nils-herrmann/clean_scirepeval huggingface dataset:
Incremental Learning Through Deep Adaptation

Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned. Existing approaches either learn sub-optimal solutions, require joint training, or incur a substantial increment in the number of parameters for each added domain, typically as many as the original network. We propose a method called Deep Adaptation Modules (DAM) that constrains newly learned filters to be linear combinations of existing ones. DAMs precisely preserve performance on the original domain, require a fraction (typically 13%, dependent on network architecture) of the number of parameters compared to standard fine-tuning procedures and converge in less cycles of training to a comparable or better level of performance. When coupled with standard network quantization techniques, we further reduce the parameter cost to around 3% of the original with negligible or no loss in accuracy. The learned architecture can be controlled to switch between various learned representations, enabling a single network to solve a task from multiple different domains. We conduct extensive experiments showing the effectiveness of our method on a range of image classification tasks and explore different aspects of its behavior.
Performance Analysis of RIP, OSPF, IGRP and EIGRP Routing Protocols in a Network

In a network topology for forwarding packets various routing protocols are being used. Routers maintain a routing table for successful delivery of the packets from the source node to the correct destined node. The extent of information stored by a router about the network depends on the algorithm it follows. Most of the popular routing algorithms used are RIP, OSPF, IGRP and EIGRP. Here in this paper we are analyzing the performance of these very algorithms on the basis of the cost of delivery, amount of overhead on each router, number of updates needed, failure recovery, delay encountered and resultant throughput of the system. We are trying to find out which protocol suits the best for the network and through a thorough analysis we have tried to find the pros and cons of each protocol.
Entity linking for biomedical literature

The Entity Linking (EL) task links entity mentions from an unstructured document to entities in a knowledge base. Although this problem is well-studied in news and social media, this problem has not received much attention in the life science domain. One outcome of tackling the EL problem in the life sciences domain is to enable scientists to build computational models of biological processes with more efficiency. However, simply applying a news-trained entity linker produces inadequate results.



Since existing supervised approaches require a large amount of manually-labeled training data, which is currently unavailable for the life science domain, we propose a novel unsupervised collective inference approach to link entities from unstructured full texts of biomedical literature to 300 ontologies. The approach leverages the rich semantic information and structures in ontologies for similarity computation and entity ranking.



Without using any manual annotation, our approach significantly outperforms state-of-the-art supervised EL method (9% absolute gain in linking accuracy). Furthermore, the state-of-the-art supervised EL method requires 15,000 manually annotated entity mentions for training. These promising results establish a benchmark for the EL task in the life science domain. We also provide in depth analysis and discussion on both challenges and opportunities on automatic knowledge enrichment for scientific literature.



In this paper, we propose a novel unsupervised collective inference approach to address the EL problem in a new domain. We show that our unsupervised approach is able to outperform a current state-of-the-art supervised approach that has been trained with a large amount of manually labeled data. Life science presents an underrepresented domain for applying EL techniques. By providing a small benchmark data set and identifying opportunities, we hope to stimulate discussions across natural language processing and bioinformatics and motivate others to develop techniques for this largely untapped domain.
Image Compression: Sparse Coding vs. Bottleneck Autoencoders

Bottleneck autoencoders have been actively researched as a solution to image compression tasks. However, we observed that bottleneck autoencoders produce subjectively low quality reconstructed images. In this work, we explore the ability of sparse coding to improve reconstructed image quality for the same degree of compression. We observe that sparse image compression produces visually superior reconstructed images and yields higher values of pixel-wise measures of reconstruction quality (PSNR and SSIM) compared to bottleneck autoencoders. In addition, we find that using alternative metrics that correlate better with human perception, such as feature perceptual loss and the classification accuracy, sparse image compression scores up to 18.06% and 2.7% higher, respectively, compared to bottleneck autoencoders. Although computationally much more intensive, we find that sparse coding is otherwise superior to bottleneck autoencoders for the same degree of compression.
From structure from motion to historical building information modeling: populating a semantic-aware library of architectural elements

