nils-herrmann/clean_scirepeval huggingface dataset:
Editorial: special issue on web content mining

With the phenomenal growth of the Web, there is an everincreasing volume of data and information published in numerous Web pages. The research in Web mining aims to develop new techniques to effectively extract and mine useful knowledge or information from these Web pages . Due to the heterogeneity and lack of structure of Web data, automated discovery of targeted or unexpected knowledge/information is a challenging task. It calls for novel methods that draw from a wide range of fields spanning data mining, machine learning, natural language processing, statistics, databases, and information retrieval. In the past few years, there was a rapid expansion of activities in the Web mining field, which consists of Web usage mining, Web structure mining, and Web content mining. Web usage mining refers to the discovery of user access patterns from Web usage logs. Web structure mining tries to discover useful knowledge from the structure of hyperlinks. Web content mining aims to extract/mine useful information or knowledge from Web page contents. For this special issue, we focus on Web content mining.
Strengths and Weaknesses of Quantum Computing

Recently a great deal of attention has focused on quantum computation following a sequence of results [4, 16, 15] suggesting that quantum computers are more powerful than classical probabilistic computers. Following Shor’s result that factoring and the extraction of discrete logarithms are both solvable in quantum polynomial time, it is natural to ask whether all of NP can be efficiently solved in quantum polynomial time. In this paper, we address this question by proving that relative to an oracle chosen uniformly at random, with probability 1, the class NP cannot be solved on a quantum Turing machine in time o(2n/2). We also show that relative to a permutation oracle chosen uniformly at random, with probability 1, the class NP ∩ co–NP cannot be solved on a quantum Turing machine in time o(2n/3). The former bound is tight since recent work of Grover [13] shows how to accept the class NP relative to any oracle on a quantum computer in time O(2n/2).  ∗ IBM T. J. Watson Research Laboratory, Yorktown Heights, New York, NY 10598, USA. email: bennetc@watson.ibm.com. † 1 Microsoft Way, Redmond, WA 98052 – 6399, USA. email: ethanb@microsoft.com. ‡ Supported in part by Canada’s nserc and Québec’s fcar. §Département IRO, Université de Montréal, C.P. 6128, succursale centre-ville, Montréal (Québec), Canada H3C 3J7. email: brassard@iro.umontreal.ca. ¶ Supported by NSF Grant No. CCR-9310214. Computer Science Division, University of California, Berkeley, CA 94720, USA. email: vazirani@cs.berkeley.edu.
An ontology-based approach for the reconstruction and analysis of digital incidents timelines

Due to the democratisation of new technologies, computer forensics investigators have to deal with volumes of data which are becoming increasingly large and heterogeneous. Indeed, in a single machine, hundred of events occur per minute, produced and logged by the operating system and various software. Therefore, the identification of evidence, and more generally, the reconstruction of past events is a tedious and time-consuming task for the investigators. Our work aims at reconstructing and analysing automatically the events related to a digital incident, while respecting legal requirements. To tackle those three main problems (volume, heterogeneity and legal requirements), we identify seven necessary criteria that an efficient reconstruction tool must meet to address these challenges. This paper introduces an approach based on a three-layered ontology, called ORD2I, to represent any digital events. ORD2I is associated with a set of operators to analyse the resulting timeline and to ensure the reproducibility of the investigation.
Contemporary review of injectable facial fillers.

Perhaps the most significant change in facial rejuvenation in the last 10 years has been the introduction of nonsurgical treatments for the relaxation of facial wrinkles and for the restoration of lost volume. Fillers such as paraffin and silicone have been used in the past for volume restoration, but only recently have new fillers been developed whose safety and efficacy have been supported by clinical research. The introduction of hyaluronic acid (HA) fillers in 2003 began the filler revolution and paved the way for development of biostimulatory and permanent materials. There is an abundance of high-level evidence-based studies comparing the HA fillers, calcium hydroxylapatite, and poly(methyl methacrylate) with collagen and other HA formulations, but there is only limited high-level data evaluating poly-L-lactic acid.
Monte-Carlo Localization for Mobile Wireless Sensor Networks

