nils-herrmann/clean_scirepeval huggingface dataset:
The Colletotrichum gloeosporioides species complex

The limit of the Colletotrichum gloeosporioides species complex is defined genetically, based on a strongly supported clade within the Colletotrichum ITS gene tree. All taxa accepted within this clade are morphologically more or less typical of the broadly defined C. gloeosporioides, as it has been applied in the literature for the past 50 years. We accept 22 species plus one subspecies within the C. gloeosporioides complex. These include C. asianum, C. cordylinicola, C. fructicola, C. gloeosporioides, C. horii, C. kahawae subsp. kahawae, C. musae, C. nupharicola, C. psidii, C. siamense, C. theobromicola, C. tropicale, and C. xanthorrhoeae, along with the taxa described here as new, C. aenigma, C. aeschynomenes, C. alatae, C. alienum, C. aotearoa, C. clidemiae, C. kahawae subsp. ciggaro, C. salsolae, and C. ti, plus the nom. nov. C. queenslandicum (for C. gloeosporioides var. minus). All of the taxa are defined genetically on the basis of multi-gene phylogenies. Brief morphological descriptions are provided for species where no modern description is available. Many of the species are unable to be reliably distinguished using ITS, the official barcoding gene for fungi. Particularly problematic are a set of species genetically close to C. musae and another set of species genetically close to C. kahawae, referred to here as the Musae clade and the Kahawae clade, respectively. Each clade contains several species that are phylogenetically well supported in multi-gene analyses, but within the clades branch lengths are short because of the small number of phylogenetically informative characters, and in a few cases individual gene trees are incongruent. Some single genes or combinations of genes, such as glyceraldehyde-3-phosphate dehydrogenase and glutamine synthetase, can be used to reliably distinguish most taxa and will need to be developed as secondary barcodes for species level identification, which is important because many of these fungi are of biosecurity significance. In addition to the accepted species, notes are provided for names where a possible close relationship with C. gloeosporioides sensu lato has been suggested in the recent literature, along with all subspecific taxa and formae speciales within C. gloeosporioides and its putative teleomorph Glomerella cingulata.


TAXONOMIC NOVELTIES
Name replacement - C. queenslandicum B. Weir & P.R. Johnst. New species - C. aenigma B. Weir & P.R. Johnst., C. aeschynomenes B. Weir & P.R. Johnst., C. alatae B. Weir & P.R. Johnst., C. alienum B. Weir & P.R. Johnst, C. aotearoa B. Weir & P.R. Johnst., C. clidemiae B. Weir & P.R. Johnst., C. salsolae B. Weir & P.R. Johnst., C. ti B. Weir & P.R. Johnst. New subspecies - C. kahawae subsp. ciggaro B. Weir & P.R. Johnst.  Epitypification - C. queenslandicum B. Weir & P.R. Johnst.
Deep Learning Lane Detection for Autonomous Vehicle Localization

Autonomous vehicles for consumer use on public roadways are an active area of research and development by various academic and industry groups. One approach to this problem, taken by Google for example, involves creating a 3-D high-resolution map of the desired route, so the vehicle has full awareness of all static features of the route. When autonomously driving this route, the vehicle employs a high precision LIDAR sensor to generate a 3-D point cloud of its surroundings, which is then used to localize the vehicle on the high-resolution map and detect dynamic objects, such as other cars and pedestrians. This approach has proven to be very successful, but it relies on mapping all desired routes before hand, it requires an expensive LIDAR sensor, and performance suffers when the mapped environment changes, due to construction or snow for example. An alternative approach would be to gather all necessary inputs to complete the autonomous driving task on the fly. However, this requires more sensing capability and sensors that are robust to changes in the environment. An approach taken by many auto manufacturers is to limit autonomous driving to highway only, as this provides a structured environment that reduces the sensing requirement. Additionally, vision sensors are preferred over LIDAR due to cost and maintenance. Current, vision systems for vehicles provide lane estimates but performance can degrade due to poor quality lane marks, difficult lighting conditions, and poor road conditions. In this project we use a deep learning based lane detection algorithm to identify lanes from a vehicle mounted vision sensor. This lane information is then used to localize the vehicle onto a lane level map with a particle filter.
Improved Radiation Characteristics of Small Antipodal Vivaldi Antenna for Microwave and Millimeter-Wave Imaging Applications

A small antipodal Vivaldi antenna with rectangular slits at sun-shaped configuration and a half elliptical-shaped dielectric lens is designed for microwave and millimeter-wave imaging applications. The slits are developed to extend low end of frequency band and increase antenna gain at lower frequencies, while the lens is applied to this antenna to feature high gain at higher frequencies, high front-to-back ratio, low sidelobe and cross-polarization levels, narrow half-power beamwidth, and modification on E-plane tilt of beam. A prototype of the antenna with small size of 30 × 55 × 0.508 mm3 is fabricated and employed as a part of microwave and millimeter-wave imaging system. Applicability of the proposed antenna for 3-D and 2-D images of targets such as rubber discs and metal rods at different depths inside construction materials including foam and plasterboard sheet-layered structures is demonstrated. High-range resolution images are achieved by applying synthetic aperture radar algorithm.
A 3-10GHz Broadband CMOS T/R Switch for UWB Applications

A 3-10 GHz broadband CMOS T/R switch for ultra-wideband (UWB) transceiver is presented. The broadband CMOS T/R switch is fabricated based on the 0.18 mu 1P6M standard CMOS process. On-chip measurement of the CMOS T/R switch is performed. The insertion loss of the proposed CMOS T/R Switch is about 3.1plusmn1.3dB. The return losses at both input and output terminals are higher than 14 dB. It is also characterized with 25-34dB isolation and 18-20 dBm input P1dB. The broadband CMOS T/R switch shows highly linear phase and group delay of 20plusmn10 ps from 10MHz to 15GHz. It can be easily integrated with other CMOS RFICs to form on-chip transceivers for various UWB applications
Online Learning of Interpretable Word Embeddings

Word embeddings encode semantic meanings of words into low-dimension word vectors. In most word embeddings, one cannot interpret the meanings of specific dimensions of those word vectors. Nonnegative matrix factorization (NMF) has been proposed to learn interpretable word embeddings via non-negative constraints. However, NMF methods suffer from scale and memory issue because they have to maintain a global matrix for learning. To alleviate this challenge, we propose online learning of interpretable word embeddings from streaming text data. Experiments show that our model consistently outperforms the state-of-the-art word embedding methods in both representation ability and interpretability. The source code of this paper can be obtained from http: //github.com/skTim/OIWE.
A training-based speech regeneration approach with cascading mapping models

Computational speech reconstruction algorithms have the ultimate aim of returning natural sounding speech to aphonic and dysphonic patients as well as those who can only whisper. In particular, individuals who have lost glottis function due to disease or surgery, retain the power of vocal tract modulation to some degree but they are unable to speak anything more than hoarse whispers without prosthetic aid. While whispering can be seen as a natural and secondary aspect of speech communications for most people, it becomes the primary mechanism of communications for those who have impaired voice production mechanisms, such as laryngectomees. In this paper, by considering the current limitations of speech reconstruction methods, a novel algorithm for converting whispers to normal speech is proposed and the efficiency of the algorithm is explored. The algorithm relies upon cascading mapping models and makes use of artificially generated whispers (called whisperised speech) to regenerate natural phonated speech from whispers. Using a training-based approach, the mapping models exploit whisperised speech to overcome frame to frame time alignment problems that are inherent in the speech reconstruction process. This algorithm effectively regenerates missing information in the conventional frameworks of phonated speech reconstruction, ∗Corresponding author Email address: hsharifzadeh@unitec.ac.nz (Hamid R. Sharifzadeh)  2016 and is able to outperform the current state-of-the-art regeneration methods using both subjective and objective criteria.
Visual Analytics for MOOC Data

With the rise of massive open online courses (MOOCs), tens of millions of learners can now enroll in more than 1,000 courses via MOOC platforms such as Coursera and edX. As a result, a huge amount of data has been collected. Compared with traditional education records, the data from MOOCs has much finer granularity and also contains new pieces of information. It is the first time in history that such comprehensive data related to learning behavior has become available for analysis. What roles can visual analytics play in this MOOC movement? The authors survey the current practice and argue that MOOCs provide an opportunity for visualization researchers and that visual analytics systems for MOOCs can benefit a range of end users such as course instructors, education researchers, students, university administrators, and MOOC providers.
Distributed Decision and Control for Cooperative UAVs Using Ad Hoc Communication

This study develops a novel distributed algorithm for task assignment (TA), coordination, and communication of multiple unmanned aerial vehicles (UAVs) engaging multiple targets and conceives an ad hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for distributed flight coordination. Distributed TA is implemented by a relaxation process, wherein each node computes a temporary TA based on the union of the TAs of its neighbors in the tree. The computation of the temporary TAs at each node is based on weighted matching in the UAV-target distances graph. A randomized sampling mechanism is used to propagate TAs among different parts of the tree. Thus, changes in the location of the UAVs and targets do not pass through the root of the tree. Simulation experiments show that the combination of the flocking and the TA algorithms yields the best performance.
A weakest-adversary security metric for network configuration security analysis