In recent years, we have witnessed a huge diffusion of building information modeling (BIM) approaches in the field of architectural design, although very little research has been undertaken to explore the value, criticalities, and advantages attributable to the application of these methodologies in the cultural heritage domain. Furthermore, the last developments in digital photogrammetry lead to the easy generation of reliable low-cost three-dimensional textured models that could be used in BIM platforms to create semanticaware objects that could compose a specific library of historical architectural elements. In this case, the transfer between the point cloud and its corresponding parametric model is not so trivial and the level of geometrical abstraction could not be suitable with the scope of the BIM. The aim of this paper is to explore and retrace the milestone works on this crucial topic in order to identify the unsolved issues and to propose and test a unique and simple workflow practitioner centered and based on the use of the latest available solutions for point cloud managing into commercial BIM platforms.
Author name disambiguation: What difference does it make in author-based citation analysis?

In this paper, we explore how strongly author name disambiguation (AND) affects the results of an author-based citation analysis study, and identify conditions under which the commonly used simplified approach of using surnames and first initials may suffice in practice. We compare author citation ranking and co-citation mapping results in the stem cell research field 2004-2009 between two AND approaches: the traditional simplified approach of using author surnames and first initials, and a sophisticated algorithmic approach. We find that the traditional approach leads to extremely distorted rankings and substantially distorted mappings of authors in this field when based on firstor all-author citation counting, whereas last-author based citation ranking and co-citation mapping both appear relatively immune to the author name ambiguity problem. This is largely because romanized names of Chinese and Korean authors, who are very active in this field, are extremely ambiguous, but few of these researchers consistently publish as last authors in by-lines. We conclude that more earnest effort is required to deal with the author name ambiguity problem in both citation analysis and information retrieval, especially given the current trend towards globalization. In the stem cell field, where lab heads are traditionally listed as last authors in by-lines, last-author based citation ranking and co-citation mapping using the traditional simple approach to author name disambiguation may serve as a simple workaround, but likely at the price of largely filtering out Chinese and Korean contributions to the field as well as important contributions by young researchers.
Satellite Image (Multispectral) Enhancement Techniques in Wavelet Domain: An Overview

Satellite images are used in many applications like military, forecasting, astronomy, and geographical information. Satellite images have to face spatial and spectral resolution problems due to scattering, atmospheric conditions, etc. , also they have poor perception. This limitation of resolution needs to be overcome before further processing. The goal of image resolution enhancement is to improve specific features of an image for its correct representation. Better resolution is obtained by applying enhancement techniques on blurred and noisy images. In this paper various image enhancement techniques under wavelet domain like WZP, DWT, and DT-CWT are discussed. This paper also gives selection of suitable enhancement technique
A survey of wireless sensor network security in the context of Internet of Things

Today, the common vision of smart components is usually based on the concept of the Internet of Things (IoT). Intelligent infrastructures combine sensor networks, network connectivity and software to oversee and analyze complex systems to identify inefficiencies and inform operational decision-making. Wireless Sensor nodes collect operational information over time and provide real-time data on current conditions such as (volcano activities, disaster parameters in general). The security of wireless sensor networks in the world of the Internet of Things is a big challenge, since there are several types of attacks against different layers of OSI model, in their goal is falsified the values of the detected parameters.
Geology of the Area Between the Sustut Copper Deposit and the Day Porphyry Copper Prospect

