Query: Stylometric Analysis of Scientific Articles

Relevant passages:
Scientific Paper Summarization Using Citation Summary Networks <=SEP=> Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others’ viewpoint of the target article’s contributions and the study of its citation summary network using a clustering approach.

Finding Deceptive Opinion Spam by Any Stretch of the Imagination <=SEP=> Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam—fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.

Transductive Inference for Text Classification using Support Vector Machines <=SEP=> This paper introduces Transductive Support Vector Machines (TSVMs) for text classi cation. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimize misclassi cations of just those particular examples. The paper presents an analysis of why TSVMs are well suited for text classi cation. These theoretical ndings are supported by experiments on three test collections. The experiments show substantial improvements over inductive methods, especially for small training sets, cutting the number of labeled training examples down to a twentieth on some tasks. This work also proposes an algorithm for training TSVMs e ciently, handling 10,000 examples and more.

Thumbs up? Sentiment Classification using Machine Learning Techniques <=SEP=> We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging. Publication info: Proceedings of EMNLP 2002, pp. 79–86.

Judging Grammaticality with Tree Substitution Grammar Derivations <=SEP=> In this paper, we show that local features computed from the derivations of tree substitution grammars — such as the identify of particular fragments, and a count of large and small fragments — are useful in binary grammatical classification tasks. Such features outperform n-gram features and various model scores by a wide margin. Although they fall short of the performance of the hand-crafted feature set of Charniak and Johnson (2005) developed for parse tree reranking, they do so with an order of magnitude fewer features. Furthermore, since the TSGs employed are learned in a Bayesian setting, the use of their derivations can be viewed as the automatic discovery of tree patterns useful for classification. On the BLLIP dataset, we achieve an accuracy of 89.9% in discriminating between grammatical text and samples from an n-gram language model.

Code Review Quality: How Developers See It <=SEP=> In a large, long-lived project, an effective code review process is key to ensuring the long-term quality of the code base. In this work, we study code review practices of a large, open source project, and we investigate how the developers themselves perceive code review quality. We present a qualitative study that summarizes the results from a survey of 88 Mozilla core developers. The results provide developer insights into how they define review quality, what factors contribute to how they evaluate submitted code, and what challenges they face when performing review tasks. We found that the review quality is primarily associated with the thoroughness of the feedback, the reviewer's familiarity with the code, and the perceived quality of the code itself. Also, we found that while different factors are perceived to contribute to the review quality, reviewers often find it difficult to keep their technical skills up-to-date, manage personal priorities, and mitigate context switching.

EPOCHS: a platform for agent-based electric power and communication simulation built from commercial off-the-shelf components <=SEP=> This paper reports on the development and subsequent use of the electric power and communication synchronizing simulator (EPOCHS), a distributed simulation environment. Existing electric power simulation tools accurately model power systems of the past, which were controlled as large regional power pools without significant communication elements. However, as power systems increasingly turn to protection and control systems that make use of computer networks, these simulators are less and less capable of predicting the likely behavior of the resulting power grids. Similarly, the tools used to evaluate new communication protocols and systems have been developed without attention to the roles they might play in power scenarios. EPOCHS integrates multiple research and commercial off-the-shelf systems to bridge the gap.

Closed-form Solution for IMU based LSD-SLAM Point Cloud Conversion into the Scaled 3D World Environment <=SEP=> SLAM is a very popular research stream in computer vision and robotics nowadays. For more effective SLAM implementation it is necessary to have reliable information about the environment, also the data should be aligned and scaled according to the real world coordinate system. Monocular SLAM research is an attractive sub-stream, because of the low equipment cost, size and weight. In this paper we present a way to build a conversion from LSD-SLAM coordinate space to the real world coordinates using a true metric scale with IMU sensor data implementation. The causes of differences between the real and calculated spaces are explained and the possibility of conversions between the spaces is proved. Additionally, a closed-form solution for inter space transformation calculation is presented. The synthetic method of generating high level accurate and well controlled input data for the LSD-SLAM algorithm is presented. Finally, the reconstructed 3D environment representation is delivered as an output of the implemented conversion.

