nils-herrmann/clean_scirepeval huggingface dataset:
SEMANTIC WEB MINING FOR INTELLIGENT WEB PERSONALIZATION

Semantic Web Mining is the outcome of two new and fast developing domains: Semantic Web and Data Mining. The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.  Mining is the nontrivial process of identifying valid, previously unknown, potentially useful patterns in data. Semantic Web Mining refers to the application of data mining techniques to extract knowledge from World Wide Web or the area of data mining that refers to the use of algorithms for extracting patterns from resources distributed over in the web. The aim of Semantic Web Mining is to discover and retrieve useful and interesting patterns from a huge set of web data. This web data consists of different kind of information, including web structure data, web log data and user profiles data. Semantic Web Mining is a relatively new area, broadly interdisciplinary, attracting researchers from: computer science, information retrieval specialists and experts from business studies fields. Web data mining includes web content mining, web structure mining and web usage mining. All of these approaches attempt to extract knowledge from the web, produce some useful results from the knowledge extracted and apply these results to the real world problems. To improve the internet service quality and increase the user click rate on a specific website, it is necessary for a web developer to know what the user really want to do, predict which pages the user is potentially interested in. In this paper, various techniques for Semantic Web mining like web content mining, web usage mining and web structure mining are discussed. Our main focus is on web usage mining and its application in web personalization. Study shows that the accuracy of recommendation system has improved significantly with the use of semantic web mining in web personalization.
MOOC Pedagogy : Gleaning Good Practice from Existing MOOCs

The revolutionary potential of massive open online courses (MOOCs) has been met with much skepticism, particularly in terms of the quality of learning offered. Believing that a focus on learning is more important than a focus on course completion rates, this position paper presents a pedagogical assessment of MOOCs using Chickering and Gamson's Seven Principles of Good Practice in Undergraduate Education and Bloom's taxonomy, based on the author's personal experience as a learner in four xMOOCs. Although most xMOOCs have similar characteristics, the author shows that they are not all offered in exactly the same way, and some provide more sound pedagogy that develops higher order thinking, whereas others do not. The author uses this evaluation, as well as reviews of other xMOOCs in the literature, to glean some good pedagogical practices in xMOOCs and areas for improvement.
Spectral Networks and Locally Connected Networks on Graphs

Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains without the action of a translation group. In particular, we propose two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. We show through experiments that for lowdimensional graphs it is possible to learn convolutional layers with a number of parameters independent of the input size, resulting in efficient deep architectures.
Deep learning of texture and structural features for multiclass Alzheimer's disease classification

This work proposes multiclass deep learning classification of Alzheimer's disease (AD) using novel texture and other associated features extracted from structural MRI. Two distinct learning models (Model 1 and 2) are presented where both include subcortical area specific feature extraction, feature selection and stacked auto-encoder (SAE) deep neural network (DNN). The models learn highly complex and subtle differences in spatial atrophy patterns using white matter volumes, gray matter volumes, cortical surface area, cortical thickness, and different types of Fractal Brownian Motion co-occurrence matrices for texture as features to classify AD from cognitive normal (CN) and mild cognitive impairment (MCI) in dementia patients. A five layer SAE with state-of-the-art dropout learning is trained on a publicly available ADNI dataset and the model performances are evaluated at two levels: one using in-house tenfold cross validation and another using the publicly available CADDementia competition. The in-house evaluations of our two models achieve 56.6% and 58.0% tenfold cross validation accuracies using 504 ADNI subjects. For the public domain evaluation, we are the first to report DNN to CADDementia and our methods yield competitive classification accuracies of 51.4% and 56.8%. Further, both of our proposed models offer higher True Positive Fraction (TPF) for AD class when compared to the top-overall ranked algorithm while Model 1 also ties for top diseased class sensitivity at 58.2% in the CADDementia challenge. Finally, Model 2 achieves strong disease class sensitivity with improvement in specificity and overall accuracy. Our algorithms have the potential to provide a rapid, objective, and non-invasive assessment of AD.
Slot Grammar: A System for Simpler Construction of Practical Natural Language Grammars

Natural Language Engineering encourages papers reporting research with a clear potential for practical application. Theoretical papers that consider techniques in sufficient detail to provide for practical implementation are also welcomed, as are shorter reports of on-going research, conference reports, comparative discussions of NLE products, and policy-oriented papers examining e.g. funding programmes or market opportunities. All contributions are peer reviewed and the review process is specifically designed to be fast, contributing to the rapid publication of accepted papers.
Using focus groups in medical education research: AMEE Guide No. 91.