The region was covered by 1:250 000 mapping by the Geological Survey of Canada during the mid 1940s (Lord, 1948). A number of showings were found. One of these, the Marmot, was the focus of the first modern exploration (1960s) in the general area. At the same time there was significant exploration activity for porphyry copper and molybdenum mineralization in the intrusive belt running north and south through the McConnell Range. A large gossan was discovered in 1966 at the present site of the Kemess North prospect and led to similar exploration on nearby ground. Falconbridge Nickel Ltd., during a reconnaissance helicopter flight in 1971, discovered a malachite-stained bed in the Sustut drainage that was traceable for over 2500 feet. Their assessment suggested a replacement copper deposi t hosted by volcaniclastic rocks in the upper part of the Takla Group. Numerous junior and major resource companies acquired ground in the area. In 1972 copper was found on the Willow cliffs on the opposite side of the Sustut River and a porphyry style target was identified at the Day. In 1973 the B.C. Geological Survey conducted a mineral deposit study of the Sustut copper area (Church, 1974a). The Geological Survey of Canada returned to pursue general and detailed studies within the McConnell sheet (Richards 1976, and Monger 1977). Monger and Church (1976) revised the stratigraphic nomenclature based on breaks and lithological changes in the volcanic succession supported by fossil data and field observations. In 1983, follow up of a gold-copper-molybdenum soil anomaly led to the discovery of the Kemess South porphyry deposit.
Photon-trapping microstructures enable high-speed high-ef fi ciency silicon photodiodes

High-speed, high-efficiency photodetectors play an important role in optical communication links that are increasingly being used in data centres to handle higher volumes of data traffic and higher bandwidths, as big data and cloud computing continue to grow exponentially. Monolithic integration of optical components with signal-processing electronics on a single silicon chip is of paramount importance in the drive to reduce cost and improve performance. We report the first demonstration of microand nanoscale holes enabling light trapping in a silicon photodiode, which exhibits an ultrafast impulse response (full-width at half-maximum) of 30 ps and a high efficiency of more than 50%, for use in data-centre optical communications. The photodiode uses microand nanostructured holes to enhance, by an order of magnitude, the absorption efficiency of a thin intrinsic layer of less than 2 μm thickness and is designed for a data rate of 20 gigabits per second or higher at a wavelength of 850 nm. Further optimization can improve the efficiency to more than 70%.
A Retrieval-Based Dialogue System Utilizing Utterance and Context Embeddings

Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the distributed vector representation (embedding) of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system.
The Anti-CRISPR Story: A Battle for Survival.

The last decade has seen the fields of molecular biology and genetics transformed by the development of CRISPR-based gene editing technologies. These technologies were derived from bacterial defense systems that protect against viral invasion. Elegant studies focused on the evolutionary battle between CRISPR-encoding bacteria and the viruses that infect and kill them revealed the next step in this arms race, the anti-CRISPR proteins. Investigation of these proteins has provided important new insight into how CRISPR-Cas systems work and how bacterial genomes evolve. They have also led to the development of important biotechnological tools that can be used for genetic engineering, including off switches for CRISPR-Cas9 genome editing in human cells.
fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs

Convolutional Neural Networks (ConvNets) are a powerful Deep Learning model, providing state-of-the-art accuracy to many emerging classification problems. However, ConvNet classification is a computationally heavy task, suffering from rapid complexity scaling. This paper presents fpgaConvNet, a novel domain-specific modelling framework together with an automated design methodology for the mapping of ConvNets onto reconfigurable FPGA-based platforms. By interpreting ConvNet classification as a streaming application, the proposed framework employs the Synchronous Dataflow (SDF) model of computation as its basis and proposes a set of transformations on the SDF graph that explore the performance-resource design space, while taking into account platform-specific resource constraints. A comparison with existing ConvNet FPGA works shows that the proposed fully-automated methodology yields hardware designs that improve the performance density by up to 1.62× and reach up to 90.75% of the raw performance of architectures that are hand-tuned for particular ConvNets.
A scientometric analysis of knowledge management and intellectual capital academic literature (1994-2008)

The purpose of this study is to conduct a scientometric analysis of the body of literature contained in 11 major knowledge management and intellectual capital (KM/IC) peer-reviewed journals.  A total of 2,175 articles published in 11 major KM/IC peer-reviewed journals were carefully reviewed and subjected to scientometric data analysis techniques.  A number of research questions pertaining to country, institutional and individual productivity, co-operation patterns, publication frequency, and favourite inquiry methods were proposed and answered. Based on the findings, many implications emerged that improve one’s understanding of the identity of KM/IC as a distinct scientific field.  The pool of KM/IC journals examined did not represent all available publication outlets, given that at least 20 peer-reviewed journals exist in the KM/IC field. There are also KM/IC papers published in other non-KM/IC specific journals. However, the 11 journals that were selected for the study have been evaluated by Bontis and Serenko as the top publications in the KM/IC area.  Practitioners have played a significant role in developing the KM/IC field. However, their contributions have been decreasing. There is still very much a need for qualitative descriptions and case studies. It is critically important that practitioners consider collaborating with academics for richer research projects.  This is the most comprehensive scientometric analysis of the KM/IC field ever conducted.
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

