nils-herrmann/clean_scirepeval huggingface dataset:
Fabrication of origami wheel using pattern embedded fabric and its application to a deformable mobile robot

The unique characteristics of origami to realize 3-D shape from 2-D patterns have been fascinating many researchers and engineers. This paper presents a fabrication of origami patterned fabric wheels that can deform and change the radius of the wheels. PVC segments are enclosed in the fabrics to build a tough and foldable structure. A special cable driven mechanism was designed to allow the wheels to deform while rotating. A mobile robot with two origami wheels has been built and tested to show that it can deform its wheels to overcome various obstacles.
The Enterprise Ontology

This document presents the Enterprise Ontology a collection of terms and de nitions relevant to business enterprises It was developed as part of the Enterprise Project a collaborative e ort to provide a framework for enterprise modelling The Enterprise Ontology will serve as a basis for this framework which includes methods and a computer toolset for enterprise modelling We give an overview of the Enterprise Project elaborate on the intended use of the Ontology and discuss the process we went through to build it The scope of the Enterprise Ontology is limited to those core concepts required for the project however it is expected that it will appeal to a wider audience It should not be considered static during the course of the project the Enterprise Ontology will be further re ned and extended
Drug-Drug Interactions prediction from enzyme action crossing through machine learning approaches

Drug-Drug Interactions (DDIs) are major causes of morbidity and treatment inefficacy. The prediction of DDIs for avoiding the adverse effects is an important issue. There are many drug-drug interaction pairs, it is impossible to do in vitro or in vivo experiments for all the possible pairs. The limitation of DDIs research is the high costs. Many drug interactions are due to alterations in drug metabolism by enzymes. The most common among these enzymes are cytochrome P450 enzymes (CYP450). Drugs can be substrate, inhibitor or inducer of CYP450 which will affect metabolite of other drugs. This paper proposes enzyme action crossing attribute creation for DDIs prediction. Machine learning techniques, k-Nearest Neighbor (k-NN), Neural Networks (NNs), and Support Vector Machine (SVM) were used to find DDIs for simvastatin based on enzyme action crossing. SVM preformed the best providing the predictions at the accuracy of 70.40 % and of 81.85 % with balance and unbalance class label datasets respectively. Enzyme action crossing method provided the new attribute that can be used to predict drug-drug interactions.
Scienstein : A Research Paper Recommender System

This paper introduces Scienstein, the first hybrid research paper recommender system and a powerful alternative to currently used academic search engines. Scienstein improves the approach of the usually used keyword-based search by combining it with citation analysis, author analysis, source analysis, implicit ratings, explicit ratings and in addition, innovative and yet unused methods like the ‘Distance Similarity Index’ (DSI) and the ‘In-text Impact Factor’ (ItIF). Instead of entering just keywords, a user may provide entire documents, including reference lists as input and make implicit and explicit ratings to improve recommendations. With citation, author and source analysis, similar and related documents are easily determinable. All these techniques are managed by a user-friendly GUI.
AcousticBrainz: A Community Platform for Gathering Music Information Obtained from Audio

We introduce the AcousticBrainz project, an open platform for gathering music information. At its core, AcousticBrainz is a database of music descriptors computed from audio recordings using a number of state-of-the-art Music Information Retrieval algorithms. Users run a supplied feature extractor on audio files and upload the analysis results to the AcousticBrainz server. All submissions include a MusicBrainz identifier allowing them to be linked to various sources of editorial information. The feature extractor is based on the open source Essentia audio analysis library. From the data submitted by the community, we run classifiers aimed at adding musically relevant semantic information. These classifiers can be developed by the community using tools available on the AcousticBrainz website. All data in AcousticBrainz is freely available and can be accessed through the website or API. For AcousticBrainz to be successful we need to have an active community that contributes to and uses this platform, and it is this community that will define the actual uses and applications of its data.
Demystifying Numenta anomaly benchmark

Detecting anomalies in large-scale, streaming datasets has wide applicability in a myriad of domains like network intrusion detection for cyber-security, fraud detection for credit cards, system health monitoring, and fault detection in safety critical systems. Due to its wide applicability, the problem of anomaly detection has been well-studied by industry and academia alike, and many algorithms have been proposed for detecting anomalies in different problem settings. But until recently, there was no openly available, systematic dataset and/or framework using which the proposed anomaly detection algorithms could be compared and evaluated on a common ground. Numenta Anomaly Benchmark (NAB), made available by Numenta1 in 2015, addressed this gap by providing a set of openly-available, labeled data files and a common scoring system, using which different anomaly detection algorithms could be fairly evaluated and compared. In this paper, we provide an in-depth analysis of the key aspects of the NAB framework, and highlight inherent challenges therein, with the objective to provide insights about the gaps in the current framework that must be addressed so as to make it more robust and easy-to-use. Furthermore, we also provide additional evaluation of five state-of-the-art anomaly detection algorithms (including the ones proposed by Numenta) using the NAB datasets, and based on the evaluation results, we argue that the performance of these algorithms is not sufficient for practical, industry-scale applications, and must be improved upon so as to make them suitable for large-scale anomaly detection problems.
Smart grid data integrity attacks: characterizations and countermeasuresπ

Coordinated cyberattacks of power meter readings can be arranged to be undetectable by any bad data detection algorithm in the power system state estimation process. These unobservable attacks present a potentially serious threat to grid operations. Of particular interest are sparse attacks that involve the compromise of a modest number of meter readings. An efficient algorithm to find all unobservable attacks [under standard DC load flow approximations] involving the compromise of exactly two power injection meters and an arbitrary number of line power meters is presented. This requires O(n2m) flops for a power system with n buses and m line meters. If all lines are metered, there exist canonical forms that characterize all 3, 4, and 5-sparse unobservable attacks. These can be quickly detected in power systems using standard graph algorithms. Known-secure phasor measurement units [PMUs] can be used as countermeasures against an arbitrary collection of cyberattacks. Finding the minimum number of necessary PMUs is NP-hard. It is shown that p + 1 PMUs at carefully chosen buses are sufficient to neutralize a collection of p cyberattacks.
Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection

Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly. In this paper, we introduce a novel approach to sequence tagging that learns to correct the errors from cross-lingual projection using an explicit debiasing layer. This is framed as joint learning over two corpora, one tagged with gold standard and the other with projected tags. We evaluated with only 1,000 tokens tagged with gold standard tags, along with more plentiful parallel data. Our system equals or exceeds the state-of-the-art on eight simulated lowresource settings, as well as two real lowresource languages, Malagasy and Kinyarwanda.
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

Deep neural networks (DNNs) provide excellent performance across a wide range of classification tasks, but their training requires high computational resources and is often outsourced to third parties. Recent work has shown that outsourced training introduces the risk that a malicious trainer will return a backdoored DNN that behaves normally on most inputs but causes targeted misclassifications or degrades the accuracy of the network when a trigger known only to the attacker is present. In this paper, we provide the first effective defenses against backdoor attacks on DNNs. We implement three backdoor attacks from prior work and use them to investigate two promising defenses, pruning and fine-tuning. We show that neither, by itself, is sufficient to defend against sophisticated attackers. We then evaluate fine-pruning, a combination of pruning and fine-tuning, and show that it successfully weakens or even eliminates the backdoors, i.e., in some cases reducing the attack success rate to 0% with only a 0.4% drop in accuracy for clean (non-triggering) inputs. Our work provides the first step toward defenses against backdoor attacks in deep neural networks.
A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts

Owing to demand characteristics of spare part, demand forecasting for spare parts is especially difficult. Based on the properties of spare part demand, we develop a hybrid forecasting approach, which can synthetically evaluate autocorrelation of demand time series and the relationship of explanatory variables with demand of spare part. In the described approach, support vector machines (SVMs) are adapted to forecast occurrences of nonzero demand of spare part, and a hybrid mechanism for integrating the SVM forecast results and the relationship of occurrence of nonzero demand with explanatory variables is proposed. Using real data sets of 30 kinds of spare parts from a petrochemical enterprise in China, we show that our method produces more accurate forecasts of distribution of lead-time demands of spare parts than do current methods across almost all the lead times.
Early-childhood poverty and adult attainment, behavior, and health.

This article assesses the consequences of poverty between a child's prenatal year and 5th birthday for several adult achievement, health, and behavior outcomes, measured as late as age 37. Using data from the Panel Study of Income Dynamics (1,589) and controlling for economic conditions in middle childhood and adolescence, as well as demographic conditions at the time of the birth, findings indicate statistically significant and, in some cases, quantitatively large detrimental effects of early poverty on a number of attainment-related outcomes (adult earnings and work hours). Early-childhood poverty was not associated with such behavioral measures as out-of-wedlock childbearing and arrests. Most of the adult earnings effects appear to operate through early poverty's association with adult work hours.
Business intelligence systems: design and implementation strategies

Managing an organization requires access to information in order to monitor activities and assess performance. Trying to understand what information an organization has can be challenging because the information systems collect and process vast amount of data in various forms. To flow in the running stream of rapidly changing, increasingly competitive global market scenario and increasingly volatile consumer and market behavior and rapidly shortening product life cycles, business enterprises today are necessary to analyze accurate and timely information about financial operations, customers, and products using familiar business terms, in order to gain analytical insight into business problems and opportunities. Enterprises are building business intelligence systems that support business analysis and decision making to help them better understand their operations and compete in the marketplace. This paper describes the life cycle comprising various phases in the development of a BI system. The paper elaborates the implementation issues of BI in an organization focusing a case study
ExTaSem! Extending, Taxonomizing and Semantifying Domain Terminologies

We introduce EXTASEM!, a novel approach for the automatic learning of lexical taxonomies from domain terminologies. First, we exploit a very large semantic network to collect thousands of in-domain textual definitions. Second, we extract (hyponym, hypernym) pairs from each definition with a CRF-based algorithm trained on manually-validated data. Finally, we introduce a graph induction procedure which constructs a full-fledged taxonomy where each edge is weighted according to its domain pertinence. EXTASEM! achieves state-of-the-art results in the following taxonomy evaluation experiments: (1) Hypernym discovery, (2) Reconstructing gold standard taxonomies, and (3) Taxonomy quality according to structural measures. We release weighted taxonomies for six domains for the use and scrutiny of the community.
Reliability Qualification of CoSi2 Electrical Fuse for 90Nm Technology

The reliability of CoSi2/p-poly Si electrical fuse (eFUSE) programmed by electromigration for 90nm technology will be presented. Both programmed and unprogrammed fuse elements were shown to be stable through extensive reliability evaluations. A qualification methodology is demonstrated to define an optimized reliable electrical fuse programming window by combining fuse resistance measurements, physical analysis, and functional sensing data. This methodology addresses the impact on electrical fuse reliability caused by process variation and device degradation (e.g., NBTI) in the sensing circuit and allows an adequate margin to ensure electrical fuse reliability over the chip lifetime
Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review

A sentiment analysis has received a lot of attention from researchers working in the fields of natural language processing and text mining. However, there is a lack of annotated data sets that can be used to train a model for all domains, which is hampering the accuracy of sentiment analysis. Many research studies have attempted to tackle this issue and to improve cross-domain sentiment classification. In this paper, we present the results of a comprehensive systematic literature review of the methods and techniques employed in a cross-domain sentiment analysis. We focus on studies published during the period of 2010–2016. From our analysis of those works, it is clear that there is no perfect solution. Hence, one of the aims of this review is to create a resource in the form of an overview of the techniques, methods, and approaches that have been used to attempt to solve the problem of cross-domain sentiment analysis in order to assist researchers in developing new and more accurate techniques in the future.
Evaluating topic models for digital libraries

Topic models could have a huge impact on improving the ways users find and discover content in digital libraries and search interfaces through their ability to automatically learn and apply subject tags to each and every item in a collection, and their ability to dynamically create virtual collections on the fly. However, much remains to be done to tap this potential, and empirically evaluate the true value of a given topic model to humans. In this work, we sketch out some sub-tasks that we suggest pave the way towards this goal, and present methods for assessing the coherence and interpretability of topics learned by topic models. Our large-scale user study includes over 70 human subjects evaluating and scoring almost 500 topics learned from collections from a wide range of genres and domains. We show how scoring model -- based on pointwise mutual information of word-pair using Wikipedia, Google and MEDLINE as external data sources - performs well at predicting human scores. This automated scoring of topics is an important first step to integrating topic modeling into digital libraries
High-Voltage, High-Frequency Pulse Generator for Nonequilibrium Plasma Generation and Combustion Enhancement

This paper outlines the design and implementation of a solid-state high-voltage, high-frequency pulse generator used to drive capacitive loads and particularly, nonequilibrium plasmas at atmospheric pressure. The generator is capable of producing open circuit pulses of 0-12 kV with a 300 ns duration (full-width at half-maximum) at a repetition frequency of 0-25 kHz. The working principles of the generator are presented, along with its electrical diagnostics to illustrate its operation with capacitive loads. The generator was applied to a pin-to-plane electrode configuration to generate a diffuse nonequilibrium plasma discharge with peak voltage of 12 kV. Energy deposition and average power required to drive the discharges in open air at 25 kHz were calculated to be 112 μJ/pulse and 2.80 W. The generator was also applied to lean, stagnation-plate stabilized V-shaped flames to increase their blowoff velocity. A 28%-51% increase of the blowoff velocity is observed using a discharge with the peak voltage of 6.2 kV, and the repetition rate of 25 kHz.
DECAF: A Platform-Neutral Whole-System Dynamic Binary Analysis Platform

Dynamic binary analysis is a prevalent and indispensable technique in program analysis. While several dynamic binary analysis tools and frameworks have been proposed, all suffer from one or more of: prohibitive performance degradation, a semantic gap between the analysis code and the program being analyzed, architecture/OS specificity, being user-mode only, and lacking APIs. We present DECAF, a virtual machine based, multi-target, whole-system dynamic binary analysis framework built on top of QEMU. DECAF provides Just-In-Time Virtual Machine Introspection and a plugin architecture with a simple-to-use event-driven programming interface. DECAF implements a new instruction-level taint tracking engine at bit granularity, which exercises fine control over the QEMU Tiny Code Generator (TCG) intermediate representation to accomplish on-the-fly optimizations while ensuring that the taint propagation is sound and highly precise. We perform a formal analysis of DECAF's taint propagation rules to verify that most instructions introduce neither false positives nor false negatives. We also present three platform-neutral plugins—Instruction Tracer, Keylogger Detector, and API Tracer, to demonstrate the ease of use and effectiveness of DECAF in writing cross-platform and system-wide analysis tools. Implementation of DECAF consists of 9,550 lines of C++ code and 10,270 lines of C code and we evaluate DECAF using CPU2006 SPEC benchmarks and show average overhead of 605 percent for system wide tainting and 12 percent for VMI.
Routing in telecommunications networks with " smart " ant-like agents

A simple mechanism is presented, based on ant-like agents, for routing and load balancing in telecommunications networks, following the initial works of Appleby and Stewart (1994) and Schoonderwoerd et al. (1997). In the present work, agents are very similar to those proposed by Schoonderwoerd et al. (1997), but are supplemented with a simplified dynamic programming capability, initially experimented by Guérin (1997) with more complex agents, which is shown to significantly improve the network's relaxation and its response to perturbations.  Intelligent agents and network management
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation

Estimating count and density maps from crowd images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning. In addition, techniques developed for crowd counting can be applied to related tasks in other fields of study such as cell microscopy, vehicle counting and environmental survey. The task of crowd counting and density map estimation is riddled with many challenges such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Nevertheless, over the last few years, crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios. The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets. In this paper, we provide a comprehensive survey of recent Convolutional Neural Network (CNN) based approaches that have demonstrated significant improvements over earlier methods that rely largely on hand-crafted representations. First, we briefly review the pioneering methods that use hand-crafted representations and then we delve in detail into the deep learning-based approaches and recently published datasets. Furthermore, we discuss the merits and drawbacks of existing CNN-based approaches and identify promising avenues of research in this rapidly evolving field.
Ignoring Irrelevant Facts and Operators in Plan Generation

It is traditional wisdom that one should start from the goals when generating a plan in order to focus the plan generation process on potentially relevant actions. The graphplan system, however, which is the most eecient planning system nowadays, builds a \planning graph" in a forward-chaining manner. Although this strategy seems to work well, it may possibly lead to problems if the planning task description contains irrelevant information. Although some irrelevant information can be ltered out by graphplan, most cases of irrelevance are not noticed. In this paper, we analyze the eeects arising from \irrelevant" information to planning task descriptions for diierent types of planners. Based on that, we propose a family of heuristics that select relevant information by minimizing the number of initial facts that are used when approximating a plan by backchaining from the goals ignoring any connicts. These heuristics, although not solution-preserving, turn out to be very useful for guiding the planning process, as shown by applying the heuristics to a large number of examples from the literature.
A Compact Koch Fractal UWB MIMO Antenna With WLAN Band-Rejection

In this letter, a compact octagonal shaped fractal ultrawideband multiple-input-multiple-output antenna is presented, and its characteristics are investigated. In order to achieve the desired miniaturization and wideband phenomena, self-similar and space filling properties of Koch fractal geometry are used in the antenna design. These fractal monopoles are placed orthogonal to each other for good isolation. Moreover, grounded stubs are used in the geometry to provide further improvement in the isolation. The band rejection phenomenon in wireless local area network band is achieved by etching a C-shaped slot from the monopole of the antenna. The proposed antenna has compact dimensions of 45 mm × 45 mm and exhibits quasi-omnidirectional radiation pattern. In addition, it shows an impedance bandwidth (S11 <; -10 dB ) from 2 to 10.6 GHz with isolation better than 17 dB over the entire ultra-wideband range. Diversity performance is also evaluated in terms of envelope correlation coefficient and capacity loss. The measured results show good agreement with the simulated ones.