nils-herrmann/clean_scirepeval huggingface dataset:
Semantic MEDLINE: An advanced information management application for biomedicine

Semantic MEDLINE integrates information retrieval, advanced natural language processing, automatic summarization, and visualization into a single Web portal. The application is intended to help manage the results of PubMed searches by condensing core semantic content in the citations retrieved. Output is presented as a connected graph of semantic relations, with links to the original MEDLINE citations. The ability to connect salient information across documents helps users keep up with the research literature and discover connections which might otherwise go unnoticed. Semantic MEDLINE can make an impact on biomedicine by supporting scientific discovery and the timely translation of insights from basic research into advances in clinical practice and patient care. Marcelo Fiszman has an M.D. from the State University of Rio de Janeiro and a Ph.D. in biomedical informatics from the University of Utah. He was awarded a postdoctoral fellowship in biomedical informatics at the National Library of Medicine (NLM) and is currently a research scientist there. His work focuses on natural language processing algorithms that exploit symbolic, rule-based techniques for semantic interpretation of biomedical text. He is also interested in using extracted semantic information for automatic abstraction summarization and literaturebased discovery. These efforts underpin Semantic MEDLINE, which is currently under development at NLM. This innovative biomedical information management application combines document retrieval, semantic interpretation, automatic summarization, and knowledge visualization into a single application.
One-DOF Cylindrical Deployable Structures with Rigid Quadrilateral Panels

In this paper,we present a novel cylindrical deployable structure and variations of its design with the following characteristics: 1. Flat-foldable: The shape flattens into a compact 2D configuration. 2. Rigid-foldable: Each element does not deform throughout the transformation. 3. One-DOF: The mechanism has exactly one degree of freedom. 4.  Facets can be substituted with thick or multilayered panels without introducing the distortion of elements.
Interactive ontology debugging: Two query strategies for efficient fault localization☆

Effective debugging of ontologies is an important prerequisite for their broad application, especially in areas that rely on everyday users to create and maintain knowledge bases, such as the Semantic Web. In such systems ontologies capture formalized vocabularies of terms shared by its users. However in many cases users have different local views of the domain, i.e. of the context in which a given term is used. Inappropriate usage of terms together with natural complications when formulating and understanding logical descriptions may result in faulty ontologies. Recent ontology debugging approaches use diagnosis methods to identify causes of the faults. In most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. To identify the best query we propose two query selection strategies: a simple "split-in-half" strategy and an entropy-based strategy. The latter allows knowledge about typical user errors to be exploited to minimize the number of queries. Our evaluation showed that the entropy-based method significantly reduces the number of required queries compared to the "split-in-half" approach. We experimented with different probability distributions of user errors and different qualities of the a priori probabilities. Our measurements demonstrated the superiority of entropy-based query selection even in cases where all fault probabilities are equal, i.e. where no information about typical user errors is available.
Active Clamping Circuit With Status Feedback for Series-Connected HV-IGBTs

Active clamping circuits can effectively suppress the voltage imbalance between series-connected insulated-gate bipolar transistors (IGBTs). However, while the active clamping circuit is conducting, extra current is injected into the gate of the IGBT, the gate voltage increases, and the IGBT operates in the active region. Therefore, long time conduction and frequent actions of the active clamping circuit will lead to extremely high switching loss of the IGBT with high collector-emitter voltage and serious switching loss imbalance between the series-connected IGBTs, which imperils the efficiency and safety of the system, particularly in the applications of high-voltage IGBT (HV-IGBT) series connection. In this paper, a novel active clamping circuit with status feedback is proposed for series-connected HV-IGBTs. A high-speed status feedback subcircuit is designed for transforming the operation status of an active clamping circuit into optical signal and feeding it back to the control system. According to the feedback signals, the microcontroller regulates the switching-on time and switching-off time of the series-connected HV-IGBTs' drive signals timely in order to implement voltage balancing and, thus, to restrain the actions of active clamping circuits. By applying this voltage balancing method to the HV-IGBT series connection circuit, the voltage balancing performance is expected to improve, and high switching loss and serious switching loss imbalance are expected to be suppressed. Experimental results verify the validity and superiorities of the proposed voltage balancing circuit and control strategy.
Autonomous shaping: knowledge transfer in reinforcement learning

We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that estimates intermediate rewards, resulting in accelerated learning in later tasks that are related but distinct. Such agents can be trained on a sequence of relatively easy tasks in order to develop a more informative measure of reward that can be transferred to improve performance on more difficult tasks without requiring a hand coded shaping function. We use a rod positioning task to show that this significantly improves performance even after a very brief training period.
User Preference Modeling and Exploitation in IoT Scenarios

Recommender Systems are commonly used in web applications to support users in finding items of their interest. We here propose to use Recommender Systems in Internet of Things scenarios to support human decision making in the physical world. For instance, users' choices (visit actions) for points of interests (POIs) while they explore a sensor enabled city can be tracked and used to generate recommendations for not yet visited POIs. In this PhD research, we propose a novel learning approach for generating an explainable human behaviour model and relevant recommendations in sensor enabled areas. Moreover, we propose techniques for simulating user behaviour and analyse the collective dynamics of a population of users.
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods
The Semantic Web Revisited

The article included many scenarios in which intelligent agents and bots undertook tasks on behalf of their human or corporate owners. Of course, shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks: they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years
SoK: SSL and HTTPS: Revisiting Past Challenges and Evaluating Certificate Trust Model Enhancements

Internet users today depend daily on HTTPS for secure communication with sites they intend to visit. Over the years, many attacks on HTTPS and the certificate trust model it uses have been hypothesized, executed, and/or evolved. Meanwhile the number of browser-trusted (and thus, de facto, user-trusted) certificate authorities has proliferated, while the due diligence in baseline certificate issuance has declined. We survey and categorize prominent security issues with HTTPS and provide a systematic treatment of the history and on-going challenges, intending to provide context for future directions. We also provide a comparative evaluation of current proposals for enhancing the certificate infrastructure used in practice.
Natural Language Interface to Database-An Introduction

Information plays an important role in our lives .it is required numerous applications. One of the major sources of information is databases. Databases and database technology are growing rapidly and are having major impact on the growing use of computers. Computers are used in accessing of information from database. Almost all IT applications are storing, retrieving organizing, accessing analyzing, and information from databases. using the internet. Retrieving information from database requires knowledge of database languages like Structured Query Language (SQL) However, not everybody is able to write SQL queries as they may not be aware of the syntax of SQL structure of the database,. This has lead to the developing such a system where non-expert users compose their questions in their natural language and obtain the results in the form of database. So instead of working with the SQL one can query relational databases in their natural language. This new idea is provoked to develop new type of processing system called natural language interface to database Natural language interface to database enhances the users performance and is a flexible approach querying in database. This paper is an introduction to natural language interfaces to databases (NLIDBS). Covers a brief overview of the NLIDB its components its advantages disadvantages, approaches and techniques .
Do Multi-Sense Embeddings Improve Natural Language Understanding?

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations. Yet while ‘multi-sense’ methods have been proposed and tested on artificial wordsimilarity tasks, we don’t know if they improve real natural language understanding tasks. In this paper we introduce a multisense embedding model based on Chinese Restaurant Processes that achieves state of the art performance on matching human word similarity judgments, and propose a pipelined architecture for incorporating multi-sense embeddings into language un-
Intraday periodicity and volatility persistence in financial markets

The pervasive intraday periodicity in the return volatility in foreign exchange and equity markets is shown to have a strong impact on the dynamic properties of high frequency returns. Only by taking account of this strong intraday periodicity is it possible to uncover the complex intraday volatility dynamics that exists both within and across different financial markets. The explicit periodic modeling procedure developed here provides such a framework and thus sets the stage for a formal integration of standard volatility models with market microstructure variables to allow for a more comprehensive empirical investigation of the fundamental determinants behind the volatility clustering phenomenon.
Interference cancellation for cellular systems: a contemporary overview

Cellular networks today are interference-limited and only becomes increasingly so in the future due to the many users that need to share the spectrum to achieve high-rate multimedia communication. Despite the enormous amount of academic and industrial research in the past 20 years on interference-aware receivers and the large performance improvements promised by these multi-user techniques, today's receivers still generally treat interference as background noise. In this article, we enumerate the reasons for this widespread scepticism, and discuss how current and future trends increases the need for and viability of multi-user receivers for both the uplink, where many asynchronous users are simultaneously detected, and the downlink, where users are scheduled and largely orthogonalized; but the mobile handset still needs to cope with a few dominant interfering base stations. New results for interference cancelling receivers that use conventional front-ends are shown to alleviate many of the shortcomings of prior techniques, particularly for the challenging uplink. This article gives an overview of key recent research breakthroughs on interference cancellation and highlights system-level considerations for future multi-user receivers.
Casting doubts on the viability of WiFi offloading

With the advent of the smartphone, mobile data usage has exploded which in turn has created tremendous pressure on cellular data networks. A promising candidate to reduce the impact of cellular data growth is WiFi offloading. However, recent data from our study of two hundred student smartphone users casts doubts on the reductions that can be gained from WiFi offloading. Despite the users operating in a dense university WiFi environment, cellular consumption still dominated overall data usage. We believe the root cause of lesser WiFi utilization can be traced to the WiFi being optimized for laptop WiFi reception rather than the more constrained smartphone WiFi reception. Our work examines the relationship of WiFi versus 3G usage through a variety of aspects including active phone usage, application types, and traffic volume over an eight week period from the Spring of 2012.
Compressor trees for decimal partial product reduction

Decimal multiplication has grown in interest due to the recent announcement of new IEEE 754R standards and the availability of high-speed decimal computation hardware. Prior research enabled partial products to be coded more efficiently for their use in radix 10 architectures. This paper clarifies previous techniques for partial product reduction using carry-save adders and presents a new 4:2 compressor structure. This new structure improves performance at the expense of more gates, however, regularity is introduced into the circuit to promote implementations in Very Large Scale Integration (VLSI) Designs. Results are presented and compared for several designs using a TSMC SCN6M $0.18 mu m feature size.
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds. We use a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image. Such a combination is used for effectively capturing both the high-level semantic information (face/body detectors) and the low-level features (blob detectors), that are necessary for crowd counting under large scale variations. As most crowd datasets have limited training samples (<100 images) and deep learning based approaches require large amounts of training data, we perform multi-scale data augmentation. Augmenting the training samples in such a manner helps in guiding the CNN to learn scale invariant representations. Our method is tested on the challenging UCF_CC_50 dataset, and shown to outperform the state of the art methods.
Model-based and learned semantic object labeling in 3D point cloud maps of kitchen environments

We report on our experiences regarding the acquisition of hybrid Semantic 3D Object Maps for indoor household environments, in particular kitchens, out of sensed 3D point cloud data. Our proposed approach includes a processing pipeline, including geometric mapping and learning, for processing large input datasets and for extracting relevant objects useful for a personal robotic assistant to perform complex manipulation tasks. The type of objects modeled are objects which perform utilitarian functions in the environment such as kitchen appliances, cupboards, tables, and drawers. The resulted model is accurate enough to use it in physics-based simulations, where doors of 3D containers can be opened based on their hinge position. The resulted map is represented as a hybrid concept and is comprised of both the hierarchically classified objects and triangular meshes used for collision avoidance in manipulation routines.
A comparison of approaches to large-scale data analysis