Scalable Iterative Classification for Sanitizing Large-Scale Datasets <=SEP=> Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose personally identifiable information. Much of this data exhibits weak structure (e.g., text), such that machine learning approaches have been developed to detect and remove identifiers from it. While learning is never perfect, and relying on such approaches to sanitize data can leak sensitive information, a small risk is often acceptable. Our goal is to balance the value of published data and the risk of an adversary discovering leaked identifiers. We model data sanitization as a game between 1) a publisher who chooses a set of classifiers to apply to data and publishes only instances predicted as non-sensitive and 2) an attacker who combines machine learning and manual inspection to uncover leaked identifying information. We introduce a fast iterative greedy algorithm for the publisher that ensures a low utility for a resource-limited adversary. Moreover, using five text data sets we illustrate that our algorithm leaves virtually no automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93 percent of the original data, and completes after at most five iterations.

The diverse CB1 and CB2 receptor pharmacology of three plant cannabinoids: delta9-tetrahydrocannabinol, cannabidiol and delta9-tetrahydrocannabivarin. <=SEP=> Cannabis sativa is the source of a unique set of compounds known collectively as plant cannabinoids or phytocannabinoids. This review focuses on the manner with which three of these compounds, (-)-trans-delta9-tetrahydrocannabinol (delta9-THC), (-)-cannabidiol (CBD) and (-)-trans-delta9-tetrahydrocannabivarin (delta9-THCV), interact with cannabinoid CB1 and CB2 receptors. Delta9-THC, the main psychotropic constituent of cannabis, is a CB1 and CB2 receptor partial agonist and in line with classical pharmacology, the responses it elicits appear to be strongly influenced both by the expression level and signalling efficiency of cannabinoid receptors and by ongoing endogenous cannabinoid release. CBD displays unexpectedly high potency as an antagonist of CB1/CB2 receptor agonists in CB1- and CB2-expressing cells or tissues, the manner with which it interacts with CB2 receptors providing a possible explanation for its ability to inhibit evoked immune cell migration. Delta9-THCV behaves as a potent CB2 receptor partial agonist in vitro. In contrast, it antagonizes cannabinoid receptor agonists in CB1-expressing tissues. This it does with relatively high potency and in a manner that is both tissue and ligand dependent. Delta9-THCV also interacts with CB1 receptors when administered in vivo, behaving either as a CB1 antagonist or, at higher doses, as a CB1 receptor agonist. Brief mention is also made in this review, first of the production by delta9-THC of pharmacodynamic tolerance, second of current knowledge about the extent to which delta9-THC, CBD and delta9-THCV interact with pharmacological targets other than CB1 or CB2 receptors, and third of actual and potential therapeutic applications for each of these cannabinoids.

Design of an IGBT-based LCL-Resonant Inverter for High-Frequency Induction Heating <=SEP=> A power electronic inverter is developed for a high-frequency induction heating application. The application requires up to 160kW of power at a frequency of 1OOkHz. This power-frequency product represents a significant challenge for today's power semiconductor technology. Voltage source and current source inverters both using ZCS or ZVS are analyzed and compared. To attain the level of performance required, an LCL loadresonant topology is selected to enable ZVS close to the zero current crossing of the load. This mode of softswitching is suitable to greatly reduce the IGBT losses. Inverter control is achieved via a Phase Locked Loop (PLL). This paper presents the circuit design, modeling and control considerations.

Parents with doubts about vaccines: which vaccines and reasons why. <=SEP=> OBJECTIVES
The goals were (1) to obtain national estimates of the proportions of parents with indicators of vaccine doubt, (2) to identify factors associated with those parents, compared with parents reporting no vaccine doubt indicators, (3) to identify the specific vaccines that prompted doubt and the reasons why, and (4) to describe the main reasons parents changed their minds about delaying or refusing a vaccine for their child.


