nils-herrmann/clean_scirepeval huggingface dataset:
Distributed and highly-scalable WAN network attack sensing and sophisticated analysing framework based on Honeypot technology

Recently, the increase of interconnectivity has led to a rising amount of IoT enabled devices in botnets. Such botnets are currently used for large scale DDoS attacks. To keep track with these malicious activities, Honeypots have proven to be a vital tool. We developed and set up a distributed and highly-scalable WAN Honeypot with an attached backend infrastructure for sophisticated processing of the gathered data. For the processed data to be understandable we designed a graphical frontend that displays all relevant information that has been obtained from the data. We group attacks originating in a short period of time in one source as sessions. This enriches the data and enables a more in-depth analysis. We produced common statistics like usernames, passwords, username/password combinations, password lengths, originating country and more. From the information gathered, we were able to identify common dictionaries used for brute-force login attacks and other more sophisticated statistics like login attempts per session and attack efficiency.
Software project initiation and planning - an empirical study

This paper describes a study of 14 software companies, on how they initiate and pre-plan software projects. The aim was to obtain an indication of the range of planning activities carried out. The study, using a convenience sample, was carried out using structured interviews, with questions about early software project planning activities. The study offers evidence that an iterative and incremental development process presents extra difficulties in the case of fixedcontract projects. We also found evidence that feasibility studies were common, but generally informal in nature. Documentation of the planning process, especially for project scoping, was variable. For Incremental and Iterative Development projects, an upfront decision on software architecture was shown to be preferred over allowing the architecture to just „emerge‟. There is also evidence that risk management is recognised but often performed incompletely. Finally appropriate future research arising from the study is described.
Stochastic pooling maxout networks for low-resource speech recognition

Maxout network is a powerful alternate to traditional sigmoid neural networks and is showing success in speech recognition. However, maxout network is prone to overfitting thus regularization methods such as dropout are often needed. In this paper, a stochastic pooling regularization method for max-out networks is proposed to control overfitting. In stochastic pooling, a distribution is produced for each pooling region by the softmax normalization of the piece values. The active piece is selected based on the distribution during training, and an effective probability weighting is conducted during testing. We apply the stochastic pooling maxout (SPM) networks within the DNN-HMM framework and evaluate its effectiveness under a low-resource speech recognition condition. On benchmark test sets, the SPM network yields 4.7-8.6% relative improvements over the baseline maxout network. Further evaluations show the superiority of stochastic pooling over dropout for low-resource speech recognition.
Communication Optimization of Iterative Sparse Matrix-Vector Multiply on GPUs and FPGAs

Trading communication with redundant computation can increase the silicon efficiency of FPGAs and GPUs in accelerating communication-bound sparse iterative solvers. While k iterations of the iterative solver can be unrolled to provide O(k) reduction in communication cost, the extent of this unrolling depends on the underlying architecture, its memory model, and the growth in redundant computation. This paper presents a systematic procedure to select this algorithmic parameter k, which provides communication-computation tradeoff on hardware accelerators like FPGA and GPU. We provide predictive models to understand this tradeoff and show how careful selection of k can lead to performance improvement that otherwise demands significant increase in memory bandwidth. On an Nvidia C2050 GPU, we demonstrate a 1.9×-42.6× speedup over standard iterative solvers for a range of benchmarks and that this speedup is limited by the growth in redundant computation. In contrast, for FPGAs, we present an architecture-aware algorithm that limits off-chip communication but allows communication between the processing cores. This reduces redundant computation and allows large k and hence higher speedups. Our approach for FPGA provides a 0.3×-4.4× speedup over same-generation GPU devices where k is picked carefully for both architectures for a range of benchmarks.
Maximal Sparsity with Deep Networks?

The iterations of many sparse estimation algorithms are comprised of a fixed linear filter cascaded with a thresholding nonlinearity, which collectively resemble a typical neural network layer. Consequently, a lengthy sequence of algorithm iterations can be viewed as a deep network with shared, hand-crafted layer weights. It is therefore quite natural to examine the degree to which a learned network model might act as a viable surrogate for traditional sparse estimation in domains where ample training data is available. While the possibility of a reduced computational budget is readily apparent when a ceiling is imposed on the number of layers, our work primarily focuses on estimation accuracy. In particular, it is well-known that when a signal dictionary has coherent columns, as quantified by a large RIP constant, then most tractable iterative algorithms are unable to find maximally sparse representations. In contrast, we demonstrate both theoretically and empirically the potential for a trained deep network to recover minimal `0-norm representations in regimes where existing methods fail. The resulting system is deployed on a practical photometric stereo estimation problem, where the goal is to remove sparse outliers that can disrupt the estimation of surface normals from a 3D scene.
Corpus domain effects on distributional semantic modeling of medical terms

Automatically quantifying semantic similarity and relatedness between clinical terms is an important aspect of text mining from electronic health records, which are increasingly recognized as valuable sources of phenotypic information for clinical genomics and bioinformatics research. A key obstacle to development of semantic relatedness measures is the limited availability of large quantities of clinical text to researchers and developers outside of major medical centers. Text from general English and biomedical literature are freely available; however, their validity as a substitute for clinical domain to represent semantics of clinical terms remains to be demonstrated.



We constructed neural network representations of clinical terms found in a publicly available benchmark dataset manually labeled for semantic similarity and relatedness. Similarity and relatedness measures computed from text corpora in three domains (Clinical Notes, PubMed Central articles and Wikipedia) were compared using the benchmark as reference. We found that measures computed from full text of biomedical articles in PubMed Central repository (rho = 0.62 for similarity and 0.58 for relatedness) are on par with measures computed from clinical reports (rho = 0.60 for similarity and 0.57 for relatedness). We also evaluated the use of neural network based relatedness measures for query expansion in a clinical document retrieval task and a biomedical term word sense disambiguation task. We found that, with some limitations, biomedical articles may be used in lieu of clinical reports to represent the semantics of clinical terms and that distributional semantic methods are useful for clinical and biomedical natural language processing applications.



The software and reference standards used in this study to evaluate semantic similarity and relatedness measures are publicly available as detailed in the article.



pakh0002@umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Enhanced perceptual functioning in autism: an update, and eight principles of autistic perception.

We propose an "Enhanced Perceptual Functioning" model encompassing the main differences between autistic and non-autistic social and non-social perceptual processing: locally oriented visual and auditory perception, enhanced low-level discrimination, use of a more posterior network in "complex" visual tasks, enhanced perception of first order static stimuli, diminished perception of complex movement, autonomy of low-level information processing toward higher-order operations, and differential relation between perception and general intelligence. Increased perceptual expertise may be implicated in the choice of special ability in savant autistics, and in the variability of apparent presentations within PDD (autism with and without typical speech, Asperger syndrome) in non-savant autistics. The overfunctioning of brain regions typically involved in primary perceptual functions may explain the autistic perceptual endophenotype.
Spacing effects in learning: a temporal ridgeline of optimal retention.

To achieve enduring retention, people must usually study information on multiple occasions. How does the timing of study events affect retention? Prior research has examined this issue only in a spotty fashion, usually with very short time intervals. In a study aimed at characterizing spacing effects over significant durations, more than 1,350 individuals were taught a set of facts and--after a gap of up to 3.5 months--given a review. A final test was administered at a further delay of up to 1 year. At any given test delay, an increase in the interstudy gap at first increased, and then gradually reduced, final test performance. The optimal gap increased as test delay increased. However, when measured as a proportion of test delay, the optimal gap declined from about 20 to 40% of a 1-week test delay to about 5 to 10% of a 1-year test delay. The interaction of gap and test delay implies that many educational practices are highly inefficient.
Apparel classification using Convolutional Neural Networks

Apparel classification encompasses the identification of an outfit in an image. The area has its applications in social media advertising, e-commerce and criminal law. In our work, we introduce a new method for shopping apparels online. This paper describes our approach to classify images using Convolutional Neural Networks. We concentrate mainly on two aspects of apparel classification: (1) Multiclass classification of apparel type and (2) Similar Apparel retrieval based on the query image. This shopping technique relieves the burden of storing a lot of information related to the images and traditional ways of filtering search results can be replaced by image filters
Pylearn2: a machine learning research library

Pylearn2 is a machine learning research library. This does n t just mean that it is a collection of machine learning algorithms that share a comm n API; it means that it has been designed for flexibility and extensibility in ord e to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summar y of the library’s architecture, and a description of how the Pylearn2 communi ty functions socially.
A multichannel CSMA MAC protocol with receiver-based channel selection for multihop wireless networks

In this paper, we propose a CSMA-based medium access control protocol for multihop wireless networks that uses multiple channels and a dynamic channel selection method. The proposed protocol uses one control channel and N data channels, where N is independent of the number of nodes in the network. The source uses an exchange of control packets on the control channel to decide on the best channel to send the data packet on. Channel selection is based on maximizing the signal-to-interference plus noise ratio at the receiver. We present performance evaluations obtained from simulations that demonstrate the effectiveness of the proposed protocol.
Topographic anatomy of the superior labial artery for dermal filler injection.

The superior labial artery, which is a branch of the facial artery, supplies the upper lip area. The aim of this study was to determine the distribution pattern of the superior labial artery and provide precise topographic information of the artery for dermal filler injection.



Sixty hemifaces from 18 Korean and 18 Thai cadavers were used for this study. The various distribution patterns of the superior labial artery were classified according to its relationship with the facial artery.



The course of the superior labial artery was classified into four types: type I (56.7 percent), in which the artery and the alar branch both arise directly and independently from the facial artery; type II (21.7 percent), in which the superior labial artery branches off from the facial artery and then gives off an alar branch; type III (15.0 percent), in which it is the terminal branch of the facial artery; and type IV (6.7 percent), in which the artery is absent. The origin of the superior labial artery was located 12.1 ± 3.1 mm (mean ± SD) lateral and at a variable angle of 42.8 ± 26.9 degrees relative to the mouth corner.



The superior labial artery proceeded from the origin of the artery located within a 1.5-cm-side square superolateral to the mouth corner as running along the vermilion border of the upper lip to the facial sagittal midline at a depth of 3 mm. Thus, clinicians should be careful when injecting dermal filler into this area.
Bayesian network structures and inference techniques for automatic speech recognition

This paper describes the theory and implementation of Bayesian networks in the context of automatic speech recognition. Bayesian networks provide a succinct and expressive graphical language for factoring joint probability distributions, and we begin by presenting the structures that are appropriate for doing speech recognition training and decoding. This approach is notable because it expresses all the details of a speech recognition system in a uniform way using only the concepts of random variables and conditional probabilities. A powerful set of computational routines complements the representational utility of Bayesian networks, and the second part of this paper describes these algorithms in detail. We present a novel view of inference in general networks – where inference is done via a change-of-variables that renders the network tree-structured and amenable to a very simple form of inference. We present the technique in terms of straightforward dynamic programming recursions analogous to HMM a–b computation, and then extend it to handle deterministic constraints amongst variables in an extremely efficient manner. The paper concludes with a sequence of experimental results that show the range of effects that can be modeled, and that significant reductions in error-rate can be expected from intelligently factored state representations.
3D Face Reconstruction from a Single Image Using a Single Reference Face Shape

Human faces are remarkably similar in global properties, including size, aspect ratio, and location of main features, but can vary considerably in details across individuals, gender, race, or due to facial expression. We propose a novel method for 3D shape recovery of faces that exploits the similarity of faces. Our method obtains as input a single image and uses a mere single 3D reference model of a different person's face. Classical reconstruction methods from single images, i.e., shape-from-shading, require knowledge of the reflectance properties and lighting as well as depth values for boundary conditions. Recent methods circumvent these requirements by representing input faces as combinations (of hundreds) of stored 3D models. We propose instead to use the input image as a guide to "mold” a single reference model to reach a reconstruction of the sought 3D shape. Our method assumes Lambertian reflectance and uses harmonic representations of lighting. It has been tested on images taken under controlled viewing conditions as well as on uncontrolled images downloaded from the Internet, demonstrating its accuracy and robustness under a variety of imaging conditions and overcoming significant differences in shape between the input and reference individuals including differences in facial expressions, gender, and race.
An Integrated Control Algorithm for a Single-Stage PV Pumping System Using an Open-End Winding Induction Motor

In this paper, a single-stage solution for solar photovoltaic (PV) pumping system using a dual-inverter fed open-end winding induction motor drive is presented. The three-level dual-inverter requires a low PV bus voltage compared with its conventional three-level counterpart. This could avoid large string of PV modules and helps in reducing the voltage rating of the capacitors and semiconductor devices used in the system. This may further help in reduction of cost of the system. The proposed single-stage system is operated using an integrated control algorithm, which includes the maximum power point tracking (MPPT), the V/f control, and the sample-averaged zero-sequence elimination (SAZE) pulsewidth modulation (PWM) technique. While the MPPT algorithm ensures the extraction of maximum power from the PV source, the V/f control improves the motor pump performance. Furthermore, the zero-sequence current is avoided by the SAZE PWM algorithm. Thus, the integrated control algorithm improves the overall performance of the system. Furthermore, this paper also presents the details of system design and analysis of its dynamic behavior during transient environmental conditions. The performance of the system is verified using MATLAB simulation and hardware prototype.
fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python

The fastcluster package is a C++ library for hierarchical, agglomerative clustering. It provides a fast implementation of the most efficient, current algorithms when the input is a dissimilarity index. Moreover, it features memory-saving routines for hierarchical clustering of vector data. It improves both asymptotic time complexity (in most cases) and practical performance (in all cases) compared to the existing implementations in standard software: several R packages, MATLAB, Mathematica, Python with SciPy. The fastcluster package presently has interfaces to R and Python. Part of the functionality is designed as a drop-in replacement for the methods hclust and flashClust in R and scipy.cluster.hierarchy.linkage in Python, so that existing programs can be effortlessly adapted for improved performance.
Control of a Single-Phase Cascaded H-Bridge Multilevel Inverter for Grid-Connected Photovoltaic Systems

This paper presents a single-phase cascaded H-bridge converter for a grid-connected photovoltaic (PV) application. The multilevel topology consists of several H-bridge cells connected in series, each one connected to a string of PV modules. The adopted control scheme permits the independent control of each dc-link voltage, enabling, in this way, the tracking of the maximum power point for each string of PV panels. Additionally, low-ripple sinusoidal-current waveforms are generated with almost unity power factor. The topology offers other advantages such as the operation at lower switching frequency or lower current ripple compared to standard two-level topologies. Simulation and experimental results are presented for different operating conditions.
Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks

Sprout is an end-to-end transport protocol for interactive applications that desire high throughput and low delay. Sprout works well over cellular wireless networks, where link speeds change dramatically with time, and current protocols build up multi-second queues in network gateways. Sprout does not use TCP-style reactive congestion control; instead the receiver observes the packet arrival times to infer the uncertain dynamics of the network path. This inference is used to forecast how many bytes may be sent by the sender, while bounding the risk that packets will be delayed inside the network for too long. In evaluations on traces from four commercial LTE and 3G networks, Sprout, compared with Skype, reduced self-inflicted end-to-end delay by a factor of 7.9 and achieved 2.2× the transmitted bit rate on average. Compared with Google’s Hangout, Sprout reduced delay by a factor of 7.2 while achieving 4.4× the bit rate, and compared with Apple’s Facetime, Sprout reduced delay by a factor of 8.7 with 1.9× the bit rate. Although it is end-to-end, Sprout matched or outperformed TCP Cubic running over the CoDel active queue management algorithm, which requires changes to cellular carrier equipment to deploy. We also tested Sprout as a tunnel to carry competing interactive and bulk traffic (Skype and TCP Cubic), and found that Sprout was able to isolate client application flows from one another.
Rehabilitation of hemiparesis after stroke with a mirror

With an ageing population in an industrialised world, the global burden of stroke is staggering millions of strokes a year. Hemiparesis is one of the most.Lancet. Rehabilitation of hemiparesis after stroke with a mirror. Altschuler EL, Wisdom SB, Stone L, Foster C, Galasko D.Rehabilitation of the severely affected paretic arm after stroke represents a major challenge, especially in the presence of sensory impairment. Objective.in patients after stroke. This article reviews the evidence for motor imagery or.
A framework for comparing models of computation

We give a denotational framework (a “meta model”) within which certain properties of models of computation can be compared. It describes concurrent processes in general terms as sets of possible behaviors. A process is determinate if, given the constraints imposed by the inputs, there are exactly one or exactly zero behaviors. Compositions of processes are processes with behaviors in the intersection of the behaviors of the component processes. The interaction between processes is through signals, which are collections of events. Each event is a value-tag pair, where the tags can come from a partially ordered or totally ordered set. Timed models are where the set of tags is totally ordered. Synchronous events share the same tag, and synchronous signals contain events with the same set of tags. Synchronous processes have only synchronous signals as behaviors. Strict causality (in timed tag systems) and continuity (in untimed tag systems) ensure determinacy under certain technical conditions. The framework is used to compare certain essential features of various models of computation, including Kahn process networks, dataflow, sequential processes, concurrent sequential processes with rendezvous, Petri nets, and discrete-event systems.