but also showed that the relevance of information needs strongly determines perceived usefulness, perceived ease of use, and user attitudes toward Internet use for information seeking, as well as strongly influencing individual performance during the information use stage. More importantly, relevance has greater positive effect on perceived performance and perceived usefulness for enterprise Intranet users than for simple/interactive Intraweb users. Enterprise Intranet users have more positive attitudes toward the Internet and more positive perceptions of system effectiveness for supporting office tasks than simple/interactive Intraweb users. Furthermore, perceived ease of use is the strongest determinant of user attitude toward Internet use in both enterprise Intranet and simple/interactive Intraweb applications. Overall, the extended TAM explains the behavior of enterprise Intranet users better than that of simple/ interactive Intraweb users. Limitations of this study are discussed.
Simple Baseline for Visual Question Answering

We describe a very simple bag-of-words baseline for visual question answering. This baseline concatenates the word features from the question and CNN features from the image to predict the answer. When evaluated on the challenging VQA dataset [2], it shows comparable performance to many recent approaches using recurrent neural networks. To explore the strength and weakness of the trained model, we also provide an interactive web demo1, and open-source code2.
An Empirical Evaluation of Three Defect-Detection Techniques

We replicated a controlled experiment first run in the early 1980’s to evaluate the effectiveness and efficiency of 50 student subjects who used three defect-detection techniques to observe failures and isolate faults in small C programs. The three techniques were code reading by stepwise abstraction, functional (black-box) testing, and structural (white-box) testing. Two internal replications showed that our relatively inexperienced subjects were similarly effective at observing failures and isolating faults with all three techniques. However, our subjects were most efficient at both tasks when they used functional testing. Some significant differences among the techniques in their effectiveness at isolating faults of different types were seen. These results suggest that inexperienced subjects can apply a formal verification technique (code reading) as effectively as an execution-based validation technique, but they are most efficient when using functional testing.
Named Entity Recognition with Bidirectional LSTM-CNNs

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly-available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.
Monocular visual odometry in urban environments using an omnidirectional camera

We present a system for monocular simultaneous localization and mapping (mono-SLAM) relying solely on video input. Our algorithm makes it possible to precisely estimate the camera trajectory without relying on any motion model. The estimation is completely incremental: at a given time frame, only the current location is estimated while the previous camera positions are never modified. In particular, we do not perform any simultaneous iterative optimization of the camera positions and estimated 3D structure (local bundle adjustment). The key aspect of the system is a fast and simple pose estimation algorithm that uses information not only from the estimated 3D map, but also from the epipolar constraint. We show that the latter leads to a much more stable estimation of the camera trajectory than the conventional approach. We perform high precision camera trajectory estimation in urban scenes with a large amount of clutter. Using an omnidirectional camera placed on a vehicle, we cover one of the longest distance ever reported, up to 2.5 kilometers.
Identification and control of dynamical systems using neural networks

It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described.
Revisiting the VLAD image representation

Recent works on image retrieval have proposed to index images by compact representations encoding powerful local descriptors, such as the closely related VLAD and Fisher vector. By combining such a representation with a suitable coding technique, it is possible to encode an image in a few dozen bytes while achieving excellent retrieval results. This paper revisits some assumptions proposed in this context regarding the handling of "visual burstiness", and shows that ad-hoc choices are implicitly done which are not desirable. Focusing on VLAD without loss of generality, we propose to modify several steps of the original design. Albeit simple, these modifications significantly improve VLAD and make it compare favorably against the state of the art.
A Game Theoretic Investigation of Deception in Network Security

We perform a game theoretic investigation of the effects of deception on the in teractions between an attacker and a defender of a computer network. The defend er can employ camouflage by either disguising a normal system as a honeypot, or by disguisin a honeypot as a normal system. We model the interactions between defender and attacker u sing a signaling game, a non-cooperative two player dynamic game of incomplete information. F or this model, we determine which strategies admit perfect Bayesian equilibria. These equ ilibria are refined Nash equilibria in which neither the defender nor the attacker will unilaterally c hoose to deviate from their strategies. We discuss the benefits of employing deceptive eq uilibrium strategies in the defense of a computer network.
Question Answering on Linked Data: Challenges and Future Directions