METHODS
Data were from the National Immunization Survey (2003-2004). Groups included parents who ever got a vaccination for their child although they were not sure it was the best thing to do ("unsure"), delayed a vaccination for their child ("delayed"), or decided not to have their child get a vaccination ("refused").


RESULTS
A total of 3924 interviews were completed. Response rates were 57.9% in 2003 and 65.0% in 2004. Twenty-eight percent of parents responded yes to ever experiencing >or=1 of the outcome measures listed above. In separate analyses for each outcome measure, vaccine safety concern was a predictor for unsure, refused, and delayed parents. The largest proportions of unsure and refused parents chose varicella vaccine as the vaccine prompting their concern, whereas delayed parents most often reported "not a specific vaccine" as the vaccine prompting their concern. Most parents who delayed vaccines for their child did so for reasons related to their child's illness, unlike the unsure and refused parents. The largest proportion of parents who changed their minds about delaying or not getting a vaccination for their child listed "information or assurances from health care provider" as the main reason.


CONCLUSIONS
Parents who exhibit doubts about immunizations are not all the same. This research suggests encouraging children's health care providers to solicit questions about vaccines, to establish a trusting relationship, and to provide appropriate educational materials to parents.

A Review on Comparative analysis of different clustering and Decision Tree for Synthesized Data Mining Algorithm <=SEP=> Web mining is the sub category or application of data mining techniques to extract knowledge from Web data. With the advent of new advancements in technology the rapid use of new algorithms has been increased in the market. A data mining is one of the fast growing research field which is used in a wide areas of applications. The data mining consists of classification algorithms, association algorithms and searching algorithms. Different classification and clustering algorithm are used for the synthetic datasets. In this paper various techniques that are based on clustering and decision tree for synthesized data mining are discussed.

Poor Man's Methadone: A Case Report of Loperamide Toxicity. <=SEP=> Loperamide, a common over-the-counter antidiarrheal drug and opioid derivative, is formulated to act upon intestinal opioid receptors. However, at high doses, loperamide crosses the blood-brain barrier and reaches central opioid receptors in the brain, leading to central opiate effects including euphoria and respiratory depression. We report the case of a young man found dead in his residence with a known history of drug abuse. At autopsy, the only significant findings were a distended bladder and bloody oral purge. Drug screening found nontoxic levels of alprazolam, fluoxetine, and marijuana metabolites. Liquid chromatography time-of-flight mass spectrometry found an unusual set of split isotope peaks consistent with chlorine. On the basis of autopsy and toxicological findings, loperamide toxicity was suspected because of its opioid properties and molecular formula containing chlorine. A sample of loperamide was analyzed by liquid chromatography time-of-flight mass spectrometry, resulting in a matching mass and retention time to the decedent's sample. Subsequently, quantitative testing detected 63 ng/mL of loperamide or more than 6 times of therapeutic peak concentration. Cause of death was determined as "toxic effects of loperamide with fluoxetine and alprazolam." Because of its opioid effects and easy accessibility, loperamide is known as "poor man's methadone" and may go undetected at medical and forensic drug screening.

Feature Extraction and Classification for Automatic Speaker Recognition System – A Review <=SEP=> Automatic speaker recognition (ASR) has found immense applications in the industries like banking, security, forensics etc. for its advantages such as easy implementation, more secure, more user friendly. To have a good recognition rate is a pre-requisite for any ASR system which can be achieved by making an optimal choice among the available techniques for ASR. In this paper, different techniques for the system have been discussed such as MFCC, LPCC, LPC, Wavelet decomposition for feature extraction and VQ, GMM, SVM, DTW, HMM for feature classification. All these techniques are also compared with each other to find out best suitable candidate among them. On the basis of the comparison done, MFCC has upper edge over other techniques for feature extraction as it is more consistent with human hearing. GMM comes out to be the best among classification models due to its good classification accuracy and less memory usage. Keywords— Automatic Speaker Recognition, Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients, Gaussian Mixture Model ( GMM), Vector Quantization ( VQ), Dynamic Time Warping (DTW), Hidden Markov Model ( HMM), Wavelet decomposition

Learning to Identify Review Spam <=SEP=> In the past few years, sentiment analysis and opinion mining becomes a popular and important task. These studies all assume that their opinion resources are real and trustful. However, they may encounter the faked opinion or opinion spam problem. In this paper, we study this issue in the context of our product review mining system. On product review site, people may write faked reviews, called review spam, to promote their products, or defame their competitors’ products. It is important to identify and filter out the review spam. Previous work only focuses on some heuristic rules, such as helpfulness voting, or rating deviation, which limits the performance of this task. In this paper, we exploit machine learning methods to identify review spam. Toward the end, we manually build a spam collection from our crawled reviews. We first analyze the effect of various features in spam identification. We also observe that the review spammer consistently writes spam. This provides us another view to identify review spam: we can identify if the author of the review is spammer. Based on this observation, we provide a twoview semi-supervised method, co-training, to exploit the large amount of unlabeled data. The experiment results show that our proposed method is effective. Our designed machine learning methods achieve significant improvements in comparison to the heuristic baselines.

Enhanced cardiac perception is associated with benefits in decision-making. <=SEP=> In the present study we provide the first empirical evidence that viscero-sensory feedback from an internal organ is associated with decision-making processes. Participants with accurate vs. poor perception of their heart activity were compared with regard to their performance in the Iowa Gambling Task. During this task, participants have to choose between four card decks. Decks A and B yield high gains and high losses, and if played continuously, result in net loss. In contrast, decks C and D yield small gains and also small losses, but result in net profit if they are selected continuously. Accordingly, participants have to learn to avoid the net loss options in favor of the net gain options. In our study, participants with good cardiac perception chose significantly more of the net gain and fewer of the net loss options. Our findings document the substantial role of visceral feedback in decision-making processes in complex situations.

Novel blood pressure estimation method using single photoplethysmography feature <=SEP=> Continuous blood pressure (BP) monitoring has a significant meaning to the prevention and early diagnosis of cardiovascular disease. However, existing continuous BP monitoring approaches, especially cuff-less BP monitoring approaches, are all contraptions which complex and huge computation required. For example, for the most sophisticated cuff-less BP monitoring method using pulse transit time (PTT), the simultaneous record of photoplethysmography (PPG) signal and electrocardiography (ECG) are required, and various measurement of characteristic points are needed. These issues hindered widely application of cuff less BP measurement in the wearable devices. In this study, a novel BP estimation method using single PPG signal feature was proposed and its performance in BP estimation was also tested. The results showed that the new approach proposed in this study has a mean error −0.91 ± 3.84 mmHg for SBP estimation and −0.36 ± 3.36 mmHg for DBP estimation respectively. This approach performed better than the traditional PTT based BP estimation, which mean error for SBP estimation was −0.31 ± 4.78 mmHg, and for DBP estimation was −0.18 ± 4.32 mmHg. Further investigation revealed that this new BP estimation approach only required measurement of one characteristic point, reducing much computation when implementing. These results demonstrated that this new approach might be more suitable implemented in the wearable BP monitoring devices.

Wideband printed rectangular monopole antenna for circularly polarization <=SEP=> In this paper, a printed monopole antenna for circular polarization has been proposed. The relationship between the geometry of the antenna and the axial ratio is investigated. A wideband circularly polarized antenna is designed according to the parametric studies. The simulated bandwidth of 3dB-axial ratio is from 2.06GHz to 6.04GHz (98%).

An Artificial Neural Network based Intrusion Detection System and Classification of Attacks <=SEP=> Network security is becoming an issue of paramount importance in the information technology era. Nowadays with the dramatic growth of communication and computer networks, security has become a critical subject for computer system. Intrusion detection is the art of detecting computer abuse and any attempt to break the networks. Intrusion detection system is an effective security tool that helps to prevent unauthorized access to network resources by analyzing the network traffic. Different algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize in two groups, normal or threat. One of the most promising areas of research in the area of Intrusion Detection deals with the applications of the Artificial Intelligence (AI) techniques. This proposed system presents a new approach of intrusion detection system based on artificial neural network. Multi Layer Percepron (MLP) architecture is used for Intrusion Detection System. The performance and evaluations are performed by using the set of benchmark data from a KDD (Knowledge discovery in Database) dataset. The proposed system detects the attacks and classifies them in six groups.

What can quantum theory bring to information retrieval <=SEP=> The probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be based on the notion of information need vector spaces where events, such as document relevance or observed user interactions, correspond to subspaces. As in quantum theory, a probability distribution over these subspaces is defined through weighted sets of state vectors (density operators), and used to represent the current view of the retrieval system on the user information need. Tensor spaces can be used to capture different aspects of information needs. Our evaluation shows that the framework can lead to acceptable performance in an ad-hoc retrieval task. Going beyond this, we discuss the potential of the framework for three active challenges in information retrieval, namely, interaction, novelty and diversity.

On the Traveling Salesman Problem with Simple Temporal Constraints <=SEP=> Many real-world applications require the successful combination of spatial and temporal reasoning. In this paper, we study the general framework of the Traveling Salesman Problem with Simple Temporal Constraints. Representationally, this framework subsumes the Traveling Salesman Problem, Simple Temporal Problems, as well as many of the frameworks described in the literature. We analyze the theoretical properties of the combined problem providing strong inapproximability results for the general problem, and positive results for

The Formulation of Parameters for Type Design of Indian Script Based on Calligraphic Studies <=SEP=> A number of parameters were formulated for better analysing the anatomy of Indic letterforms. This methodology contributes to the understanding of the intricacies of type design of complex Indian scripts.

Using association rules to guide a search for best fitting transfer models of student learning <=SEP=> We say a transfer model is a mapping between the questions in an intelligent tutoring system and the knowledge components (i.e., skills, strategies, declarative knowledge, etc) needed to answer a question correctly. [JKT00] showed how you could take advantage of 1) the Power Law Of Learning, 2) an existing transfer model, and 3) set of tutorial log files, to learn a function (using logistic regression) that will predict when a student will get a question correct. In the main conference proceeding [CHK2004] give an example of using this technique for transfer model selection. Koedinger and Junker [KJ99] also conceptualized a search space where each state is a new transfer model. The operators in this search space split, add or merge knowledge components based upon factors that are tagged to questions. Koedinger and Junker called this method learning factors analysis emphasizing that this method can be used to study learning. Unfortunately, the search space is huge and searching for good fitting transfer models is exponential. The main goal of this paper is show a technique that will make searching for transfer models more efficient. Our procedure implements a search method using association rules as a means of guiding the search. The association rules are mined from a dataset derived from student-tutor interaction logs. The association rules found in the mining process determine what operations to perform on the current transfer model. We report on the speed up achieved. Being able to find good transfer models quicker will help intelligent tutor system builders as well as cognitive science researchers better assess what makes certain problems hard and other problems easy for students.

A Survey of In-Band Full-Duplex Transmission: From the Perspective of PHY and MAC Layers <=SEP=> In-band full-duplex (IBFD) transmission represents an attractive option for increasing the throughput of wireless communication systems. A key challenge for IBFD transmission is reducing self-interference. Fortunately, the power associated with residual self-interference can be effectively canceled for feasible IBFD transmission with combinations of various advanced passive, analog, and digital self-interference cancellation schemes. In this survey paper, we first review the basic concepts of IBFD transmission with shared and separated antennas and advanced self-interference cancellation schemes. Furthermore, we also discuss the effects of IBFD transmission on system performance in various networks such as bidirectional, relay, and cellular topology networks. This survey covers a wide array of technologies that have been proposed in the literature as feasible for IBFD transmission and evaluates the performance of the IBFD systems compared to conventional half-duplex transmission in connection with theoretical aspects such as the achievable sum rate, network capacity, system reliability, and so on. We also discuss the research challenges and opportunities associated with the design and analysis of IBFD systems in a variety of network topologies. This work also explores the development of MAC protocols for an IBFD system in both infrastructure-based and ad hoc networks. Finally, we conclude our survey by reviewing the advantages of IBFD transmission when applied for different purposes, such as spectrum sensing, network secrecy, and wireless power transfer.

