nils-herrmann/clean_scirepeval huggingface dataset:
Semantic technologies for historical research: A survey

The diversity of sources of information for historical research fill a continuum between individual accounts transmitted for instance in letters but also in poems and songs, and aggregated statistical information as in the case of historical census. Historiography shares this heterogeneity and complexity of source material with other humanities fields. Methods to order this rich material, and by this ordering also to determine the way history is told are as old as history writing and vary among the different branches (or subdisciplines) of historical research. In this paper we focus on the work of historians, and even more specifically economic and social history.At the crossroad of information and historical sciences, so-called Historical Informatics or History and Computing emerged as a specific profession during the nineties of the last century. Together with computer scientists historians created a research agenda concentrating around questions how to create, design, enrich, edit, retrieve, analyze and present historical information with help of information technology. There exist a number problems and challenges in this field; some of them are closely related to semantics and meaning of knowledge in general. In this context, Semantic Web technologies can be applied in a number of situations, environments, applications of historical computing and historical information science. However, only a few number of contributions have yet considered these technologies. In this survey we present an overview of the past and present problems, challenges and advances of historical science computing, from out the perspective of Semantic technology.
A New Technique to Design Circularly Polarized Microstrip Antenna by Fractal Defected Ground Structure

A new technique to design single-feed circularly polarized (CP) microstrip antenna is proposed. The CP radiation is obtained by adjusting the dimension of the etched fractal defected ground structure (FDGS) in the ground plane. Parameter studies of the FDGS are given to illustrate the way to achieve CP radiation. The CP microstrip antennas with the second and third iterative FDGS are fabricated and measured. The measured 10-dB return-loss bandwidth of the CP microstrip antenna is about 30 MHz (1.558 to 1.588 GHz), while its 3-dB axial-ratio bandwidth is 6 MHz (1.572 to 1.578 GHz). The gain across the CP band is between 1.7 and 2.2
Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band

The market for driver assistance systems based on millimeter-wave radar sensor technology is gaining momentum. In the near future, the full range of newly introduced car models will be equipped with radar based systems which leads to high volume production with low cost potential. This paper provides background and an overview of the state of the art of millimeter-wave technology for automotive radar applications, including two actual silicon based fully integrated radar chips. Several advanced packaging concepts and antenna systems are presented and discussed in detail. Finally measurement results of the fully integrated radar front ends are shown.
Inductive Learning Algorithms and Representations for Text Categorization

1.  Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size, and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs) are particularly promising because they are very accurate, quick to train, and quick to evaluate.
Evaluation of machine learning techniques for network intrusion detection

Network traffic anomaly may indicate a possible intrusion in the network and therefore anomaly detection is important to detect and prevent the security attacks. The early research work in this area and commercially available Intrusion Detection Systems (IDS) are mostly signature-based. The problem of signature based method is that the database signature needs to be updated as new attack signatures become available and therefore it is not suitable for the real-time network anomaly detection. The recent trend in anomaly detection is based on machine learning classification techniques. We apply seven different machine learning techniques with information entropy calculation to Kyoto 2006+ data set and evaluate the performance of these techniques. Our findings show that, for this particular data set, most machine learning techniques provide higher than 90% precision, recall and accuracy. However, using area under the Receiver Operating Curve (ROC) metric, we find that Radial Basis Function (RBF) performs the best among the seven algorithms studied in this work.
Generating Stopword List for Sanskrit Language

In the era of information burst, optimization of processes for Information Retrieval, Text Summarization, Text and Data Analytic systems becomes utmost important. Therefore in order to achieve accuracy, redundant words with low or no semantic meaning must be filtered out. Such words are known as Stopwords. Stopwords list has been developed for languages like English, Chinese, Arabic, Hindi, etc but standard stopword list is still missing for Sanskrit language. Identifying stop words manually from Sanskrit text is a herculean task hence this paper reflects an automated stop word generator algorithm based on frequency of word and its implementation to ease the task. To fine-tune the generated list still manual intervention by language expert is required thus following a hybrid approach. The paper presents the first of its kind, a list of seventy-five generic stopwords of Sanskrit language extracted from a data amounting to nearly seventy-six thousand words.
Gender and Smile Classification Using Deep Convolutional Neural Networks

Facial gender and smile classification in unconstrained environment is challenging due to the invertible and large variations of face images. In this paper, we propose a deep model composed of GNet and SNet for these two tasks. We leverage the multi-task learning and the general-to-specific fine-tuning scheme to enhance the performance of our model. Our strategies exploit the inherent correlation between face identity, smile, gender and other face attributes to relieve the problem of over-fitting on small training set and improve the classification performance. We also propose the tasks-aware face cropping scheme to extract attribute-specific regions. The experimental results on the ChaLearn 16 FotW dataset for gender and smile classification demonstrate the effectiveness of our proposed methods.
Securing wireless sensor networks: a survey

The significant advances of hardware manufacturing technology and the development of efficient software algorithms make technically and economically feasible a network composed of numerous, small, low-cost sensors using wireless communications, that is, a wireless sensor network. WSNs have attracted intensive interest from both academia and industry due to their wide application in civil and military scenarios. In hostile scenarios, it is very important to protect WSNs from malicious attacks. Due to various resource limitations and the salient features of a wireless sensor network, the security design for such networks is significantly challenging. In this article, we present a comprehensive survey of WSN security issues that were investigated by researchers in recent years and that shed light on future directions for WSN security.
A 2.45 GHz harmonic suppression array antenna for rectenna application

In this paper, a 2.45 GHz harmonic suppression capability of array antenna has been presented for wireless power transfer applications. Harmonic suppression up to a fourth order of the fundamental operating frequency has been achieved by using rectangular slot Defected Ground Structure (DGS). The designed antenna uses an FR4 material having thickness 1.6 mm as a substrate which has loss tangent tanδ) 0.02. The gain and radiation efficiency are 4.5dB, 75% respectively. This antenna is suitable for rectenna applications.
AGDISTIS - Graph-Based Disambiguation of Named Entities Using Linked Data

Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Web of Data yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. While several approaches aim to tackle this problem, they still achieve poor accuracy. We address this drawback by presenting AGDISTIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures. Based on this combination, AGDISTIS can efficiently detect the correct URIs for a given set of named entities within an input text. We evaluate our approach on eight different datasets against state-of-theart named entity disambiguation frameworks. Our results indicate that we outperform the state-of-the-art approach by up to 29% F-measure.
Script independent scene text segmentation using fast stroke width transform and GrabCut

In this paper, a novel script independent scene text segmentation method is presented. The proposed method introduces an approach for character segmentation which combines the advantages of connected component analysis, stroke width analysis and graph cuts. The method was evaluated on a standard dataset, and also on a custom dataset of scene images of text from various Indian scripts. Additionally, a fast implementation of Stroke Width Transform (SWT) is presented here, which improves speed over other implementations by 79%. From our experiments, we have achieved encouraging results on the usefulness of the combination of text/non-text classification with GrabCut. The technique is robust to the language/script of text.
A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects

We conducted a quantitative meta-analysis of previous research on the technology acceptance model (TAM) in an attempt to make well-grounded statements on the role of subjective norm. Furthermore, we compared TAM results by taking into account moderating effects of one individual-related factor (type of respondents), one technology-related factor (type of technology), and one contingent factor (culture). Results indicated a significant influence of subjective norm on perceived usefulness and behavioral intention to use. Moderating effects were found for all three factors. The findings yielded managerial implications for both intracompany and market-based settings.
Dermoscopy: basic concepts.

Dermoscopy is a very useful technique for the analysis of pigmented skin lesions. It represents a link between clinical and histological views, permitting an earlier diagnosis of skin melanoma. It also helps in the diagnosis of many other pigmented skin lesions, such as seborrheic keratosis, pigmented basal cell carcinoma, hemangioma, blue nevus, atypical nevus, and mole, which can often clinically simulate melanoma. In this article, dermoscopy is reviewed from its history to the basic concepts of the interpretation of dermoscopic images. The goal is to introduce this subject to those not yet familiar with it, in order to instigate and encourage the training and practice of this technique of growing importance for everyday usage.
Bowel vaginoplasty in children and young women: an institutional experience with 55 patients

Absence of a vagina owing to congenital Mullerian defects or other acquired causes requires reconstruction of the female genital passage. We present our experience using various bowel segments. Bowel vaginoplasty was performed in 55 patients from January 2004 through May 2014 for cervicovaginal atresia (20), Mayer–Rokitansky–Küster–Hauser (MRKH) syndrome (20), distal vaginal atresia (8), cloaca (2), cervical atresia (1), complex urogenital sinus (1), transverse vaginal septum (1), rhabdomyosarcoma of the vagina (1), and traumatic stricture of the vagina (1). The bowel segments used were sigmoid (50), ileum (2), anorectovestibular fistula ( 2), and loop rectovaginoplasty (1). Thirty-nine patients who had the proximal vagina or uterus anastomosed to the bowel segment reported regular menstrual flows. Three patients are sexually active with satisfactory coital function. None of our patients developed pyometra. Five patients had neovaginal mucosal prolapse. Two patients had severe stenosis requiring excision of the neovagina. Seven patients had mild stenosis requiring dilatations in 6 patients and V-Y meatoplasty for 1 patient. One patient had a descending colon anastomotic leak requiring a diversion ileostomy. Genital reconstruction with bowel vaginoplasty is a highly skilled operation that provides a durable and lubricated replacement of the vagina with good outcomes. Utero-coloneovaginoplasty is a safe procedure preserving the menstrual flow in patients with a functional uterine fundus.
A new model for learning in graph domains

In several applications the information is naturally represented by graphs. Traditional approaches cope with graphical data structures using a preprocessing phase which transforms the graphs into a set of flat vectors. However, in this way, important topological information may be lost and the achieved results may heavily depend on the preprocessing stage. This paper presents a new neural model, called graph neural network (GNN), capable of directly processing graphs. GNNs extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs. A learning algorithm for GNNs is proposed and some experiments are discussed which assess the properties of the model.
A dynamic model for integrating simple web spam classification techniques

Over the last years, Internet spam content has spread enormously inside web sites mainly due to the emergence of new web technologies oriented towards the online sharing of resources and information. In such a situation, both academia and industry have shown their concern to accurately detect and effectively control web spam, resulting in a good number of anti-spam techniques currently available. However, the successful integration of different algorithms for web spam classification is still a challenge. In this context, the present study introduces WSF2, a novel web spam filtering framework specifically designed to take advantage of multiple classification schemes and algorithms. In detail, our approach encodes the life cycle of a case-based reasoning system, being able to use appropriate knowledge and dynamically adjust different parameters to ensure continuous improvement in filtering precision with the passage of time. In order to correctly evaluate the effectiveness of the dynamic model, we designed a set of experiments involving a publicly available corpus, as well as different simple well-known classifiers and ensemble approaches. The results revealed that WSF2 performed well, being able to take advantage of each classifier and to achieve a better performance when compared to other alternatives.
Enhancing Android permission through usage control: a BYOD use-case

The Bring Your Own Device (BYOD) paradigm, where the employees of a company install an application on their mobile devices to access company privileged information, is becoming very popular in the business environment. In order to perform their tasks, BYOD applications typically require a large set of rights which, in Android mobile devices, must be statically granted in order to have the application installed. However, this access control model is too coarse grained for the BYOD scenario, because employees would like to have a finer control on the rights granted to such applications, for instance to protect their privacy when they are not on duty. To address this issue, we propose to enhance the Android permission system through a Usage Control-based framework enabling employees to write policies which are continuously enforced while BYOD applications are running. This framework acts as a dynamic permission manager, where usage control policies grants, revokes and restores permissions to running applications on the base of mutable attributes describing the current context. Context is observed by using Android device standard APIs to monitor attributes such as mobile device location, WiFi status, battery level, current date and time, and so on. External trusted attribute providers can also be exploited.
Rationalizing Sentiment Analysis in Tensorflow

Sentiment analysis using deep learning models is a leading subject of interest in Natural Language Processing that is as powerful as it is opaque. Current state-ofthe-art models can produce accurate predictions, but they provide little insight as to why the model predicted this sentiment. Businesses relying on these models might be less likely to act on insight given the lack of evidence for predictions. These people would be more likely to trust such predictions if a brief explanation of the outcome is provided. Recent work by Lei et al . has set forth a framework for a multi-aspect sentiment analysis concurrently providing text rationalization with each prediction. This framework sets forth a two-part approach, which summarizes a review and predicts a sentiment. In this paper, we explore the performance of this framework, seeking to recreate and improve upon it in TensorFlow.
Knowledge Engineering with Markov Logic Networks : A Review

Within the realm of statistical relational knowledge representation formalisms, Markov logic is perhaps one of the most flexible and general languages, for it generalises both first-order logic (for finite domains) and probabilistic graphical models. Knowledge engineering with Markov logic is, however, not a straightforward task. In particular, modelling approaches that are too firmly rooted in the principles of logic often tend to produce unexpected results in practice. In this paper, I collect a number of issues that are relevant to knowledge engineering practice: I describe the fundamental semantics of Markov logic networks and explain how simple probabilistic properties can be represented. Furthermore, I discuss fallacious modelling assumptions and summarise conditions under which generalisation across domains may fail. As a collection of fundamental insights, the paper is primarily directed at knowledge engineers who are new to Markov logic.
Deep Belief Networks for phone recognition

Hidden Markov Models (HMMs) have been the state-of-the-art techniques for acoustic modeling despite their unrealistic independence assumptions and the very limited representational capacity of their hidden states. There are many proposals in the research community for deeper models that are capable of modeling the many types of variability present in the speech generation p r cess. Deep Belief Networks (DBNs) have recently proved to be very effective fo r a variety of machine learning problems and this paper applies DBNs to acous ti modeling. On the standard TIMIT corpus, DBNs consistently outperform ot her techniques and the best DBN achieves a phone error rate (PER) of 23.0% on the T IMIT core test set.
A regression-based radar-mote system for people counting

People counting is key to a diverse set of sensing applications. In this paper, we design a mote-scale event-driven solution that uses a low-power pulsed radar to estimate the number of people within the ~10m radial range of the radar. In contrast to extant solutions, most of which use computer vision, our solution is light-weight and private. It also better tolerates the presence of obstacles that partially or fully impair line of sight; this is achieved by accounting for “small” indirect radio reflections via joint time-frequency domain features. The counter itself is realized using Support Vector Regression; the regression map is learned from a medium sized dataset of 0-~40 people in various indoor room settings. 10-fold cross validation of our counter yields a mean absolute error of 2.17 between the estimated count and the ground truth and a correlation coefficient of 0.97.We compare the performance of our solution with baseline counters.
Design of millimeter-wave transformer balun with isolation circuit in silicon based technology

In this paper a novel method to design a transformer based balun with isolation between output ports and matching characteristic at all ports is proposed. The main issue of transformer balun at millimeter-wave frequencies is that it has poor matching and isolation performance. In this work, an isolation circuit is introduced to place between output ports in the transformer balun design. As a validation, comparison has been made between the balun with and without isolation circuit, which has shown great performance improvement of proposed design. Moreover, design example is given for millimeter-wave on-chip implementation of this isolation circuit in standard silicon based technology with 6 metal layers.
Physiological Signals ( ECG , Blood Pressure , Respiration , Motion , Oxygen , Saturation etc )

This document gives formatting instructions for authors preparing manuscripts for publication in the International Journals/Conferences with ER Publications on a wide range of subject areas given in “Topics Covered”. The document contains information regarding desktop publishing format, type sizes, and typefaces. Style rules are provided that explains how to handle equations, units, figures, tables, abbreviations, and acronyms. Sections are also devoted to the preparation of acknowledgments, references, and authors' biographies. The abstract is limited to 150 words and cannot contain equations, figures, tables, or references. It should concisely state what was done, how it was done, principal results, and their significance.
Bartender: a fast and accurate clustering algorithm to count barcode reads

Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data.



In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a 'multiple time point' mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods.


Availability and implementation
Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1.



sasha.levy@stonybrook.edu or song.wu@stonybrook.edu.



Supplementary data are available at Bioinformatics online.