There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale data analysis . Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper, we describe and compare both paradigms. Furthermore, we evaluate both kinds of systems in terms of performance and development complexity. To this end, we define a benchmark consisting of a collection of tasks that we have run on an open source version of MR as well as on two parallel DBMSs. For each task, we measure each system's performance for various degrees of parallelism on a cluster of 100 nodes. Our results reveal some interesting trade-offs. Although the process to load data into and tune the execution of parallel DBMSs took much longer than the MR system, the observed performance of these DBMSs was strikingly better. We speculate about the causes of the dramatic performance difference and consider implementation concepts that future systems should take from both kinds of architectures.
DepSky: Dependable and Secure Storage in a Cloud-of-Clouds

The increasing popularity of cloud storage services has lead companies that handle critical data to think about using these services for their storage needs. Medical record databases, large biomedical datasets, historical information about power systems and financial data are some examples of critical data that could be moved to the cloud. However, the reliability and security of data stored in the cloud still remain major concerns. In this work we present DepSky, a system that improves the availability, integrity, and confidentiality of information stored in the cloud through the encryption, encoding, and replication of the data on diverse clouds that form a cloud-of-clouds. We deployed our system using four commercial clouds and used PlanetLab to run clients accessing the service from different countries. We observed that our protocols improved the perceived availability, and in most cases, the access latency, when compared with cloud providers individually. Moreover, the monetary costs of using DepSky in this scenario is at most twice the cost of using a single cloud, which is optimal and seems to be a reasonable cost, given the benefits.
CloudControl: Leveraging many public ChIP-seq control experiments to better remove background noise

Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) is a widely used method to determine the binding positions of various proteins on the genome in a population of cells. A typical ChIP-seq protocol involves two experiments: one designed to capture target ChIP-seq signals ('target' experiment) and the other to capture background noise signals ('control' experiment). A peak calling algorithm then examines the difference between the target experiment data and control data to determine where the protein of interest binds along the genome. Our approach, named CloudControl, aims to improve the accuracy of peak calling by combining multiple control experiments from a publicly available source such as ENCODE to better remove background noise signals.
 To combine existing control experiment data we perform regression against a target experiment treating binned genome positions as samples (up to 32 million) and different control experiments as features (up to 450 through the ENCODE project). The regression fit is then used to generate new control ChIP-seq data, which we refer to as CloudControl data. We use the following three metrics to evaluate the CloudControl data: (i) the presence of the known motifs for the corresponding target protein near called peaks, (ii) reproducibility among pairs of biologically replicated ChIP-seq experiments, and (iii) protein-protein physical interactions inferred from called peaks. In all three metrics, CloudControl data shows superior performance over standard control tracks. This suggests that CloudControl can improve ordinary control tracks in the standard ChIP-seq protocol.
ON SUPERVISED AND SEMI-SUPERVISED k-NEAREST NEIGHBOR ALGORITHMS

The k-nearest neighbor (kNN) is one of the simplest classification methods used in machine learning. Since the main component of kNN is a distance metric, kernelization of kNN is possible. In this paper kNN and semi-supervised kNN algorithms are empirically compared on two data sets (the USPS data set and a subset of the Reuters-21578 text categorization corpus). We use a soft version of the kNN algorithm to handle multi-label classification settings. Semi-supervision is performed by using data-dependent kernels.
Two Bitcoins at the Price of One? Double-Spending Attacks on Fast Payments in Bitcoin

Bitcoin is a decentralized payment system that is based on Proof-of-Work. Bitcoin is currently gaining popularity as a digital currency; several businesses are starting to accept Bitcoin transactions. An example case of the growing use of Bitcoin was recently reported in the media; here, Bitcoins were used as a form of fast payment in a local fast-food restaurant. In this paper, we analyze the security of using Bitcoin for fast payments, where the time between the exchange of currency and goods is short (i.e., in the order of few seconds). We focus on doublespending attacks on fast payments and demonstrate that these attacks can be mounted at low cost on currently deployed versions of Bitcoin. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast transactions are not always effective in resisting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we leverage on our findings and propose a lightweight countermeasure that enables the detection of doublespending attacks in fast transactions.