Qualitative research methodology has become an established part of the medical education research field. A very popular data-collection technique used in qualitative research is the "focus group". Focus groups in this Guide are defined as "… group discussions organized to explore a specific set of issues … The group is focused in the sense that it involves some kind of collective activity … crucially, focus groups are distinguished from the broader category of group interview by the explicit use of the group interaction as research data" (Kitzinger 1994, p. 103). This Guide has been designed to provide people who are interested in using focus groups with the information and tools to organize, conduct, analyze and publish sound focus group research within a broader understanding of the background and theoretical grounding of the focus group method. The Guide is organized as follows: Firstly, to describe the evolution of the focus group in the social sciences research domain. Secondly, to describe the paradigmatic fit of focus groups within qualitative research approaches in the field of medical education. After defining, the nature of focus groups and when, and when not, to use them, the Guide takes on a more practical approach, taking the reader through the various steps that need to be taken in conducting effective focus group research. Finally, the Guide finishes with practical hints towards writing up a focus group study for publication.
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification

The existence and use of standard test collections in information retrieval experimentation allows results to be compared between research groups and over time. Such comparisons, however, are rarely made. Most researchers only report results from their own experiments, a practice that allows lack of overall improvement to go unnoticed. In this paper, we analyze results achieved on the TREC Ad-Hoc, Web, Terabyte, and Robust collections as reported in SIGIR (1998–2008) and CIKM (2004–2008). Dozens of individual published experiments report effectiveness improvements, and often claim statistical significance. However, there is little evidence of improvement in ad-hoc retrieval technology over the past decade. Baselines are generally weak, often being below the median original TREC system. And in only a handful of experiments is the score of the best TREC automatic run exceeded. Given this finding, we question the value of achieving even a statistically significant result over a weak baseline. We propose that the community adopt a practice of regular longitudinal comparison to ensure measurable progress, or at least prevent the lack of it from going unnoticed. We describe an online database of retrieval runs that facilitates such a practice.
WAIS-IV Seven-Subtest Short Form: Validity and Clinical Use in Schizophrenia.

This study assesses the psychometric properties of Ward's seven-subtest short form (SF) for WAIS-IV in a sample of adults with schizophrenia (SZ) and schizoaffective disorder.



Seventy patients diagnosed with schizophrenia or schizoaffective disorder were administered the full version of the WAIS-IV. Four different versions of the Ward's SF were then calculated. The subtests used were: Similarities, Digit Span, Arithmetic, Information, Coding, Picture Completion, and Block Design (BD version) or Matrix Reasoning (MR version). Prorated and regression-based formulae were assessed for each version.



The actual and estimated factorial indexes reflected the typical pattern observed in schizophrenia. The four SFs correlated significantly with their full-version counterparts, but the Perceptual Reasoning Index (PRI) correlated below the acceptance threshold for all four versions. The regression-derived estimates showed larger differences compared to the full form. The four forms revealed comparable but generally low clinical category agreement rates for factor indexes. All SFs showed an acceptable reliability, but they were not correlated with clinical outcomes.



The WAIS-IV SF offers a good estimate of WAIS-IV intelligence quotient, which is consistent with previous results. Although the overall scores are comparable between the four versions, the prorated forms provided a better estimation of almost all indexes. MR can be used as an alternative for BD without substantially changing the psychometric properties of the SF. However, we recommend a cautious use of these abbreviated forms when it is necessary to estimate the factor index scores, especially PRI, and Processing Speed Index.
A geometrically inspired category as a meaning space for natural language

In this dissertation we apply the theory of conceptual spaces to the domain of shapes to develop a mathematically rigorous conceptual space providing a natural, geometrically motivated encoding of shapes that can be used in the compositional distributional model of meaning. To this end, we abstractly treat hierarchical conceptual spaces, developing the mathematical tools required to define these spaces and reason about them. In particular, we define generalisations of the notions of metrics and convexity and apply these generalisations to our conceptual space. We then define a category for these spaces and discuss some of the aspects of language that the model captures. We specialise this category to the specific domain of shapes by implementing a variant of the Marr-Nishihara model. We also introduce a model for representing interactions with prepositions.
NANOTECHNOLOGY IN MEDICINE AND ANTIBACTERIAL EFFECT OF SILVER NANOPARTICLES

Nanotechnology is expected to open some new aspects to fight and prevent diseases using atomic scale tailoring of materials. The ability to uncover the structure and function of biosystems at the nanoscale, stimulates research leading to improvement in biology, biotechnology, medicine and healthcare. The size of nanomaterials is similar to that of most biological molecules and structures; therefore, nanomaterials can be useful for both in vivo and in vitro biomedical research and applications. The integration of nanomaterials with biology has led to the development of diagnostic devices, contrast agents, analytical tools, physical therapy applications, and drug delivery vehicles. In all the nanomaterials with antibacterial properties, metallic nanoparticles are the best. Nanoparticles increase chemical activity due to crystallographic surface structure with their large surface to volume ratio. The importance of bactericidal nanomaterials study is because of the increase in new resistant strains of bacteria against most potent antibiotics. This has promoted research in the well known activity of silver ions and silver-based compounds, including silver nanoparticles. This effect was size and dose dependent and was more pronounced against gram-negative bacteria than gram-positive organisms.
Platform Competition in Two-Sided Markets

Many if not most markets with network externalities are two-sided. To succeed, platforms in industries such as software, portals and media, payment systems and the Internet, must “get both sides of the market on board ”. Accordingly, platforms devote much attention to their business model, that is to how they court each side while making money overall. The paper builds a model of platform competition with two-sided markets. It unveils the determinants of price allocation and enduser surplus for different governance structures (profit-maximizing platforms and not-for-profit joint undertakings), and compares the outcomes with those under an integrated monopolist and a Ramsey planner.
Chilean underground mine dataset

This article presents a robotic dataset collected from the largest underground copper mine in the world. The sensor measurements from a 3D scanning lidar, a 2D radar, and stereo cameras were recorded from an approximately two kilometer traverse of a production-active tunnel. The equipment used and the data collection process is discussed in detail, along with the format of the data. This dataset is suitable for research in robotic navigation, as well as simultaneous localization and mapping. The download instructions are available at the following website http://dataset.amtc.cl.
Comparative Analysis of Neural QA models on SQuAD

The task of Question Answering has gained prominence in the past few decades for testing the ability of machines to understand natural language. Large datasets for Machine Reading have led to the development of neural models that cater to deeper language understanding compared to information retrieval tasks. Different components in these neural architectures are intended to tackle different challenges. As a first step towards achieving generalization across multiple domains, we attempt to understand and compare the peculiarities of existing end-to-end neural models on the Stanford Question Answering Dataset (SQuAD) by performing quantitative as well as qualitative analysis of the results attained by each of them. We observed that prediction errors reflect certain model-specific biases, which we further discuss in this paper.
Online VNF Scaling in Datacenters

Network Function Virtualization (NFV) is a promising technology that promises to significantly reduce the operational costs of network services by deploying virtualized network functions (VNFs) to commodity servers in place of dedicated hardware middleboxes. The VNFs are typically running on virtual machine instances in a cloud infrastructure, where the virtualization technology enables dynamic provisioning of VNF instances, to process the fluctuating traffic that needs to go through the network functions in a network service. In this paper, we target dynamic provisioning of enterprise network services - expressed as one or multiple service chains - in cloud datacenters, and design efficient online algorithms without requiring any information on future traffic rates. The key is to decide the number of instances of each VNF type to provision at each time, taking into consideration the server resource capacities and traffic rates between adjacent VNFs in a service chain. In the case of a single service chain, we discover an elegant structure of the problem and design an efficient randomized algorithm achieving a e/(e-1) competitive ratio. For multiple concurrent service chains, an online heuristic algorithm is proposed, which is O(1)-competitive. We demonstrate the effectiveness of our algorithms using solid theoretical analysis and trace-driven simulations.
A benchmark for the evaluation of RGB-D SLAM systems