KP-Miner: A keyphrase extraction system for English and Arabic documents <=SEP=> Automatic key phrase extraction has many important applications including but not limited to summarization, cataloging/indexing, feature extraction for clustering and classification, and data mining. This paper presents the KP-Miner system, and demonstrates through experimentation and comparison with existing systems that the it is effective in extracting keyphrases from both English and Arabic documents of varied length. Unlike other existing keyphrase extraction systems, the KP-Miner system has the advantage of being configurable as the rules and heuristics adopted by the system are related to the general nature of documents and keyphrase. This implies that the users of this system can use their understanding of the document(s) being input into the system, to fine tune it to their particular needs.

Docker Cluster Management for the Cloud - Survey Results and Own Solution <=SEP=> Docker provides a good basis to run composite applications in the cloud, especially if those are not cloud-aware, or cloud-native. However, Docker concentrates on managing containers on one host, but SaaS providers need a container management solution for multiple hosts. Therefore, a number of tools emerged that claim to solve the problem. This paper classifies the solutions, maps them to requirements from a case study and identifies gaps and integration requirements. We close some of these gaps with our own integration components and tool enhancements, resulting in the currently most complete management suite.

Technology adoption: A conjoint analysis of consumers' preference on future online banking services <=SEP=> The importance of service delivery technology and online service adoption and usage in the banking industry has received an increased discussion in the literature in recent years. Owing to the fact that Strong online banking services are important drivers for bank performance and customer service delivery; several studies have been carried out on online banking service adoption or acceptance where services are already deployed and on the factors that influence customers' adoption and use or intention to use those services. However, despite the increasing discussion in the literatures, no attempt has been made to look at consumers' preference in terms of future online banking service adoption. This study used conjoint analysis and stated preference methods with discrete choice model to analyze the technology adoption pattern regarding consumers' preference for potential future online banking services in the Nigerian banking industry. The result revealed that to increase efficiency and strengthen competitiveness, banks need to promote smart and practical branded services especially self-services at the same time promote a universal adoption of e-banking system services that add entertainment or extra convenience to customers such as ease of usage including digital wallet, real-time interaction (video banking), ATMs integrated with smart phones, website customization, biometric services, and digital currency. These services can contribute to an increasing adoption of online services. & 2015 Elsevier Ltd. All rights reserved.

Information overload and the message dynamics of online interaction spaces: a theoretical model and empirical exploration <=SEP=> This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

Decoding actions at different levels of abstraction. <=SEP=> Brain regions that mediate action understanding must contain representations that are action specific and at the same time tolerate a wide range of perceptual variance. Whereas progress has been made in understanding such generalization mechanisms in the object domain, the neural mechanisms to conceptualize actions remain unknown. In particular, there is ongoing dissent between motor-centric and cognitive accounts whether premotor cortex or brain regions in closer relation to perceptual systems, i.e., lateral occipitotemporal cortex, contain neural populations with such mapping properties. To date, it is unclear to which degree action-specific representations in these brain regions generalize from concrete action instantiations to abstract action concepts. However, such information would be crucial to differentiate between motor and cognitive theories. Using ROI-based and searchlight-based fMRI multivoxel pattern decoding, we sought brain regions in human cortex that manage the balancing act between specificity and generality. We investigated a concrete level that distinguishes actions based on perceptual features (e.g., opening vs closing a specific bottle), an intermediate level that generalizes across movement kinematics and specific objects involved in the action (e.g., opening different bottles with cork or screw cap), and an abstract level that additionally generalizes across object category (e.g., opening bottles or boxes). We demonstrate that the inferior parietal and occipitotemporal cortex code actions at abstract levels whereas the premotor cortex codes actions at the concrete level only. Hence, occipitotemporal, but not premotor, regions fulfill the necessary criteria for action understanding. This result is compatible with cognitive theories but strongly undermines motor theories of action understanding.