nils-herrmann/clean_scirepeval huggingface dataset:
On the Automated Classification of Web Sites

In this paper we discuss several issues related to automated text classification of web sites. We analyze the nature of web content and metadata in relation to requirements for text features. We find that HTML metatags are a good source of text features, but are not in wide use despite their role in search engine rankings. We present an approach for targeted spidering including metadata extraction and opportunistic crawling of specific semantic hyperlinks. We describe a system for automatically classifying web sites into industry categories and present performance results based on different combinations of text features and training data. This system can serve as the basis for a generalized framework for automated metadata creation.
The Ef cient Market Hypothesis and Its Critics

A generation ago, the ef cient market hypothesis was widely accepted by academic  nancial economists; for example, see Eugene Fama’s (1970) in uential survey article, “Ef cient Capital Markets.” It was generally believed that securities markets were extremely ef cient in re ecting information about individual stocks and about the stock market as a whole. The accepted view was that when information arises, the news spreads very quickly and is incorporated into the prices of securities without delay. Thus, neither technical analysis, which is the study of past stock prices in an attempt to predict future prices, nor even fundamental analysis, which is the analysis of  nancial information such as company earnings and asset values to help investors select “undervalued” stocks, would enable an investor to achieve returns greater than those that could be obtained by holding a randomly selected portfolio of individual stocks, at least not with comparable risk. The ef cient market hypothesis is associated with the idea of a “random walk,” which is a term loosely used in the  nance literature to characterize a price series where all subsequent price changes represent random departures from previous prices. The logic of the random walk idea is that if the  ow of information is unimpeded and information is immediately re ected in stock prices, then tomorrow’s price change will re ect only tomorrow’s news and will be independent of the price changes today. But news is by de nition unpredictable, and, thus, resulting price changes must be unpredictable and random. As a result, prices fully re ect all known information, and even uninformed investors buying a diversied portfolio at the tableau of prices given by the market will obtain a rate of return as generous as that achieved by the experts.
Controllable Procedural Content Generation via Constrained Multi-Dimensional Markov Chain Sampling

Statistical models, such as Markov chains, have recently started to be studied for the purpose of Procedural Content Generation (PCG). A major problem with this approach is controlling the sampling process in order to obtain output satisfying some desired constraints. In this paper we present three approaches to constraining the content generated using multi-dimensional Markov chains: (1) a generate and test approach that simply resamples the content until the desired constraints are satisfied, (2) an approach that finds and resamples parts of the generated content that violate the constraints, and (3) an incremental method that checks for constraint violations during sampling. We test our approaches by generating maps for two classic video games, Super Mario Bros. and Kid Icarus.
Single‐cell sequencing maps gene expression to mutational phylogenies in PDGF‐ and EGF‐driven gliomas

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor. Epidermal growth factor (EGF) and platelet-derived growth factor (PDGF) receptors are frequently amplified and/or possess gain-of-function mutations in GBM However, clinical trials of tyrosine-kinase inhibitors have shown disappointing efficacy, in part due to intra-tumor heterogeneity. To assess the effect of clonal heterogeneity on gene expression, we derived an approach to map single-cell expression profiles to sequentially acquired mutations identified from exome sequencing. Using 288 single cells, we constructed high-resolution phylogenies of EGF-driven and PDGF-driven GBMs, modeling transcriptional kinetics during tumor evolution. Descending the phylogenetic tree of a PDGF-driven tumor corresponded to a progressive induction of an oligodendrocyte progenitor-like cell type, expressing pro-angiogenic factors. In contrast, phylogenetic analysis of an EGFR-amplified tumor showed an up-regulation of pro-invasive genes. An in-frame deletion in a specific dimerization domain of PDGF receptor correlates with an up-regulation of growth pathways in a proneural GBM and enhances proliferation when ectopically expressed in glioma cell lines. In-frame deletions in this domain are frequent in public GBM data.
Intelligence Without Reason

Computers and Thought are the two categories that together de ne Arti cial Intelligence as a discipline. It is generally accepted that work in Arti cial Intelligence over the last thirty years has had a strong in uence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong in uence on our models of thought. The Von Neumann model of computation has lead Arti cial Intelligence in particular directions. Intelligence in biological systems is completely di erent. Recent work in behavior-based Arti cial Intelligence has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation.  This report describes research done at the Arti cial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for this research was provided in part by the University Research Initiative under O ce of Naval Research contract N00014{86{K{0685, in part by the Advanced Research Projects Agency under O ce of Naval Research contract N00014{ 85{K{0124, in part by the Hughes Arti cial Intelligence Center, in part by Siemens Corporation, and in part by Mazda Corporation.
Neural Networks - A Systematic Introduction

In what case do you like reading so much? What about the type of the neural networks a systematic introduction book? The needs to read? Well, everybody has their own reason why should read some books. Mostly, it will relate to their necessity to get knowledge from the book and want to read just to get entertainment. Novels, story book, and other entertaining books become so popular this day. Besides, the scientific books will also be the best reason to choose, especially for the students, teachers, doctors, businessman, and other professions who are fond of reading.
Fitting Tweedie ’ s Compound Poisson Model to Insurance Claims Data : Dispersion Modelling

We reconsider the problem of producing fair and accurate tariffs based on aggregated insurance data giving numbers of claims and total costs for the claims. Jørgensen and de Souza (Scand.  1994) assumed Poisson arrival of claims and gamma distributed costs for individual claims. Jørgensen and de Souza (1994) directly modelled the risk or expected cost of claims per insured unit, μ say. They observed that the dependence of the likelihood function on μ is as for a linear exponential family, so that modelling similar to that of generalized linear models is possible. In this paper we observe that, when modelling the cost of insurance claims, it is generally necessary to model the dispersion of the costs as well as their mean. In order to model the dispersion we use the framework of double generalized linear models. Modelling the dispersion increases the precision of the estimated tariffs. The use of double generalized linear models also allows us to handle the case where only the total cost of claims and not the number of claims has been recorded.
Collective Opinion Spam Detection: Bridging Review Networks and Metadata

Online reviews capture the testimonials of "real" people and help shape the decisions of other consumers. Due to the financial gains associated with positive reviews, however, opinion spam has become a widespread problem, with often paid spam reviewers writing fake reviews to unjustly promote or demote certain products or businesses. Existing approaches to opinion spam have successfully but separately utilized linguistic clues of deception, behavioral footprints, or relational ties between agents in a review system.
 In this work, we propose a new holistic approach called SPEAGLE that utilizes clues from all metadata (text, timestamp, rating) as well as relational data (network), and harness them collectively under a unified framework to spot suspicious users and reviews, as well as products targeted by spam. Moreover, our method can efficiently and seamlessly integrate semi-supervision, i.e., a (small) set of labels if available, without requiring any training or changes in its underlying algorithm. We demonstrate the effectiveness and scalability of SPEAGLE on three real-world review datasets from Yelp.com with filtered (spam) and recommended (non-spam) reviews, where it significantly outperforms several baselines and state-of-the-art methods. To the best of our knowledge, this is the largest scale quantitative evaluation performed to date for the opinion spam problem.
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

D EEP learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013 . Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data . To date, it is emerging as the leading machine-learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid-level and high-level abstractions obtained from raw data (e.g., images). Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications. Promising results are emerging. In medical imaging, the accurate diagnosis and/or assessment of a disease depends on both image acquisition and image interpretation. Image acquisition has improved substantially over recent years, with devices acquiring data at faster rates and increased resolution. The image interpretation process, however, has only recently begun to benefit from computer technology. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. Many diagnostic tasks require an initial search process to detect abnormalities, and to quantify measurements and changes over time. Computerized tools, specifically image analysis and machine learning, are the key enablers to improve diagnosis, by facilitating identification of the findings that require treatment and to support the expert’s workflow. Among these tools, deep learning is rapidly proving to be the state-of-the-art foundation, leading to improved accuracy. It has also opened up new frontiers in data analysis with rates of progress not before experienced.
On the segmentation of 3D LIDAR point clouds

This paper presents a set of segmentation methods for various types of 3D point clouds. Segmentation of dense 3D data (e.g. Riegl scans) is optimised via a simple yet efficient voxelisation of the space. Prior ground extraction is empirically shown to significantly improve segmentation performance. Segmentation of sparse 3D data (e.g. Velodyne scans) is addressed using ground models of non-constant resolution either providing a continuous probabilistic surface or a terrain mesh built from the structure of a range image, both representations providing close to real-time performance. All the algorithms are tested on several hand labeled data sets using two novel metrics for segmentation evaluation.
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks

We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within the pooling region. The approach is hyper-parameter free and can be combined with other regularization approaches, such as dropout and data augmentation. We achieve state-of-the-art performance on four image datasets, relative to other approaches that do not utilize data augmentation.
Underwater acoustic sensor networks: research challenges

Underwater sensor nodes will find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. Moreover, unmanned or autonomous underwater vehicles (UUVs, AUVs), equipped with sensors, will enable the exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area. In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the characteristics of the underwater channel are detailed. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed and a cross-layer approach to the integration of all communication functionalities is suggested. Furthermore, open research issues are discussed and possible solution approaches are outlined.
Sparse PCA : Convex Relaxations , Algorithms and Applications

Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. Unfortunately, this problem is also combinatorially hard and we discuss convex relaxation techniques that efficiently produce good approximate solutions. We then describe several algorithms solving these relaxations as well as greedy algorithms that iteratively improve the solution quality. Finally, we illustrate sparse PCA in several applications, ranging from senate voting and finance to news data.
High-order entropy-compressed text indexes

We present a novel implementation of compressed suffix arrays exhibiting new tradeoffs between search time and space occupancy for a given text (or sequence) of <i>n</i> symbols over an alphabet σ, where each symbol is encoded by lg|σ| bits. We show that compressed suffix arrays use just nH<inf>h</inf> + σ bits, while retaining full text indexing functionalities, such as searching any pattern sequence of length <i>m</i> in <i>O</i>(<i>m</i> lg |σ| + polylog(<i>n</i>)) time. The term H<inf>h</inf> ≤ lg |σ| denotes the hth-order empirical entropy of the text, which means that our index is nearly optimal in space apart from lower-order terms, achieving asymptotically the empirical entropy of the text (with a multiplicative constant 1). If the text is highly compressible so that H<inf>n</inf> = o(1) and the alphabet size is small, we obtain a text index with o(m) search time that requires only o(n) bits. Further results and tradeoffs are reported in the paper.
Social Turing Tests: Crowdsourcing Sybil Detection

As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effec tively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explor e the feasibility of acrowdsourcedSybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today’s Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both “experts” and “turkers” under a variety of conditions, and find that while turkers vary significantly in their effective ness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools.
An Evaluation Of N-gram System Call Sequence In Mobile Malware Detection

The rapid growth of Android-based mobile devices technology in recent years has increased the proliferation of mobile devices throughout the community at large. The ability of Android mobile devices has become similar to its desktop environment; users can do more than just a phone call and short text messaging. These days, Android mobile devices are used for various applications such as web browsing, ubiquitous services, social networking, MMS and many more. However, the rapid growth of Android mobile devices technology has also triggered the malware author to start exploiting the vulnerabilities of the devices. Based on this reason, this paper explores mobile malware detection through an n-gram system call sequence which uses a sequence of system call invoked by the mobile application as the feature in classifying a benign and malicious mobile application. Several n-gram values are evaluated with Linear-SVM classifier to determine the best n system call sequence that produces the highest detection accuracy and highest True Positive Rate (TPR) with low False Positive Rate (FPR).
An autonomous robotic assistant for drinking

Stroke and neurodegenerative diseases, among a range of other neurologic disorders, can cause chronic paralysis. Patients suffering from paralysis may remain unable to achieve even basic everyday tasks such as liquid intake. Currently, there is a great interest in developing robotic assistants controlled via brain-machine interfaces (BMIs) to restore the ability to perform such tasks. This paper describes an autonomous robotic assistant for liquid intake. The components of the system include autonomous online detection both of the cup to be grasped and of the mouth of the user. It plans motions of the robot arm under the constraints that the cup stays upright while moving towards the mouth and that the cup stays in direct contact with the user's mouth while the robot tilts it during the drinking phase. To achieve this, our system also includes a technique for online estimation of the location of the user's mouth even under partial occlusions by the cup or robot arm. We tested our system in a real environment and in a shared-control setting using frequency-specific modulations recorded by electroencephalography (EEG) from the brain of the user. Our experiments demonstrate that our BMI-controlled robotic system enables a reliable liquid intake. We believe that our approach can easily be extended to other useful tasks including food intake and object manipulation.
An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex.

EEG oscillations are hypothesized to reflect cyclical variations in the neuronal excitability, with particular frequency bands reflecting differing spatial scales of brain operation. However, despite decades of clinical and scientific investigation, there is no unifying theory of EEG organization, and the role of ongoing activity in sensory processing remains controversial. This study analyzed laminar profiles of synaptic activity [current source density CSD] and multiunit activity (MUA), both spontaneous and stimulus-driven, in primary auditory cortex of awake macaque monkeys. Our results reveal that the EEG is hierarchically organized; delta (1-4 Hz) phase modulates theta (4-10 Hz) amplitude, and theta phase modulates gamma (30-50 Hz) amplitude. This oscillatory hierarchy controls baseline excitability and thus stimulus-related responses in a neuronal ensemble. We propose that the hierarchical organization of ambient oscillatory activity allows auditory cortex to structure its temporal activity pattern so as to optimize the processing of rhythmic inputs.
A bioinspired multi-modal flying and walking robot.

With the aim to extend the versatility and adaptability of robots in complex environments, a novel multi-modal flying and walking robot is presented. The robot consists of a flying wing with adaptive morphology that can perform both long distance flight and walking in cluttered environments for local exploration. The robot's design is inspired by the common vampire bat Desmodus rotundus, which can perform aerial and terrestrial locomotion with limited trade-offs. Wings' adaptive morphology allows the robot to modify the shape of its body in order to increase its efficiency during terrestrial locomotion. Furthermore, aerial and terrestrial capabilities are powered by a single locomotor apparatus, therefore it reduces the total complexity and weight of this multi-modal robot.
Data Mining in Healthcare : Current Applications and Issues By

The successful application of data mining in highly visible fields like e-business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just discovering data mining are the fields of medicine and public health. This research paper provides a survey of current techniques of KDD, using data mining tools for healthcare and public health. It also discusses critical issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths, and detection of fraudulent insurance claims.
MEMS based hair flow-sensors as model systems for acoustic perception studies.

Arrays of MEMS fabricated flow sensors inspired by the acoustic flow-sensitive hairs found on the cerci of crickets have been designed, fabricated and characterized. The hairs consist of up to 1 mm long SU-8 structures mounted on suspended membranes with normal translational and rotational degrees of freedom. Electrodes on the membrane and on the substrate form variable capacitors, allowing for capacitive read-out. Capacitance versus voltage, frequency dependence and directional sensitivity measurements have been successfully carried out on fabricated sensor arrays, showing the viability of the concept. The sensors form a model system allowing for investigations on sensory acoustics by their arrayed nature, their adaptivity via electrostatic interaction (frequency tuning and parametric amplification) and their susceptibility to noise (stochastic resonance).