A security metric measures or assesses the extent to which a system meets its security objectives. Since meaningful quantitative security metrics are largely unavailable, the security community primarily uses qualitative metrics for security. In this paper, we present a novel quantitative metric for the security of computer networks that is based on an analysis of attack graphs. The metric measures the security strength of a network in terms of the strength of the weakest adversary who can successfully penetrate the network. We present an algorithm that computes the minimal sets of required initial attributes for the weakest adversary to possess in order to successfully compromise a network; given a specific network configuration, set of known exploits, a specific goal state, and an attacker class (represented by a set of all initial attacker attributes). We also demonstrate, by example, that diverse network configurations are not always beneficial for network security in terms of penetrability.
A nonstationary Poisson view of Internet traffic

Since the identification of long-range dependence in network traffic ten years ago, its consistent appearance across numerous measurement studies has largely discredited Poisson-based models. However, since that original data set was collected, both link speeds and the number of Internet-connected hosts have increased by more than three orders of magnitude. Thus, we now revisit the Poisson assumption, by studying a combination of historical traces and new measurements obtained from a major backbone link belonging to a Tier 1 ISP. We show that unlike the older data sets, current network traffic can be well represented by the Poisson model for sub-second time scales. At multisecond scales, we find a distinctive piecewise-linear nonstationarity, together with evidence of long-range dependence. Combining our observations across both time scales leads to a time-dependent Poisson characterization of network traffic that, when viewed across very long time scales, exhibits the observed long-range dependence. This traffic characterization reconciliates the seemingly contradicting observations of Poisson and long-memory traffic characteristics. It also seems to be in general agreement with recent theoretical models for large-scale traffic aggregation
Snafu: Function-as-a-Service (FaaS) Runtime Design and Implementation

Snafu, or Snake Functions, is a modular system to host, execute and manage language-level functions offered as stateless (micro-)services to diverse external triggers. The system interfaces resemble those of commercial FaaS providers but its implementation provides distinct features which make it overall useful to research on FaaS and prototyping of FaaSbased applications. This paper argues about the system motivation in the presence of already existing alternatives, its design and architecture, the open source implementation and collected metrics which characterise the system.
Named Entity Recognition from Diverse Text Types

Current research in Information Extraction tends to be focused on application-specific systems tailored to a particular domain. The Muse system is a multi-purpose Named Entity recognition system which aims to reduce the need for costly and time-consuming adaptation of systems to new applications, with its capability for processing texts from widely differing domains and genres. Although the system is still under development, preliminary results are encouraging, showing little degradation when processing texts of lower quality or of unusual types. The system currently averages 93% precision and 95% recall across a variety of text types.
Subgoal Discovery for Hierarchical Dialogue Policy Learning

Developing agents to engage in complex goaloriented dialogues is challenging partly because the main learning signals are very sparse in long conversations. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given successful example dialogues, we propose the Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion. We then use these subgoals to learn a multi-level policy by hierarchical reinforcement learning. We demonstrate our method by building a dialogue agent for the composite task of travel planning. Experiments with simulated and real users show that our approach performs competitively against a state-of-theart method that requires human-defined subgoals. Moreover, we show that the learned subgoals are often human comprehensible.
Soft Systems Methodology : A Thirty Year

Although the history of thought reveals a number of holistic thinkers — Aristotle, Marx, Husserl among them— it was only in the 1950s that any version of holistic thinking became institutionalized. The kind of holistic thinking which then came to the fore, and was the concern of a newly created organization, was that which makes explicit use of the concept of ‘system’, and today it is ‘systems thinking’ in its various forms which would be taken to be the very paradigm of thinking holistically. In 1954, as recounted in Chapter 3 of Systems Thinking, Systems Practice, only one kind of systems thinking was on the table: the development of a mathematically expressed general theory of systems. It was supposed that this would provide a meta-level language and theory in which the problems of many different disciplines could be expressed and solved; and it was hoped that doing this would help to promote the unity of science. These were the aspirations of the pioneers, but looking back from 1999we can see that the project has not succeeded. The literature contains very little of the kind of outcomes anticipated by the founders of the Society for General Systems Research; and scholars in the many subject areas towhich a holistic approach is relevant have been understandably reluctant to see their pet subject as simply one more example of some broader ‘general system’!
Internet visual media processing: a survey with graphics and vision applications

In recent years, the computer graphics and computer vision communities have devoted significant attention to research based on Internet visual media resources. The huge number of images and videos continually being uploaded by millions of people have stimulated a variety of visual media creation and editing applications, while also posing serious challenges of retrieval, organization, and utilization. This article surveys recent research as regards processing of large collections of images and video, including work on analysis, manipulation, and synthesis. It discusses the problems involved, and suggests possible future directions in this emerging research area.
Performance analysis of support vector machines classifiers in breast cancer mammography recognition

Support vector machine (SVM) is a supervised machine learning approach that was recognized as a statistical learning apotheosis for the small-sample database. SVM has shown its excellent learning and generalization ability and has been extensively employed in many areas. This paper presents a performance analysis of six types of SVMs for the diagnosis of the classical Wisconsin breast cancer problem from a statistical point of view. The classification performance of standard SVM (St-SVM) is analyzed and compared with those of the other modified classifiers such as proximal support vector machine (PSVM) classifiers, Lagrangian support vector machines (LSVM), finite Newton method for Lagrangian support vector machine (NSVM), Linear programming support vector machines (LPSVM), and smooth support vector machine (SSVM). The experimental results reveal that these SVM classifiers achieve very fast, simple, and efficient breast cancer diagnosis. The training results indicated that LSVM has the lowest accuracy of 95.6107 %, while St-SVM performed better than other methods for all performance indices (accuracy = 97.71 %) and is closely followed by LPSVM (accuracy = 97.3282). However, in the validation phase, the overall accuracies of LPSVM achieved 97.1429 %, which was superior to LSVM (95.4286 %), SSVM (96.5714 %), PSVM (96 %), NSVM (96.5714 %), and St-SVM (94.86 %). Value of ROC and MCC for LPSVM achieved 0.9938 and 0.9369, respectively, which outperformed other classifiers. The results strongly suggest that LPSVM can aid in the diagnosis of breast cancer.
On modeling and simulation of game theory-based defense mechanisms against DoS and DDoS attacks

As cyber attacks continue to grow in number, scope, and severity, the cyber security problem has become increasingly important and challenging to both academic researchers and industry practitioners. We explore the applicability of game theoretic approaches to the cyber security problem with focus on active bandwidth depletion attacks. We model the interaction between the attacker and the defender as a two-player non-zero-sum game in two attack scenarios: (i) one single attacking node for Denial of Service (DoS) and (ii) multiple attacking nodes for Distributed DoS (DDoS). The defender's challenge is to determine optimal firewall settings to block rogue traffics while allowing legitimate ones. Our analysis considers the worst-case scenario where the attacker also attempts to find the most effective sending rate or botnet size. In either case, we build both static and dynamic game models to compute the Nash equilibrium that represents the best strategy of the defender. We validate the effectiveness of our game theoretic defense mechanisms via extensive simulation-based experiments using NS-3.
Energy Management for MapReduce Clusters

The area of cluster-level energy management has attracted s ignificant research attention over the past few years. One class of techniques to reduce the energy consumption of clusters is to sel ectively power down nodes during periods of low utilization to increa s energy efficiency. One can think of a number of ways of selective ly powering down nodes, each with varying impact on the workloa d response time and overall energy consumption. Since the Map Reduce framework is becoming “ubiquitous”, the focus of this p aper is on developing a framework for systematically considerin g various MapReduce node power down strategies, and their impact o n the overall energy consumption and workload response time. We closely examine two extreme techniques that can be accommodated in this framework. The first is based on a recently pro posed technique called “Covering Set” (CS) that keeps only a sm ll fraction of the nodes powered up during periods of low utiliz ation. At the other extreme is a technique that we propose in this pap er, called the All-In Strategy (AIS). AIS uses all the nodes in th e cluster to run a workload and then powers down the entire cluster. Using both actual evaluation and analytical modeling we bring out the differences between these two extreme techniques and show t hat AIS is often the right energy saving strategy.
Cost optimized provisioning of elastic resources for application workflows

Workflow technologies have become a major vehicle for easy and efficient development of scientific applications. In the meantime, state-of-the-art resource provisioning technologies such as cloud computing enable users to acquire computing resources dynamically and elastically. A critical challenge in integrating workflow technologies with resource provisioning technologies is to determine the right amount of resources required for the execution of workflows in order to minimize the financial cost from the perspective of users and to maximize the resource utilization from the perspective of resource providers. This paper suggests an architecture for the automatic execution of large-scale workflow-based applications on dynamically and elastically provisioned computing resources. Especially, we focus on its core algorithm named PBTS (Partitioned Balanced Time Scheduling), which estimates the minimum number of computing hosts required to execute a workflow within a user-specified finish time. The PBTS algorithm is designed to fit both elastic resource provisioning models such as Amazon EC2 and malleable parallel application models such as MapReduce. The experimental results with a number of synthetic workflows and several real science workflows demonstrate that PBTS estimates the resource capacity close to the theoretical low bound.