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo Localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors’ radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73%, on average 30%, for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55%, on average 22%. Finally, the processing time is reduced by 93% for a similar localization accuracy.
Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web

We describe a family of caching protocols for distrib-uted networks that can be used to decrease or eliminate the occurrence of hot spots in the network. Our protocols are particularly designed for use with very large networks such as the Internet, where delays caused by hot spots can be severe, and where it is not feasible for every server to have complete information about the current state of the entire network. The protocols are easy to implement using existing network protocols such as TCP/fF’,and require very little overhead. The protocols work with local control, make efficient use of existing resources, and scale gracefully as the network grows. Our caching protocols are based on a special kind of hashing that we call consistent hashing. Roughly speaking, a consistent hash function is one which changes nr.inimaflyas the range of the function changes. Through the development of good consistent hash functions, we are able to develop caching protocols which do not require users to have a current or even consistent view of the network. We believe that consistent hash functions may eventually prove to be useful in other applications such as distributed name servers and/or quorum systems.
Can we learn to gamble efficiently

Betting is an important problem faced by millions of sports fans each day. Presented with an upcoming matchup between team A and team B, and given the opportunity to place a 50/50 wager on either, where should a gambler put her money? This decision is not, of course, made in isolation: both teams will have played a number of decided matches with other teams throughout the season. Furthermore, a reasonable assumption to make is that the relation “A tends to beat B” is transitive. Under transitivity, the best prediction strategy is clearly to sort the teams by their abilities and predict according to this ranking. The obvious difficulty is that the best ranking of the teams is not known an advance. But there’s a more subtle issue: even with knowledge of all match outcomes in advance, i.e. a list of items of the form (team X < team Y ), it’s NP-hard to determine the best ranking of the teams when the outcomes are noisy. This is exactly the infamous Minimum Feedback Arc Set problem. The question we pose is as follows: can we design a non-trivial online prediction strategy in this setting which achieves vanishing regret relative to the best ranking in hindsight, even when the latter is computationally infeasible? It is tempting to believe this is impossible, as competing with the best ranking would appear tantamount to finding the best ranking. However, this assertion is false: the algorithm need not learn a ranking explicitly, it must only output a prediction (X < Y ) or (Y < X) when presented with a pair (X, Y ), and these predictions do not necessarily have to satisfy transitivity. Indeed, consider the following simple algorithm: treat each team pair (X, Y ) as an independent learning problem (ignoring all other matchups). In the long run, this will achieve vanishing regret with respect to the best ranking. So why is this not desirable? The trivial approach unfortunately admits a bad regret bound: the algorithm must see O(n) matches per team before it can start to make decent predictions. On the other hand, there is an information-theoretic approach that requires only O(log n) observations per team—the downside, unfortunately, is that this requires exponential computation. We would like to achieve this rate with an efficient method.
Practical Techniques for Searches on Encrypted Data

It is desirable to store data on data storage servers such as mail servers and file servers in encrypted form to reduce security and privacy risks. But this usually implies that on e has to sacrifice functionality for security. For example, if a client wishes to retrieve only documents containing certai n words, it was not previously known how to let the data storage server perform the search and answer the query without loss of data confidentiality. In this paper, we describe our cryptographic schemes for the problem of searching on encrypted data and provide proofs of security for the resulting crypto systems. Ou r techniques have a number of crucial advantages. They are provably secure : they provideprovable secrecy for encryption, in the sense that the untrusted server cannot learn anything about the plaintext when only given the ciphertext; they providequery isolationfor searches, meaning that the untrusted server cannot learn anything more about the plaintext than the search result; they provide controlled searching, so that the untrusted server cannot search for an arbitrary word without the user’s authorization; they also supporthidden queries , so that the user may ask the untrusted server to search for a secret word without revealing the word to the server. The algorithms we present are simple, fast (for a document of length n, the encryption and search algorithms only need O(n) stream cipher and block cipher operations), and introduce almost no space and communication overhead, and hence are practical to use today. We gratefully acknowledge support for this research from se veral US government agencies. This research was suported in part by t he Defense Advanced Research Projects Agency under DARPA contract N66 01-9928913 (under supervision of the Space and Naval Warfare Syst ems Center San Diego), by the National Science foundation under grant F D99-79852, and by the United States Postal Service under grant USPS 1025 90-98-C3513. Views and conclusions contained in this document are t hose of the authors and do not necessarily represent the official opinio n or policies, either expressed or implied of the US government or any of its agencies, DARPA, NSF, USPS.
Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine

Sleep apnea syndrome (SAS) is prevalent in individuals and recently, there are many studies focus on using simple and efficient methods for SAS detection instead of polysomnography. However, not much work has been done on using nonlinear behavior of the electroencephalogram (EEG) signals. The purpose of this study is to find a novel and simpler method for detecting apnea patients and to quantify nonlinear characteristics of the sleep apnea. 30 min EEG scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis (DFA) and compared between six SAS and six healthy subjects during sleep. The mean scaling exponents were calculated every 30 s and 360 control values and 360 apnea values were obtained. These values were compared between the two groups and support vector machine (SVM) was used to classify apnea patients. Significant difference was found between EEG scaling exponents of the two groups (p < 0.001). SVM was used and obtained high and consistent recognition rate: average classification accuracy reached 95.1 % corresponding to the sensitivity 93.2 % and specificity 98.6 %. DFA of EEG is an efficient and practicable method and is helpful clinically in diagnosis of sleep apnea.
An Optimization Framework for Remapping and Reweighting Noisy Relevance Labels

Relevance labels is the essential part of any learning to rank framework. The rapid development of crowdsourcing platforms led to a significant reduction of the cost of manual labeling. This makes it possible to collect very large sets of labeled documents to train a ranking algorithm. However, relevance labels acquired via crowdsourcing are typically coarse and noisy, so certain consensus models are used to measure the quality of labels and to reduce the noise. This noise is likely to affect a ranker trained on such labels, and, since none of the existing consensus models directly optimizes ranking quality, one has to apply some heuristics to utilize the output of a consensus model in a ranking algorithm, e.g., to use majority voting among workers to get consensus labels. The major goal of this paper is to unify existing approaches to consensus modeling and noise reduction within a learning to rank framework. Namely, we present a machine learning algorithm aimed at improving the performance of a ranker trained on a crowdsourced dataset by proper remapping of labels and reweighting of samples. In the experimental part, we use several characteristics of workers/labels extracted via various consensus models in order to learn the remapping and reweighting functions. Our experiments on a large-scale dataset demonstrate that we can significantly improve state-of-the-art machine-learning algorithms by incorporating our framework.
Smart Electric Motorcycle Security System Based on GSM and ZigBee Communication

The storage of natural fuel is decreasing day by day and burning much of it can cause a massive environmental disaster. In order to save the nature from the disaster people need to think about some alternatives. The concept of making the battery operated vehicles comes as a rescue. The battery operated electric vehicle help in saving fuel and also help in lowering air pollution. Hybrid vehicles are already brought by several automobile manufacturers in market. This proposed system is going to offer an electric motorcycle combined with some security features based on ARM microcontroller, 8051 microcontroller, ZigBee and GSM communication which can be utilized both in Hybrid motorcycles and Electric motorcycles. When the proposed security features will be merged with those vehicles it will be an evolutionary concept in the world of next generation two-wheeler industry because besides lowering the level of air pollution and helping in controlling the use of fuel it mainly helps people to drive motorcycles safely.
polymer solar cells

