nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting graph kernels for high performance biomedical relation extraction

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences.



Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM performed better than APG kernel for the BioInfer dataset, in the Area Under Curve (AUC) measure (74% vs 69%). However, for all the other PPI datasets, namely AIMed, HPRD50, IEPA and LLL, ASM is substantially outperformed by the APG kernel in F-score and AUC measures.



We demonstrate a high performance Chemical Induced Disease relation extraction, without employing external knowledge sources or task specific heuristics. Our work shows that graph kernels are effective in extracting relations that are expressed in multiple sentences. We also show that the graph kernels, namely the ASM and APG kernels, substantially outperform the tree kernels. Among the graph kernels, we showed the ASM kernel as effective for biomedical relation extraction, with comparable performance to the APG kernel for datasets such as the CID-sentence level relation extraction and BioInfer in PPI. Overall, the APG kernel is shown to be significantly more accurate than the ASM kernel, achieving better performance on most datasets.
A Framework for Computational Thinking Based on a Systematic Research Review

Computational Thinking (CT) has become popular in recent years and has been recognised as an essential skill for all, as members of the digital age. Many researchers have tried to define CT and have conducted studies about this topic. However, CT literature is at an early stage of maturity, and is far from either explaining what CT is, or how to teach and assess this skill. In the light of this state of affairs, the purpose of this study is to examine the purpose, target population, theoretical basis, definition, scope, type and employed research design of selected papers in the literature that have focused on computational thinking, and to provide a framework about the notion, scope and elements of CT. In order to reveal the literature and create the framework for computational thinking, an inductive qualitative content analysis was conducted on 125 papers about CT, selected according to pre-defined criteria from six different databases and digital libraries. According to the results, the main topics covered in the papers composed of activities (computerised or unplugged) that promote CT in the curriculum. The targeted population of the papers was mainly K-12. Gamed-based learning and constructivism were the main theories covered as the basis for CT papers. Most of the papers were written for academic conferences and mainly composed of personal views about CT. The study also identified the most commonly used words in the definitions and scope of CT, which in turn formed the framework of CT. The findings obtained in this study may not only be useful in the exploration of research topics in CT and the identification of CT in the literature, but also support those who need guidance for developing tasks or programs about computational thinking and informatics.
Personality determinants of online shopping : Explaining online purchase intentions using a hierarchical approach

What determines whether one purchases products online or through another channel? Just as retailers seek to develop online retail websites as profitable channels of distribution, researchers have pursued answers to this very question. In pursuing this line of research, several approaches have been utilized including those based upon behavioral economics, lifestyle analysis, and merchandising effects. While some of this work identifies the potential moderation of personality traits most of it focuses on factors related to time, costs and benefits, and shopping context. Following the hierarchical approach to personality developed by Mowen (Mowen J. The 3M Model of Motivation and Personality. Norwell, MA: Kluwer Academic Press, 2000.), this study seeks to understand online purchase intent using personality constructs. The present study uses data from an online consumer panel to develop a hierarchical model of personality useful for predicting consumer intentions to purchase products and services online.
Data Mining: Practical Machine Learning Tools and Techniques - Book Review

Witten and Frank 's textbook was one of two books that 1 used for a data mining class in the Fall o f 2001. T h e book covers all major methods o f data mining that p roduce a knowledge representa t ion as output . Knowledge representa t ion is hereby unders tood as a representat ion that can be studied, unders tood, and interpreted by human beings, at least in principle. Thus , neural networks and genetic a lgor i thms are excluded f rom the topics of this textbook. We need to say "can be unders tood in pr inciple" because a large decision tree or a large rule set may be as hard to interpret as a neural network.
Supply chain management and advanced planning--basics, overview and challenges

Literature on supply chain management (SCM) covers several disciplines and is growing rapidly. This paper firstly aims at extracting the essence of SCM and advanced planning in the form of two conceptual frameworks: The house of SCM and the supply chain planning matrix. As an illustration, contributions to this feature issue will then be assigned to the building blocks of the house of SCM or to the modules covering the supply chain planning matrix. Secondly, focusing on software for advanced planning, we outline its main shortcomings and present latest research results for their resolution.
Usability of car dashboard displays for elder drivers

The elder population is rising worldwide; in the US, no longer being able to drive is a significant marker of loss of independence. One of the approaches to helping elders drive more safely is to investigate the use of automotive user interface technology, and specifically, to explore the instrument panel (IP) display design to help attract and manage attention and make information easier to interpret.
 In this paper, we explore the premise that dashboard displays can be better designed to support elder drivers, their information needs, and their cognitive capabilities. We conducted a study to understand which display design features are critically linked to issues of divided attention and driving performance. We found that contrast of size and reduced clutter are instrumental in enhancing driving performance, particularly for the elder population. Surprisingly, our results showed that color elements have a negative effect on driving performance for elders, while color elements and fills slightly improve performance. We conclude with design implications generated from this work.
Business Intelligence Maturity Models : Toward New Integrated Model

Business Intelligence (BI) has become one of the most important research areas that helps organizations and managers to better decision making process. This paper aims to show the barriers to BI adoption and discusses the most commonly used Business Intelligence Maturity Models (BIMMs). The aim also is to highlight the pitfalls of these BIMMs in order reach a solution. Using new techniques such as Service Oriented Architecture (SOA), Service Oriented Business Intelligence (SOBI) or Event Driven Architecture (EDA) leads to a new model. The proposed model named Service-Oriented Business Intelligence Maturity Model (SOBIMM) is briefly described in this paper.
Lean Software Development: A Tutorial

To put the concept of lean software development in context, it's useful to point out similarities and differences with agile software development. Agile development methods have generally expected system architecture and interaction design to occur outside the development team, or to occur in very small increments within the team. Because of this, agile practices often prove to be insufficient in addressing issues of solution design, user interaction design, and high-level system architecture. Increasingly, agile development practices are being thought of as good ways to organize software development, but insufficient ways to address design. Because design is fundamentally iterative and development is fundamentally iterative, the two disciplines suffer if they are not carefully integrated with each other. Because lean development lays out a set of principles that demand a whole-product, complete life-cycle, cross-functional approach, it's the more likely candidate to guide the combination of design, development, deployment, and validation into a single feedback loop focused on the discovery and delivery of value.
Big data and digital forensics

One of the main challenges in digital forensics is the increasing volume of data that needs to be analyzed. This problem has become even more pronounced with the emergence of big data and calls for a rethink on the way digital forensics investigations have been handled over the past years. This paper briefly discusses the challenges and needs of digital forensics in the face of the current trends and requirements of different investigations. A digital forensics analysis framework that puts into consideration the existing techniques as well as the current challenges is proposed. The purpose of the framework is to reassess the various stages of the digital forensics examination process and introduce into each stage the required techniques to enhance better collection, analysis, preservation and presentation in the face of big data and other challenges facing digital forensics.
Screening antimicrobial activity of various extracts of Urtica dioica.

Urtica dioica or stinging nettle is traditionally used as an herbal medicine in Western Asia. The current study represents the investigation of antimicrobial activity of U. dioica from nine crude extracts that were prepared using different organic solvents, obtained from two extraction methods: the Soxhlet extractor (Method I), which included the use of four solvents with ethyl acetate and hexane, or the sequential partitions (Method II) with a five solvent system (butanol). The antibacterial and antifungal activities of crude extracts were tested against 28 bacteria, three yeast strains and seven fungal isolates by the disc diffusion and broth dilution methods. Amoxicillin was used as positive control for bacteria strains, vancomycin for Streptococcus sp., miconazole nitrate (30 microg/mL) as positive control for fungi and yeast, and pure methanol (v/v) as negative control. The disc diffusion assay was used to determine the sensitivity of the samples, whilst the broth dilution method was used for the determination of the minimal inhibition concentration (MIC). The ethyl acetate and hexane extract from extraction method I (EA I and HE I) exhibited highest inhibition against some pathogenic bacteria such as Bacillus cereus, MRSA and Vibrio parahaemolyticus. A selection of extracts that showed some activity was further tested for the MIC and minimal bactericidal concentrations (MBC). MIC values of Bacillus subtilis and Methicillin-resistant Staphylococcus aureus (MRSA) using butanol extract of extraction method II (BE II) were 8.33 and 16.33mg/mL, respectively; while the MIC value using ethyl acetate extract of extraction method II (EAE II) for Vibrio parahaemolyticus was 0.13mg/mL. Our study showed that 47.06% of extracts inhibited Gram-negative (8 out of 17), and 63.63% of extracts also inhibited Gram-positive bacteria (7 out of 11); besides, statistically the frequency of antimicrobial activity was 13.45% (35 out of 342) which in this among 21.71% belongs to antimicrobial activity extracts from extraction method I (33 out of 152 of crude extracts) and 6.82% from extraction method II (13 out of 190 of crude extracts). However, crude extracts from method I exhibited better antimicrobial activity against the Gram-positive bacteria than the Gram-negative bacteria. The positive results on medicinal plants screening for antibacterial activity constitutes primary information for further phytochemical and pharmacological studies. Therefore, the extracts could be suitable as antimicrobial agents in pharmaceutical and food industry.
Text Classification for Organizational Researchers

Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger, manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this article is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. To help researchers develop their own text classifiers, the R code associated with each step is presented in a tutorial. The tutorial draws from our own work on job vacancy mining. We end the article by discussing how researchers can validate a text classification model and the associated output.
App traffic mutation: Toward defending against mobile statistical traffic analysis