In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We recorded a large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system. The sequences contain both the color and depth images in full sensor resolution (640 × 480) at video frame rate (30 Hz). The ground-truth trajectory was obtained from a motion-capture system with eight high-speed tracking cameras (100 Hz). The dataset consists of 39 sequences that were recorded in an office environment and an industrial hall. The dataset covers a large variety of scenes and camera motions. We provide sequences for debugging with slow motions as well as longer trajectories with and without loop closures. Most sequences were recorded from a handheld Kinect with unconstrained 6-DOF motions but we also provide sequences from a Kinect mounted on a Pioneer 3 robot that was manually navigated through a cluttered indoor environment. To stimulate the comparison of different approaches, we provide automatic evaluation tools both for the evaluation of drift of visual odometry systems and the global pose error of SLAM systems. The benchmark website  contains all data, detailed descriptions of the scenes, specifications of the data formats, sample code, and evaluation tools.
Architecting to achieve a billion requests per second throughput on a single key-value store server platform

Distributed in-memory key-value stores (KVSs), such as memcached, have become a critical data serving layer in modern Internet-oriented data center infrastructure. Their performance and efficiency directly affect the QoS of web services and the efficiency of data centers. Traditionally, these systems have had significant overheads from inefficient network processing, OS kernel involvement, and concurrency control. Two recent research thrusts have focused on improving key-value performance. Hardware-centric research has started to explore specialized platforms including FPGAs for KVSs; results demonstrated an order of magnitude increase in throughput and energy efficiency over stock memcached. Software-centric research revisited the KVS application to address fundamental software bottlenecks and to exploit the full potential of modern commodity hardware; these efforts also showed orders of magnitude improvement over stock memcached.
 We aim at architecting high-performance and efficient KVS platforms, and start with a rigorous architectural characterization across system stacks over a collection of representative KVS implementations. Our detailed full-system characterization not only identifies the critical hardware/software ingredients for high-performance KVS systems but also leads to guided optimizations atop a recent design to achieve a record-setting throughput of 120 million requests per second (MRPS) (167MRPS with client-side batching) on a single commodity server. Our system delivers the best performance and energy efficiency (RPS/watt) demonstrated to date over existing KVSs including the best-published FPGA-based and GPU-based claims. We craft a set of design principles for future platform architectures, and via detailed simulations demonstrate the capability of achieving a billion RPS with a single server constructed following our principles.
PPP-RTK : Precise Point Positioning Using State-Space Representation in RTK Networks

The concept of precise point positioning (PPP) is currently associated with global networks. Precise orbit and clock solutions are used to enable absolute positioning of a single receiver. However, it is restricted in ambiguity resolution, in convergence time and in accuracy. Precise point positioning based on RTK networks (PPP-RTK) as presented overcomes these limitations and gives centimeter-accuracy in a few seconds. The primary task in RTK networks using the Geo++ GNSMART software is the precise monitoring and representation of all individual GNSS error components using state-space modeling. The advantages of state-space modeling are well known for PPP applications. It is much closer to the physical error sources and can thus better represent the error characteristics. It allows to better separate the various error sources to improve performance and can lead to much less bandwidth for transmission. With RTK networks based on GNSMART it is possible to apply the PPP concept with high accuracy. Ambiguity resolution within the RTK network is mandatory and allows the precise modeling of the system state. Since the integer nature of the carrier phase ambiguities is maintained, all error components can be consistently modeled and give full accuracy in an ambiguity fixing GNSS application. For today's realtime applications, observations of a reference station together with network derived parameters to describe distance dependent errors or a virtual reference station are transmitted to GNSS users in the field using the RTCM standards. This can be termed as representation in observation space (Observation Space Representation: OSR). In contrast to this, also the actual state-space data Presented at the 18th International Technical Meeting, ION GNSS-05, September 13-16, 2005, Long Beach, California. can be used for the representation of the complete GNSS state (State Space Representation: SSR). Hence, precise absolute positioning based on a RTK network (PPP-RTK) using state-space data is a practicable concept. In principle, the concept can be applied to small, regional and global networks. A reference station separation of several 100 km to achieve ambiguity resolution and therefore the key-issue to PPP-RTK is already possible with GNSMART. The complete transition from observation-space to statespace requires the definition of adequate formats and standardized models to provide the state-space data for GNSS application. A single receiver then can position itself with centimeter-accuracy within a few seconds in post-processing and realtime applications. In between, state-space data can still be used to generate data in observation-space, e.g. RTCM or RINEX format, through a conversion algorithm. The state-space concept and pre-requisites are discussed. The benefits of state space representation of GNSS errors and their applications are pointed out.