We introduce extreme summarization, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question “What is the article about?”. We collect a real-world, large scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures longrange dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by
An enhanced computational feature selection method for medical synonym identification via bilingualism and multi-corpus training

Medical synonym identification has been an important part of medical natural language processing (NLP). However, in the field of Chinese medical synonym identification, there are problems like low precision and low recall rate. To solve the problem, in this paper, we propose a method for identifying Chinese medical synonyms. We first selected 13 features including Chinese and English features. Then we studied the synonym identification results of each feature alone and different combinations of the features. Through the comparison among identification results, we present an optimal combination of features for Chinese medical synonym identification. Experiments show that our selected features have achieved 97.37% precision rate, 96.00% recall rate and 97.33% F1 score.
Graph Summarization Methods and Applications: A Survey

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind and the challenges of graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.
MacroBase: Prioritizing Attention in Fast Data

As data volumes continue to rise, manual inspection is becoming increasingly untenable. In response, we present MacroBase, a data analytics engine that prioritizes end-user attention in high-volume fast data streams. MacroBase enables efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. MacroBase is able to deliver order-of-magnitude speedups over alternatives by optimizing the combination of explanation and classification tasks and by leveraging a new reservoir sampler and heavy-hitters sketch specialized for fast data streams. As a result, MacroBase delivers accurate results at speeds of up to 2M events per second per query on a single core. The system has delivered meaningful results in production, including at a telematics company monitoring hundreds of thousands of vehicles.
Working memory benefits creative insight, musical improvisation, and original ideation through maintained task-focused attention.

Anecdotes from creative eminences suggest that executive control plays an important role in creativity, but scientific evidence is sparse. Invoking the Dual Pathway to Creativity Model, the authors hypothesize that working memory capacity (WMC) relates to creative performance because it enables persistent, focused, and systematic combining of elements and possibilities (persistence). Study 1 indeed showed that under cognitive load, participants performed worse on a creative insight task. Study 2 revealed positive associations between time-on-task and creativity among individuals high but not low in WMC, even after controlling for general intelligence. Study 3 revealed that across trials, semiprofessional cellists performed increasingly more creative improvisations when they had high rather than low WMC. Study 4 showed that WMC predicts original ideation because it allows persistent (rather than flexible) processing. The authors conclude that WMC benefits creativity because it enables the individual to maintain attention focused on the task and prevents undesirable mind wandering.
Retrieving Collocations from Text: Xtract

Natural languages are full of collocations, recurrent combinations of words that co-occur more often than expected by chance and that correspond to arbitrary word usages. Recent work in lexicography indicates that collocations are pervasive in English; apparently, they are common in all types of writing, including both technical and nontechnical genres. Several approaches have been proposed to retrieve various types of collocations from the analysis of large samples of textual data. These techniques automatically produce large numbers of collocations along with statistical figures intended to reflect the relevance of the associations. However, noue of these techniques provides functional information along with the collocation. Also, the results produced often contained improper word associations reflecting some spurious aspect of the training corpus that did not stand for true collocations. In this paper, we describe a set of techniques based on statistical methods for retrieving and identifying collocations from large textual corpora. These techniques produce a wide range of collocations and are based on some original filtering methods that allow the production of richer and higher-precision output. These techniques have been implemented and resulted in a lexicographic tool, Xtract. The techniques are described and some results are presented on a 10 million-word corpus of stock market news reports. A lexicographic evaluation of Xtract as a collocation retrieval tool has been made, and the estimated precision of Xtract is 80%.