Fingerprinting of mobile device apps is currently an attractive and affordable data gathering technique. Even in the presence of encryption, it is possible to fingerprint a user's app by means of packet-level traffic analysis in which side-channel information is used to determine specific patterns in packets. Knowing the specific apps utilized by smartphone users is a serious privacy concern. In this study, we address the issue of defending against statistical traffic analysis of Android apps. First, we present a methodology for the identification of mobile apps using traffic analysis. Further, we propose confusion models in which we obfuscate packet lengths information leaked by mobile traffic, and we shape one class of app traffic to obscure its class features with minimum overhead. We assess the efficiency of our model using different apps and against a recently published approach for mobile apps classification. We focus on making it hard for intruders to differentiate between the altered app traffic and the actual one using statistical analysis. Additionally, we study the tradeoff between shaping cost and traffic privacy protection, specifically the needed overhead and realization feasibility. We were able to attain 91.1% classification accuracy. Using our obfuscation technique, we were able to reduce this accuracy to 15.78%.
A Dataset for Document Grounded Conversations

This paper introduces a document grounded dataset for conversations. We define “Document Grounded Conversations” as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. This positions this dataset to not only provide a relevant chat history while generating responses but also provide a source of information that the models could use. We describe two neural architectures that provide benchmark performance on the task of generating the next response. We also evaluate our models for engagement and fluency, and find that the information from the document helps in generating more engaging and fluent responses.
Can’t touch this: the impact of augmented reality versus touch and non-touch interfaces on perceived ownership

The rise of augmented reality (AR) technology presents e-retailers with new opportunities. According to previous research, it is a technology that can positively affect engagement, brand recall and purchase confidence. Mobile-enabled augmented reality differs from regular mobile phone use as the technology virtually overlays images or information to the real environment. As the use of a touch screen device (i.e. smartphone vs. laptop) has previously been found to positively affect feelings of perceived ownership, the current study examines whether the possibility to virtually manipulate a product on a mobile AR application would have an even stronger effect. This is examined for products with either material properties (i.e. products that require the examination of sensory information) or geometric properties (i.e. products that can be examined via written and/or visual information). The findings reveal that AR does indeed result in higher levels of perceived ownership, particularly in case of material products.
Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks

Over the past decade, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system [7] to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is power learning machine learning algorithm in classification while extracting high-level features. In this paper, we used convolutional neural network to classify Alzheimer’s brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Classification of clinical data such as Alzheimer’s disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimer’s subjects from normal controls where the accuracy of test data on trained data reached 96.85%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning classification is most powerful method to distinguish clinical data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated systems.
Outlier detection for high-dimensional data

Outlier detection is an integral component of statistical modelling and estimation. For highdimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the asymptotic distribution of the distance, which enables us to control the Type I error and deliver robust outlier detection. Simulation studies show that the proposed method behaves well for high-dimensional data.
Astrocytes, neurons, synapses: a tripartite view on cortical circuit development

In the mammalian cerebral cortex neurons are arranged in specific layers and form connections both within the cortex and with other brain regions, thus forming a complex mesh of specialized synaptic connections comprising distinct circuits. The correct establishment of these connections during development is crucial for the proper function of the brain. Astrocytes, a major type of glial cell, are important regulators of synapse formation and function during development. While neurogenesis precedes astrogenesis in the cortex, neuronal synapses only begin to form after astrocytes have been generated, concurrent with neuronal branching and process elaboration. Here we provide a combined overview of the developmental processes of synapse and circuit formation in the rodent cortex, emphasizing the timeline of both neuronal and astrocytic development and maturation. We further discuss the role of astrocytes at the synapse, focusing on astrocyte-synapse contact and the role of synapse-related proteins in promoting formation of distinct cortical circuits.
Robust method for outdoor localization of a mobile robot using received signal strength in low power wireless networks

This paper deals with localization of a mobile robot using received signal strength indicator (RSSI) in low power IEEE 802.15.4 conform wireless communication in an outdoor environment. Hardware modifications are derived to reduce the radio irregularity and to increase the uniqueness of the measured RSSI to distance. A novel algorithm is elaborated allowing for sub meter accuracy. It accounts for the noise and implicitly models the uncertainty. Additionally it is robust to node failures. To further improve the accuracy a particle filter is employed to perform probabilistic sensor fusion of odometry, ultrasonic and RSSI sensors and a map. The suitability of this approach is shown with real measurements achieving a mean positioning error of 0.32 m.
gSpan: Graph-Based Substructure Pattern Mining

We invesrigare new appmaches for frequent graph-based patrem mining in graph darasers andpmpose a novel ofgorirhm called gSpan (graph-based,Tubsmrure parrern mining), which discovers frequenr subsrrucrures z h o u r candidate generorion. &an builds a new lexicographic or. der among graphs, and maps each graph to a unique minimum DFS code as irs canonical label. Based on rhis lexicographic orde,: &an adopts rhe deprh-jrsr search srraregy ro mine frequenr cannecred subgraphs eflciently. Our performance study shows rhar gSpan subsianriolly outperforms previous algorithm, somerimes by an order of magnirude.