Although the present dominant photovoltaic (PV) technology is based on inorganic materials, high material and manufacturing costs limit its wide acceptance1. Intensive research has been conducted towards the development of low-cost PV technologies, of which organic photovoltaic (OPV) devices are one of the promising. OPV devices are based on organic semiconductors — carbon-based materials whose backbones are comprised mainly of alternating C–C and C=C bonds. Electron delocalization along the conjugated backbone is responsible for the semiconducting properties of OPV devices2. One of the major differences between organic semiconductors and inorganic semiconductors is the presence of tightly bonded excitons (electron–hole pairs) resulting from their low dielectric constant (εr ≈ 2–4). The binding energy of the Frenkel exciton is in the range of 0.3–1 eV (refs 2,3). Such a large binding energy prevents exciton dissociation by an electrical field (a non-radiative decay channel) and can achieve a high electroluminescent efficiency in organic light-emitting devices. The weak intermolecular van de Waals interaction enables the realization of low-cost, large-area deposition technologies such as roll-toroll printing3. In recent years, organic electronic devices such as organic light-emitting diodes (OLEDs), organic thin film transistors, OPVs and organic memory devices have attracted considerable attention, owing to their potential low cost and high performance characteristics. OLED displays have gained a considerable share in the portable electronics market, for use in devices such as smart phones. However, research into OPV cells continues to lag behind, despite the first patent4 and the first paper5 by Tang appearing ahead of those of OLEDs, probably owing to the fact that developing alternative energy sources has been viewed, until recently, as being relatively unimportant. OPVs are divided into two different categories according to whether their constituent molecules are either small or large (polymers). These two classes of materials are rather different in terms of their synthesis, purification and device fabrication processes. Polymer solar cells (PSCs) are processed from solution in organic solvents, whereas small-molecule solar cells are processed mainly using thermal evaporation deposition in a high-vacuum environment. Using the solution process to fabricate small-molecule solar cells has recently been gaining momentum6, although the film quality and crystallization is expected to be an issue. PSCs are attractive owing to a number of advantageous features7, including their thin-film architecture and low material consumption resulting from a high absorption coefficient, their use of organic polymer solar cells
Additively Manufactured Scaffolds for Bone Tissue Engineering and the Prediction of their Mechanical Behavior: A Review

Additive manufacturing (AM), nowadays commonly known as 3D printing, is a revolutionary materials processing technology, particularly suitable for the production of low-volume parts with high shape complexities and often with multiple functions. As such, it holds great promise for the fabrication of patient-specific implants. In recent years, remarkable progress has been made in implementing AM in the bio-fabrication field. This paper presents an overview on the state-of-the-art AM technology for bone tissue engineering (BTE) scaffolds, with a particular focus on the AM scaffolds made of metallic biomaterials. It starts with a brief description of architecture design strategies to meet the biological and mechanical property requirements of scaffolds. Then, it summarizes the working principles, advantages and limitations of each of AM methods suitable for creating porous structures and manufacturing scaffolds from powdered materials. It elaborates on the finite-element (FE) analysis applied to predict the mechanical behavior of AM scaffolds, as well as the effect of the architectural design of porous structure on its mechanical properties. The review ends up with the authors' view on the current challenges and further research directions.
Query Aware Determinization of Uncertain Objects

This paper considers the problem of determinizing probabilistic data to enable such data to be stored in legacy systems that accept only deterministic input. Probabilistic data may be generated by automated data analysis/enrichment techniques such as entity resolution, information extraction, and speech processing. The legacy system may correspond to pre-existing web applications such as Flickr, Picasa, etc. The goal is to generate a deterministic representation of probabilistic data that optimizes the quality of the end-application built on deterministic data. We explore such a determinization problem in the context of two different data processing tasks-triggers and selection queries. We show that approaches such as thresholding or top-1 selection traditionally used for determinization lead to suboptimal performance for such applications. Instead, we develop a query-aware strategy and show its advantages over existing solutions through a comprehensive empirical evaluation over real and synthetic datasets.
MoodScope: building a mood sensor from smartphone usage patterns

We present MoodScope, a software system which infers the mood of its user based on how the smartphone is used. Similar to smartphone sensors that measure acceleration, light, and other physical properties, MoodScope is a "sensor" that measures the mental state of the user and provides mood as an important input to context-aware computing. We run a formative statistical study with smartphone-logged data collected from 32 participants over two months. Through the study, we find that by analyzing communication history and application usage patterns, we can statistically infer a user's daily mood average with an accuracy of 93% after a two-month training period. Motivated by these results, we build a service, MoodScope, which analyzes usage history to act as a sensor of the user's mood.
Above the Clouds: A Berkeley View of Cloud Computing

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing.