nils-herrmann/clean_scirepeval huggingface dataset:
Simurg: An Extendable Multilingual Corpus for Abstractive Single Document Summarization

Abstractive single document summarization is considered as a challenging problem in the field of artificial intelligence and natural language processing. Meanwhile and specifically in the last two years, several deep learning summarization approaches were proposed that once again attracted the attention of researchers to this field.
 It is a well-known issue that deep learning approaches do not work well with small amounts of data. With some exceptions, this is, unfortunately, the case for most of the datasets available for the summarization task. Besides this problem, it should be considered that phonetic, morphological, semantic and syntactic features of the language are constantly changing over the time and unfortunately most of the summarization corpora are constructed from old resources. Another problem is the language of the corpora. Not only in the summarization field, but also in other fields of natural language processing, most of the corpora are only available in English. In addition to the above problems, license terms, and fees of the corpora are obstacles that prevent many academics and specifically non-academics from accessing these data.
 This work describes an open source framework to create an extendable multilingual corpus for abstractive single document summarization that addresses the above-mentioned problems. We describe a tool consisted of a scalable crawler and a centralized key-value store database to construct a corpus of an arbitrary size using a news aggregator service.
On the Character of Phishing URLs: Accurate and Robust Statistical Learning Classifiers

Phishing attacks resulted in an estimated $3.2 billion dollars worth of stolen property in 2007, and the success rate for phishing attacks is increasing each year [17]. Phishing attacks are becoming harder to detect and more elusive by using short time windows to launch attacks. In order to combat the increasing effectiveness of phishing attacks, we propose that combining statistical analysis of website URLs with machine learning techniques will give a more accurate classification of phishing URLs. Using a two-sample Kolmogorov-Smirnov test along with other features we were able to accurately classify 99.3% of our dataset, with a false positive rate of less than 0.4%. Thus, accuracy of phishing URL classification can be greatly increased through the use of these statistical measures.
An enhanced steering algorithm for redirected walking in virtual environments

Redirected walking techniques enable natural locomotion through immersive virtual environments that are considerably larger than the available real world walking space. However, the most effective strategy for steering the user remains an open question, as most previously presented algorithms simply redirect toward the center of the physical space. In this work, we present a theoretical framework that plans a walking path through a virtual environment and calculates the parameters for combining translation, rotation, and curvature gains such that the user can traverse a series of defined waypoints efficiently based on a utility function. This function minimizes the number of overt reorientations to avoid introducing potential breaks in presence. A notable advantage of this approach is that it leverages knowledge of the layout of both the physical and virtual environments to enhance the steering strategy.
Core Ontologies for Safe Autonomous Driving

Representing the knowledge of driving environments in a structured machine-readable format is necessary for safe autonomous driving. We use ontologies to represent the knowledge of maps, driving paths, and driving environments to improve safety for smart vehicles. In this paper, we introduce core ontologies that are used for developing Advanced Driver Assistance Systems. The ontologies can be reused and extended for constructing Knowledge Base for smart vehicles as well as for implementing different types of Advanced Driver Assistance Systems.
Oral-motor function and feeding intervention.

This article presents the elements of the Oral Motor Intervention section of the Infant Care Path for Physical Therapy in the Neonatal Intensive Care Unit (NICU). The types of physical therapy interventions presented in this path are evidence based as well as infant driven and family focused. In the context of anticipated maturation of suck-swallow-breathe coordination, the timing and methods for initiation of oral feedings and transition from gavage to full breast or bottle-feedings are presented with supporting evidence.
3D Printed Reversible Shape Changing Components with Stimuli Responsive Materials.

The creation of reversibly-actuating components that alter their shapes in a controllable manner in response to environmental stimuli is a grand challenge in active materials, structures, and robotics. Here we demonstrate a new reversible shape-changing component design concept enabled by 3D printing two stimuli responsive polymers-shape memory polymers and hydrogels-in prescribed 3D architectures. This approach uses the swelling of a hydrogel as the driving force for the shape change, and the temperature-dependent modulus of a shape memory polymer to regulate the time of such shape change. Controlling the temperature and aqueous environment allows switching between two stable configurations - the structures are relatively stiff and can carry load in each - without any mechanical loading and unloading. Specific shape changing scenarios, e.g., based on bending, or twisting in prescribed directions, are enabled via the controlled interplay between the active materials and the 3D printed architectures. The physical phenomena are complex and nonintuitive, and so to help understand the interplay of geometric, material, and environmental stimuli parameters we develop 3D nonlinear finite element models. Finally, we create several 2D and 3D shape changing components that demonstrate the role of key parameters and illustrate the broad application potential of the proposed approach.
A SiGe MMIC 6-bit PIN diode phase shifter

A 6-bit PIN diode phase shifter has been successfully demonstrated at microwave frequencies in a SiGe bipolar technology. A post-silicon polymer dielectric interconnect technology is implemented to achieve low loss microstrip structures on the silicon substrate. The monolithic microwave integrated circuit exhibits flat phase shift, low VSWR, and low insertion loss variation, over the 7- to 11-GHz band. This phase shifter demonstrates the feasibility of integrating SiGe technology into microwave systems.
A Critical Review on Outlier Detection Techniques

Outlier Detection is a Data Mining Application. Outlier contains noisy data which is researched in various domains. The various techniques are already being researched that is more generic. We surveyed on various techniques and applications of outlier detection that provides a novel approach that is more useful for the beginners. The proposed approach helps to clean data at university level in less time with great accuracy. This survey includes the existing outlier techniques and applications where the noisy data exists. Our paper defines critical review on various techniques used in different applications of outlier detection that are to be researched further and they gives a particular type of knowledge based data i.e. more useful in research activities. So where the Anomalies is present it will be detected through outlier detection techniques and monitored accordingly especially in educational Data Mining.
An Artificial Neural Network based Intrusion Detection System and Classification of Attacks

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

Household manipulation presents a challenge to robots because it requires perceiving a variety of objects, planning multi-step motions, and recovering from failure. This paper presents practical techniques that improve performance in these areas by considering the complete system in the context of this specific domain. We validate these techniques on a table-clearing task that involves loading objects into a tray and transporting it. The results show that these techniques improve success rate and task completion time by incorporating expected realworld performance into the system design.
Maturational Constraints on Language Learning

This paper suggests that there are constraints on learning required to explain the acquisition of language, in particular, mafuratlonal constraints. First, empirical evidence for this claim is reviewed. The evidence from several studies of both first and second language acquisition suggests that normal language learning occurs only when exposure to the language begins early in life. With exposure beginning later in life, asymptotic performance in the language declines: the effects over age of first exposure are approximately linear through childhood, with a flattening of the function in adulthood. These outcomes argue that some type of constraints ensuring successful language learning exist early in life, and weaken with increosing maturation. Second, two hypotheses are considered as to the nature of these maturational changes. One hypothesis is that constraints on learning particular to language acquisition undergo maturational decay. A second hypothesis, which is considered in more detail, suggests that language learning abilities decline because of the expansion of nonlinguistic cognitive abilities.
The Palliative Prognostic Index: a scoring system for survival prediction of terminally ill cancer patients

Although accurate prediction of survival is essential for palliative care, few clinical methods of determining how long a patient is likely to live have been established. To develop a validated scoring system for survival prediction, a retrospective cohort study was performed with a training–testing procedure on two independent series of terminally ill cancer patients. Performance status (PS) and clinical symptoms were assessed prospectively. In the training set (355 assessments on 150 patients) the Palliative Prognostic Index (PPI) was defined by PS, oral intake, edema, dyspnea at rest, and delirium. In the testing sample (233 assessments on 95 patients) the predictive values of this scoring system were examined. In the testing set, patients were classified into three groups: group A (PPI≤2.0), group B (2.0<PPI≤4.0), and group C (PPI>4.0). Group B survived significantly longer than group C, and group A survived significantly longer than either of the others. Also, when a PPI of more than 6 was adopted as a cut-off point, 3 weeks' survival was predicted with a sensitivity of 80% and a specificity of 85%. When a PPI of more than 4 was used as a cut-off point, 6 weeks' survival was predicted with a sensitivity of 80% and a specificity of 77%. In conclusion, whether patients live longer than 3 or 6 weeks can be acceptably predicted by PPI.
An ultracapacitor integrated power conditioner for intermittency smoothing and improving power quality of distribution grid

Penetration of various types of distributed energy resources (DERs) like solar, wind and PHEVs onto the distribution grid is on the rise. There is a corresponding increase in power quality problems and intermittencies on the distribution grid. In order to reduce the intermittencies and improve the power quality of the distribution grid an Ultracapacitor (UCAP) integrated power conditioner is proposed in this paper. UCAP integration gives the power conditioner active power capability which is useful in tackling the grid intermittencies and in improving the voltage sag and swell compensation. UCAPs have low energy density, high power density and fast charge/discharge rates which are all ideal characteristics for meeting high power low energy events like grid intermittencies, sags/swells. In this paper UCAP is integrated into dc-link of the power conditioner through a bi-directional dc-dc converter which helps in providing a stiff dc-link voltage. The integration helps in providing active/reactive power support, intermittency smoothing and sag/swell compensation. Design and control of both the dc-ac inverters and the dc-dc converter are discussed. The simulation model of the overall system is developed and compared to the experimental hardware setup.
Text Mining of Tweet for Sentiment Classification and Association with Stock Prices

In present days, the social media and networking act as one of the key platforms for sharing information and opinions. Many people share ideas, express their view points and opinions on various topic of their interest. Social media text has rich information about the companies, their products and various services offered by them. In this research we focus exploring the association of sentiments of social media text and stock prices of a company. The tweets of several company has been extracted and performed sentiment classification using Naïve Bayes classifier and SVM classifier. To perform the classification, N-gram based feature vectors are constructed using important words of tweets. Further, the pattern of association between number of tweets which are positive or negative and stock prices has been explored. Motivated by such an association, the features related to tweets such as number of positive, negative, neutral tweets and total number of tweets are used to predict the stock market status using Support Vector Machine classifier.
Predictive maintenance techniques

This article discusses the importance of PdM for industrial process applications and investigates a number of emerging technologies that enable this approach, including online energy-efficiency evaluation and continuous condition monitoring. The article gives an overview of existing and future technologies that can be used in these areas. Two methods for bearing fault detection and energy-efficiency estimation are discussed. The article concludes with focus on one pilot installation at Weyerhaeuser's Containerboard Packaging Plant in Manitowoc, Wisconsin, USA, monitoring three critical induction motors: a 75-hrho blower motor, a 50-hrho hydraulic pump motor, and a 200-hp compressor motor. Finally, the field experience gained in this plant is presented as two case studies.
Deep Learning Approaches to Semantic Relevance Modeling for Chinese Question-Answer Pairs

The human-generated question-answer pairs in the Web social communities are of great value for the research of automatic question-answering technique. Due to the large amount of noise information involved in such corpora, it is still a problem to detect the answers even though the questions are exactly located. Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. Since both the questions and their answers usually contain a small number of sentences, the relevance modeling methods have to overcome the problem of word feature sparsity. In this article, the deep learning principle is introduced to address the semantic relevance modeling task. Two deep belief networks with different architectures are proposed by us to model the semantic relevance for the question-answer pairs. According to the investigation of the textual similarity between the community-driven question-answering (cQA) dataset and the forum dataset, a learning strategy is adopted to promote our models’ performance on the social community corpora without hand-annotating work. The experimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.
MADMAC: Multiple Attribute Decision Methodology for Adoption of Clouds

Cloud Adoption decisions tend to involve multiple, conflicting criteria (attributes) with incommensurable units of measurements, which must be compared among multiple alternatives using imprecise and incomplete available information. Multi-attribute Decision Making (MADM) has been shown to provide a rational basis to aid decision making in such scenarios. We present a MADMAC framework for cloud adoption, consisting of 3 Decision Areas (DA) referred to as the Cloud Switch, Cloud Type and Vendor Choice. It requires the definition of Attributes, Alternatives and Attribute Weights, to construct a Decision Matrix and arrive at a relative ranking to identify the optimal alternative. We also present a taxonomy organized in a two level hierarchy: Server-centric clouds, Client-centric clouds and Mobile-centric clouds, which further map to detailed, specific applications or workloads. DSS presented showing algorithms derived from MADMAC can compute and optimize CA decisions separately for the three stages, where the attributes differently influence CA decisions. A modified Wide-band Delphi method is proposed for assessing the relative weights for each attribute, by workload. Relative ranks are calculated using these weights, and the Simple Additive Weighting (SAW) method is used to generate value functions for all the alternatives, and rank the alternatives by their value to finally choose the best alternative. Results from application of the method to four different types of workloads show that the method converges on reasonable cloud adoption decisions. MADMAC's key advantage is its fully quantitative and iterative convergence approach based on proven multi-attribute decision methods, which enables decision makers to comparatively assess the relative robustness of alternative cloud adoption decisions in a defensible manner. Being amenable to automation, it can respond well to even complex arrays of decision criteria inputs, unlike human decision makers. It can be implemented as a web-based DSS to readily support cloud decision making world-wide, and improved further using fuzzy TOPSIS methods, to address concerns about preferential inter-dependence of attributes, insufficient input data or judgment expertise.
Tooling Framework for Instantiating Natural Language Querying System

Recent times have seen a growing demand for natural language querying (NLQ) interfaces to retrieve information from the structured data sources such as knowledge bases. Using this interface, business users can directly interact with a database without the knowledge of the query language or the data schema. Our earlier work describes a natural language query engine called ATHENA which has several shortcoming around ease of use and compatibility with data stores, formats and flows. In this demonstration paper, we present a tooling framework to address these challenges so that one can instantiate an NLQ system with utmost ease. Our framework makes it easy and practically applicable to all NLIDB scenarios involving different sources of structured data, file formats, and ontologies to enable natural language querying on top of them with minimal human configuration. We present the tool design and the solution to the challenges towards building such a system and demonstrate its applicability in the medical domain. PVLDB Reference Format: Manasa Jammi, Jaydeep Sen, Ashish Mittal, Sagar Verma, Vardaan Pahuja, Rema Ananthanarayanan, Pranay Lohia, Hima Karanam, Diptikalyan Saha, Karthik Sankaranarayanan. Tooling Framework for Instantiating Natural Language Querying System. PVLDB, 11 (12): 2014-2017, 2018.  https://doi.org/10.14778/3229863.3236248
A Multi-Agent System Based Approach to Fight Financial Fraud : An Application to Money Laundering

A Multi-Agent System Based Approach to Fight Financial Fraud: An Application to Money Laundering Claudio Alexandre 1,∗ ID and João Balsa 1 1 Faculdade de Ciências da Universidade de Lisboa (BioISI-MAS), Campo Grande, 1749-016, Lisboa, Portugal * Author to whom correspondence should be addressed.  name Version December 19, 2017 submitted to  The anti-money laundering (AML) process has failed both in identifying suspicious cases in 1 due time as in assisting the AML analysts in decision making. Starting from a new generic anti-fraud 2 approach, this article presents the main aspects related to the development of a multi-agent system 3 that goes beyond the capture of suspicious transactions, seeking to assist the human expert in the 4 analysis of suspicious behaviour. First, a transactional behavioural profile of clients is obtained 5 in a data mining process. A set of rules, obtained through data mining over a real database, in 6 conjunction with specific rules based on legal aspects and in the expertise of the AML analysts make 7 up the agents’ knowledge base. The cases for which the system was unable to suggest a decision are 8 flagged as requiring more detailed analysis. The system analysed 6 months of real transactions and 9 indicated several suspicious profiles, a set of these suspects was investigated by the AML analysts 10 who proved the suspicion of several cases, including some that had not been identified by the systems 11 in execution.
An Energy-Efficient and Wide-Range Voltage Level Shifter With Dual Current Mirror

This brief presents an energy-efficient level shifter (LS) to convert a subthreshold input signal to an above-threshold output signal. In order to achieve a wide range of conversion, a dual current mirror (CM) structure consisting of a virtual CM and an auxiliary CM is proposed. The circuit has been implemented and optimized in SMIC 40-nm technology. The postlayout simulation demonstrates that the new LS can achieve voltage conversion from 0.2 to 1.1 V. Moreover, at the target voltage of 0.3 V, the proposed LS exhibits an average propagation delay of 66.48 ns, a total energy per transition of 72.31 fJ, and a static power consumption of 88.4 pW, demonstrating an improvement of <inline-formula> <tex-math notation="LaTeX">$6.0\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$13.1\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$89.0\times $ </tex-math></inline-formula>, respectively, when compared with Wilson CM-based LS.