Answering (QA) systems are becoming the inspiring model for the future of search engines. While recently, underlying datasets for QA systems have been promoted from unstructured datasets to structured datasets with highly semantic-enriched metadata, but still question answering systems involve serious challenges which cause to be far beyond desired expectations. In this paper, we raise the challenges for building a Question Answering (QA) system especially with the focus of employing structured data (i.e. knowledge graph). This paper provide an exhaustive insight of the known challenges, so far. Thus, it helps researchers to easily spot open rooms for the future research agenda.
Intercarrier interference self-cancellation space-frequency codes for MIMO-OFDM

Space-frequency (SF) codes that exploit both spatial and frequency diversity can be designed using orthogonal frequency division multiplexing (OFDM). However, OFDM is sensitive to frequency offset (FO), which generates intercarrier interference (ICI) among subcarriers. We investigate the pair-wise error probability (PEP) performance of SF codes over quasistatic, frequency selective Rayleigh fading channels with FO. We prove that the conventional SF code design criteria remain valid. The negligible performance loss for small FOs (less than 1%), however, increases with FO and with signal to noise ratio (SNR). While diversity can be used to mitigate ICI, as FO increases, the PEP does not rapidly decay with SNR. Therefore, we propose a new class of SF codes called ICI self-cancellation SF (ISC-SF) codes to combat ICI effectively even with high FO (10%). ISC-SF codes are constructed from existing full diversity space-time codes. Importantly, our code design provide a satisfactory tradeoff among error correction ability, ICI reduction and spectral efficiency. Furthermore, we demonstrate that ISC-SF codes can also mitigate the ICI caused by phase noise and time varying channels. Simulation results affirm the theoretical analysis.
Using active learning and semantic clustering for noise reduction in distant supervision

The use of external databases to generate training data, also known as Distant Supervision, has become an effective way to train supervised relation extractors but this approach inherently suffers from noise. In this paper we propose a method for noise reduction in distantly supervised training data, using a discriminative classifier and semantic similarity between the contexts of the training examples. We describe an active learning strategy which exploits hierarchical clustering of the candidate training samples. To further improve the effectiveness of this approach, we study the use of several methods for dimensionality reduction of the training samples. We find that semantic clustering of training data combined with cluster-based active learning allows filtering the training data, hence facilitating the creation of a clean training set for relation extraction, at a reduced manual labeling cost.
Self-Paced Learning with Diversity

Self-paced learning (SPL) is a recently proposed learning regime inspired by the learning process of humans and animals that gradually incorporates easy to more complex samples into training. Existing methods are limited in that they ignore an important aspect in learning: diversity. To incorporate this information, we propose an approach called self-paced learning with diversity (SPLD) which formalizes the preference for both easy and diverse samples into a general regularizer. This regularization term is independent of the learning objective, and thus can be easily generalized into various learning tasks. Albeit non-convex, the optimization of the variables included in this SPLD regularization term for sample selection can be globally solved in linearithmic time. We demonstrate that our method significantly outperforms the conventional SPL on three real-world datasets. Specifically, SPLD achieves the best MAP so far reported in literature on the Hollywood2 and Olympic Sports datasets.
Deconvolutional networks

Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information which destroys cues such as edge intersections, parallelism and symmetry. We present a learning framework where features that capture these mid-level cues spontaneously emerge from image data. Our approach is based on the convolutional decomposition of images under a spar-sity constraint and is totally unsupervised. By building a hierarchy of such decompositions we can learn rich feature sets that are a robust image representation for both the analysis and synthesis of images.
Mobility modeling in wireless networks: categorization, smooth movement, and border effects

The movement pattern of mobile users plays an important role in performance analysis of wireless computer and communication networks. In this paper, we first give an overview and classification of mobility models used for simulation-based studies. Then, we present an enhanced random mobility model, which makes the movement trace of mobile stations more realistic than common approaches for random mobility. Our movement concept is based on random processes for speed and direction control in which the new values are correlated to previous ones. Upon a speed change event, a new target speed is chosen, and an acceleration is set to achieve this target speed. The principles for direction changes are similar. Finally, we discuss strategies for the stations' border behavior (i.e., what happens when nodes move out of the simulation area) and show the effects of certain border behaviors and mobility models on the spatial user distribution.
Link Prediction Across Multiple Social Networks

The problem of link prediction has been studied extensively in literature. There are various versions of the link prediction problem \textit{e.g.,} link existence problem, link removal problem, predicting edge weights over time etc. In this paper we describe a new type of link prediction problem called the Inter-network link-prediction problem where the task is to predict links \textit{across} different networks. Thus given a set of nodes which participate in multiple networks the task is to determine if one can predict the edges that occur in one network by only using node attribute and edge information from other networks. We use insights from theories of evolution of social communication networks and the MTML framework to derive models which can be used to make link predictions across networks. For the experiments data from different \textit{types} of social networks from a Massively Multiplayer Online Role Playing Game (MMORPG) is used.
Investor Protection and Corporate Valuation

We present a model of the effects of legal protection of minority shareholders and of cash-f low ownership by a controlling shareholder on the valuation of firms. We then test this model using a sample of 539 large firms from 27 wealthy economies. Consistent with the model, we find evidence of higher valuation of firms in countries with better protection of minority shareholders and in firms with higher cashf low ownership by the controlling shareholder. RECENT RESEARCH SUGGESTS THAT THE EXTENT of legal protection of investors in a country is an important determinant of the development of its financial markets. Where laws are protective of outside investors and well enforced, investors are willing to finance firms, and financial markets are both broader and more valuable. In contrast, where laws are unprotective of investors, the development of financial markets is stunted. Moreover, systematic differences among countries in the structure of laws and their enforcement, such as the historical origin of their laws, account for the differences in financial development ~La Porta et al. ~1997, 1998!!. How does better protection of outside investors ~both shareholders and creditors! promote financial market development? When their rights are better protected by the law, outside investors are willing to pay more for financial assets such as equity and debt. They pay more because they recognize that, with better legal protection, more of the firm’s profits would come back to them as interest or dividends as opposed to being expropriated by the entrepreneur who controls the firm. By limiting expropriation, the law raises the price that securities fetch in the marketplace. In turn, this enables more entrepreneurs to finance their investments externally, leading to the expansion of financial markets. Although the ultimate benefit of legal investor protection for financial development has now been well documented, the effect of protection on valuation has received less attention. In this paper, we present a theoretical and empirical analysis of this effect.  La Porta and Shleifer are from Harvard University, Lopez-de-Silanes from Yale University, and Vishny from the University of Chicago. We thank Altan Sert and Ekaterina Trizlova for research assistance, Malcolm Baker, Simeon Djankov, Edward Glaeser, Simon Johnson, René Stulz, Daniel Wolfenzon, Jeff Wurgler, Luigi Zingales, and three anonymous referees for comments, the NSF for support of this research.
Extracting Argument and Domain Words for Identifying Argument Components in Texts

Argument mining studies in natural language text often use lexical (e.g. n-grams) and syntactic (e.g. grammatical production rules) features with all possible values. In prior work on a corpus of academic essays, we demonstrated that such large and sparse feature spaces can cause difficulty for feature selection and proposed a method to design a more compact feature space. The proposed feature design is based on post-processing a topic model to extract argument and domain words. In this paper we investigate the generality of this approach, by applying our methodology to a new corpus of persuasive essays. Our experiments show that replacing n-grams and syntactic rules with features and constraints using extracted argument and domain words significantly improves argument mining performance for persuasive essays.
On the Biomimetic Design of the Berkeley Lower Extremity Exoskeleton (BLEEX)

Many places in the world are too rugged or enclosed for vehicles to access. Even today, material transport to such areas is limited to manual labor and beasts of burden. Modern advancements in wearable robotics may make those methods obsolete. Lower extremity exoskeletons seek to supplement the intelligence and sensory systems of a human with the significant strength and endurance of a pair of wearable robotic legs that support a payload. This paper outlines the use of Clinical Gait Analysis data as the framework for the design of such a system at UC Berkeley.
Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics"

In this work, we use foursquare check-ins to cluster users via topic modeling, a technique commonly used to classify text documents according to latent "themes". Here, however, the latent variables which group users can be thought of not as themes but rather as factors which drive check in behaviors, allowing for a qualitative understanding of influences on user check ins. Our model is agnostic of geo-spatial location, time, users' friends on social networking sites and the venue categories-we treat the existence of and intricate interactions between these factors as being latent, allowing them to emerge entirely from the data. We instantiate our model on data from New York and the San Francisco Bay Area and find evidence that the model is able to identify groups of people which are of different types (e.g. tourists), communities (e.g. users tightly clustered in space) and interests (e.g. people who enjoy athletics).
Target-driven visual navigation in indoor scenes using deep reinforcement learning

Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new goals, and (2) data inefficiency, i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows better generalization. To address the second issue, we propose the AI2-THOR framework, which provides an environment with high-quality 3D scenes and a physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently. We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), (4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment.