nils-herrmann/clean_scirepeval huggingface dataset:
Artificial neural networks improve the accuracy of cancer survival prediction.

The TNM staging system originated as a response to the need for an accurate, consistent, universal cancer outcome prediction system. Since the TNM staging system was introduced in the 1950s, new prognostic factors have been identified and new methods for integrating prognostic factors have been developed. This study compares the prediction accuracy of the TNM staging system with that of artificial neural network statistical models.



For 5-year survival of patients with breast or colorectal carcinoma, the authors compared the TNM staging system's predictive accuracy with that of artificial neural networks (ANN). The area under the receiver operating characteristic curve, as applied to an independent validation data set, was the measure of accuracy.



For the American College of Surgeons' Patient Care Evaluation (PCE) data set, using only the TNM variables (tumor size, number of positive regional lymph nodes, and distant metastasis), the artificial neural network's predictions of the 5-year survival of patients with breast carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.720; ANN, 0.770; P < 0.001). For the National Cancer Institute's Surveillance, Epidemiology, and End Results breast carcinoma data set, using only the TNM variables, the artificial neural network's predictions of 10-year survival were significantly more accurate than those of the TNM staging system (TNM, 0.692; ANN, 0.730; P < 0.01). For the PCE colorectal data set, using only the TNM variables, the artificial neural network's predictions of the 5-year survival of patients with colorectal carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.737; ANN, 0.815; P < 0.001). Adding commonly collected demographic and anatomic variables to the TNM variables further increased the accuracy of the artificial neural network's predictions of breast carcinoma survival (0.784) and colorectal carcinoma survival (0.869).



Artificial neural networks are significantly more accurate than the TNM staging system when both use the TNM prognostic factors alone. New prognostic factors can be added to artificial neural networks to increase prognostic accuracy further. These results are robust across different data sets and cancer sites.
Firefly Algorithm for Polynomial Bézier Surface Parameterization

A classical issue in many applied fields is to obtain an approximating surface to a given set of data points. This problem arises in Computer-Aided Design and Manufacturing (CAD/CAM), virtual reality, medical imaging, computer graphics, computer animation, and many others. Very often, the preferred approximating surface is polynomial, usually described in parametric form. This leads to the problem of determining suitable parametric values for the data points, the so-called surface parameterization. In real-world settings, data points are generally irregularly sampled and subjected to measurement noise, leading to a very difficult nonlinear continuous optimization problem, unsolvable with standard optimization techniques. This paper solves the parameterization problem for polynomial Bézier surfaces by applying the firefly algorithm, a powerful nature-inspired metaheuristic algorithm introduced recently to address difficult optimization problems. The method has been successfully applied to some illustrative examples of open and closed surfaces, including shapes with singularities. Our results show that the method performs very well, being able to yield the best approximating surface with a high degree of accuracy.
Slew-rate and gain enhancement in two stage operational amplifiers

A two stage op-amp with an effective technique to enhance slew-rate and gain is presented. The enhancement is provided by an auxiliary monitor circuit which is activated in slewing conditions, but can contribute to the gain in normal conditions. The amplifier, simulated in a 0.18 µm technology, achieves 74 dB DC gain, 160 MHz bandwidth and 26.8 V/µs slew-rate for a load capacitance of 1.75 pF with 362 µW power consumption, considering a supply voltage of 1.8 V.
bigNN: An open-source big data toolkit focused on biomedical sentence classification

Every single day, a massive amount of text data is generated by different medical data sources, such as scientific literature, medical web pages, health-related social media, clinical notes, and drug reviews. Processing this wealth of data is indeed a daunting task, and it forces us to adopt smart and scalable computational strategies, including machine intelligence, big data analytics, and distributed architecture. In this contribution, we designed and developed an open-source big data neural network toolkit, namely bigNN which tackles the problem of large-scale biomedical text classification in an efficient fashion, facilitating fast prototyping and reproducible text analytics researches. bigNN scales up a word2vec-based neural network model over Apache Spark 2.10 and Hadoop Distributed File System (HDFS) 2.7.3, allowing for more efficient big data sentence classification. The toolkit supports big data computing, and simplifies rapid application development in sentence analysis by allowing users to configure and examine different internal parameters of both Apache Spark and the neural network model. bigNN is fully documented, and it is publicly and freely available at https://github.com/bircatmcri/bigNN.
DropAll: Generalization of Two Convolutional Neural Network Regularization Methods

We introduce DropAll, a generalization of DropOut  and DropConnect , for regularization of fully-connected layers within convolutional neural networks. Applying these methods amounts to subsampling a neural network by dropping units. Training with DropOut, a randomly selected subset of activations are dropped, when training with DropConnect we drop a randomly subsets of weights. With DropAll we can perform both methods. We show the validity of our proposal by improving the classification error of networks trained with DropOut and DropConnect, on a common image classification dataset. To improve the classification, we also used a new method for combining networks, which was proposed in .
CrowdRecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint

This paper proposes a novel participant selection framework, named CrowdRecruiter, for mobile crowdsensing. CrowdRecruiter operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model and minimizes incentive payments by selecting a small number of participants while still satisfying probabilistic coverage constraint. In order to achieve the objective when piggybacking crowdsensing tasks with phone calls, CrowdRecruiter first predicts the call and coverage probability of each mobile user based on historical records. It then efficiently computes the joint coverage probability of multiple users as a combined set and selects the near-minimal set of participants, which meets coverage ratio requirement in each sensing cycle of the PCS task. We evaluated CrowdRecruiter extensively using a large-scale real-world dataset and the results show that the proposed solution significantly outperforms three baseline algorithms by selecting 10.0% -- 73.5% fewer participants on average under the same probabilistic coverage constraint.
Crowdsourcing, Attention and Productivity

The tragedy of the digital commons does not prevent the copious voluntary production of content that one witnesses in the web. We show through an analysis of a massive data set from YouTube that the productivity exhibited in crowdsourcing exhibits a strong positive dependence on attention, measured by the number of downloads. Conversely, a lack of attention leads to a decrease in the number of videos uploaded and the consequent drop in productivity, which in many cases asymptotes to no uploads whatsoever. Moreover, uploaders compare themselves to others when having low productivity and to themselves when exceeding a threshold. 1 ar X iv :0 80 9. 30 30 v1 [ cs .C Y ] 1 7 Se p 20 08 We are witnessing an inversion of the traditional way by which content has been generated and consumed over the centuries. From photography to news and encyclopedic knowledge, the centuries-old pattern has been one in which a relatively few people and organizations produce content and most people consume it. With the advent of the web and the ease with which one can migrate content to it, that pattern has reversed, leading to a situation whereby millions create content in the form of blogs, news, videos, music, etc. and relatively few can attend to it all. This phenomenon, which goes under the name of crowdsourcing, is exemplified by websites such as Digg, Flicker, YouTube, and Wikipedia, where content creation without the traditional quality filters manages to produce sought out movies, news and even knowledge that rivals the best encyclopedias. That such content is valued is confirmed by the fact that access to these sites accounts for a sizable percentage of internet traffic. For example, as of June, 2007 YouTube alone comprised approximately 20% of all HTTP traffic, or nearly 10% of all traffic on the Internet . What makes crowdsourcing both interesting and puzzling is the underlying dilemma facing every contributor, which is best exemplified by the well-known tragedy of the commons. In such dilemmas, a group of people attempts to provide a common good in the absence of a central authority. In the case of crowdsourcing, the common good is in the form or videos, music, or encyclopedic knowledge that can be freely accessed by anyone. Furthermore, the good has jointness of supply, which means that its consumption by others does not affect the amounts that other users can use. And since it is nearly impossible to exclude non contributors from using the common good, it is rational for individuals not to upload content and free ride on the production of others. The dilemma ensues when every individual can reason this way and free ride on the efforts of others, making everyone worse off—thus the tragedy of the digital commons [1, 3, 7, 5, 10]. And yet paradoxically, there is ample evidence that while the ratio of contributions to downloads is indeed small, the growth in content provision persists at levels that are hard to understand if analyzed from a public goods point of view. One possible explanation for this puzzling behavior, which we explore in this paper, is that those contributing to the digital commons
Introduction to Pattern Recognition - Statistical, Structural, Neural and Fuzzy Logic Approaches

Find the secret to improve the quality of life by reading this introduction to pattern recognition statistical structural neural and fuzzy logic approaches. This is a kind of book that you need now. Besides, it can be your favorite book to read after having this book. Do you ask why? Well, this is a book that has different characteristic with others. You may not need to know who the author is, how well-known the work is. As wise word, never judge the words from who speaks, but make the words as your good value to your life.
Sieve algorithms for the shortest vector problem are practical

The most famous lattice problem is the Shortest Vector Problem (SVP), which has many applications in cryptology. The best approximation algorithms known for SVP in high dimension rely on a subroutine for exact SVP in low dimension. In this paper, we assess the practicality of the best (theoretical) algorithm known for exact SVP in low dimension: the sieve algorithm proposed by Ajtai, Kumar and Sivakumar (AKS) in 2001. AKS is a randomized algorithm of time and space complexity 2, which is theoretically much lower than the super-exponential complexity of all alternative SVP algorithms. Surprisingly, no implementation and no practical analysis of AKS has ever been reported. It was in fact widely believed that AKS was impractical: for instance, Schnorr claimed in 2003 that the constant hidden in the 2 complexity was at least 30. In this paper, we show that AKS can actually be made practical: we present a heuristic variant of AKS whose running time is (4/3+ε) polynomial-time operations, and whose space requirement is (4/3+ε)n/2 polynomially many bits. Our implementation can experimentally find shortest lattice vectors up to dimension 50, but is slower than classical alternative SVP algorithms in these dimensions.
A Study on Low-Rate DDoS Attacks in Real Networks

DDoS attacks are becoming more advanced to attack with a small traffic that is difficult to detect. In this paper, we propose two DDoS attacks that send a small traffic. One named SQL Search attack that is modified DoS attack using SQL wildcards. The other named Mass Contents Request attack that request large files. We prove experimentally that they are possible for the real homepages to DDoS attack with 10Megabytes/sec to 1Gigabytes/sec traffic. Also, we prove that proposed DDoS attacks have a small traffic and no patterns that not only it is difficult to detect of threshold-based but also it is hardly possible to detect of pattern-based through an experiment.
Artificial Intelligence in Hypertension Diagnosis : A Review

Artificial Intelligence methods are becoming very popular in medical applications due to high reliability and ease. From the past decades, Artificial Intelligence techniques such as Artificial Neural Networks, Fuzzy Expert Systems, Robotics etc have found an increased usage in disease diagnosis, patient monitoring, disease risk evaluation, predicting effect of new medicines and robotic handling of surgeries. This paper presents an introduction and survey on different artificial intelligence methods used by researchers for the application of diagnosing or predicting Hypertension. Hypertension, Artificial Neural Networks, Fuzzy Systems.
Inductive invariant generation via abductive inference

This paper presents a new method for generating inductive loop invariants that are expressible as boolean combinations of linear integer constraints. The key idea underlying our technique is to perform a backtracking search that combines Hoare-style verification condition generation with a logical abduction procedure based on quantifier elimination to speculate candidate invariants. Starting with true, our method iteratively strengthens loop invariants until they are inductive and strong enough to verify the program. A key feature of our technique is that it is lazy: It only infers those invariants that are necessary for verifying program correctness. Furthermore, our technique can infer arbitrary boolean combinations (including disjunctions) of linear invariants. We have implemented the proposed approach in a tool called HOLA. Our experiments demonstrate that HOLA can infer interesting invariants that are beyond the reach of existing state-of-the-art invariant generation tools.
Photosketcher: Interactive Sketch-Based Image Synthesis

Photosketcher is an interactive system for progressively synthesizing novel images using only sparse user sketches as input. Photosketcher works on the image content exclusively; it doesn't require keywords or other metadata associated with the images. Users sketch the rough shape of a desired image part, and Photosketcher searches a large collection of images for it. The search is based on a bag-of-features approach that uses local descriptors for translation-invariant retrieval of image parts. Composition is based on user scribbles: from the scribbles, Photosketcher predicts the desired part using Gaussian mixture models and computes an optimal seam using graph cuts. To further reduce visible seams, users can blend the composite image in the gradient domain.
Optimal Power Control for Energy Efficient D2D Communication and Its Distributed Implementation

In this letter, we propose an energy efficient power control scheme for resource sharing between cellular and device-to-device (D2D) users in cellular network assisted D2D communication. We take into account the circuit power consumption of the device-to-device user (DU) and aim at maximizing the DU's energy efficiency while guaranteeing the required throughputs of both the DU and the cellular user. Specifically, we define three different regions for the circuit power consumption of the DU and derive the optimal power control scheme for each region. Moreover, a distributed algorithm is proposed for implementation of the optimal power control scheme.
Recruitment of CRISPR-Cas systems by Tn7-like transposons.

A survey of bacterial and archaeal genomes shows that many Tn7-like transposons contain minimal type I-F CRISPR-Cas systems that consist of fused cas8f and cas5f, cas7f, and cas6f genes and a short CRISPR array. Several small groups of Tn7-like transposons encompass similarly truncated type I-B CRISPR-Cas. This minimal gene complement of the transposon-associated CRISPR-Cas systems implies that they are competent for pre-CRISPR RNA (precrRNA) processing yielding mature crRNAs and target binding but not target cleavage that is required for interference. Phylogenetic analysis demonstrates that evolution of the CRISPR-Cas-containing transposons included a single, ancestral capture of a type I-F locus and two independent instances of type I-B loci capture. We show that the transposon-associated CRISPR arrays contain spacers homologous to plasmid and temperate phage sequences and, in some cases, chromosomal sequences adjacent to the transposon. We hypothesize that the transposon-encoded CRISPR-Cas systems generate displacement (R-loops) in the cognate DNA sites, targeting the transposon to these sites and thus facilitating their spread via plasmids and phages. These findings suggest the existence of RNA-guided transposition and fit the guns-for-hire concept whereby mobile genetic elements capture host defense systems and repurpose them for different stages in the life cycle of the element.
High-Performance OCR for Printed English and Fraktur Using LSTM Networks

Long Short-Term Memory (LSTM) networks have yielded excellent results on handwriting recognition. This paper describes an application of bidirectional LSTM networks to the problem of machine-printed Latin and Fraktur recognition. Latin and Fraktur recognition differs significantly from handwriting recognition in both the statistical properties of the data, as well as in the required, much higher levels of accuracy. Applications of LSTM networks to handwriting recognition use two-dimensional recurrent networks, since the exact position and baseline of handwritten characters is variable. In contrast, for printed OCR, we used a one-dimensional recurrent network combined with a novel algorithm for baseline and x-height normalization. A number of databases were used for training and testing, including the UW3 database, artificially generated and degraded Fraktur text and scanned pages from a book digitization project. The LSTM architecture achieved 0.6% character-level test-set error on English text. When the artificially degraded Fraktur data set is divided into training and test sets, the system achieves an error rate of 1.64%. On specific books printed in Fraktur (not part of the training set), the system achieves error rates of 0.15% (Fontane) and 1.47% (Ersch-Gruber). These recognition accuracies were found without using any language modelling or any other post-processing techniques.
Network Store: Exploring Slicing in Future 5G Networks

In this paper, we provide a revolutionary vision of 5G networks, in which SDN technologies are used for the programmability of the wireless network, and where a NFV-ready network store is provided to Mobile Network Operators (MNO), Enterprises, and Over-The-Top (OTT) third parties. The proposed network serves as a digital distribution platform of programmable Virtualized Network Functions (VNFs) that enables 5G application use-cases. Currently existing application stores, such as Apple's App Store for iOS applications, Google's Play Store for Android, or Ubuntu's Software Center, deliver applications to user specific software platforms. Our vision is to provide a digital marketplace, gathering 5G enabling Network Applications and Network Functions, written to run on top of commodity cloud infrastructures, connected to remote radio heads (RRH). The 5G Network Store will be the same to the network provider as the application store is currently to a software platform.
Building high-performance smartphones via non-volatile memory: The swap approach

Smartphones are getting increasingly high-performance with advances in mobile processors and larger main memories to support feature-rich applications. However, the storage subsystem has always been a prohibitive factor that slows down the pace of reaching even higher performance while maintaining good user experience. Despite today's smartphones are equipped with larger-than-ever main memories, they consume more energy and still run out of memory. But the slow NAND flash based storage vetoes the possibility of swapping---an important technique to extend main memory---and leaves a system that constantly terminates user applications under memory pressure.
 In this paper, we revisit swapping for smartphones with fast, byte-addressable, non-volatile memory (NVM) technologies. Instead of using flash, we build the swap area with NVM, to allow high performance without sacrificing user experience. Based on NVM's high performance and byte-addressability, we show that a copy-on-write swap-in scheme can achieve even better performance by avoiding unnecessary memory copy operations. To avoid fast worn-out of certain NVMs, we also propose Heap-Wear, a wear leveling algorithm that more evenly distributes writes in NVM. Evaluation results based on the Google Nexus 5 smartphone show that our solution can effectively enhance smartphone performance and give better wear-leveling of NVM.
When Game Becomes Life: The Creators and Spectators of Online Game Replays and Live Streaming

Online gaming franchises such as World of Tanks, Defense of the Ancients, and StarCraft have attracted hundreds of millions of users who, apart from playing the game, also socialize with each other through gaming and viewing gamecasts. As a form of User Generated Content (UGC), gamecasts play an important role in user entertainment and gamer education. They deserve the attention of both industrial partners and the academic communities, corresponding to the large amount of revenue involved and the interesting research problems associated with UGC sites and social networks. Although previous work has put much effort into analyzing general UGC sites such as YouTube, relatively little is known about the gamecast sharing sites. In this work, we provide the first comprehensive study of gamecast sharing sites, including commercial streaming-based sites such as Amazon’s Twitch.tv and community-maintained replay-based sites such as WoTreplays. We collect and share a novel dataset on WoTreplays that includes more than 380,000 game replays, shared by more than 60,000 creators with more than 1.9 million gamers. Together with an earlier published dataset on Twitch.tv, we investigate basic characteristics of gamecast sharing sites, and we analyze the activities of their creators and spectators. Among our results, we find that (i) WoTreplays and Twitch.tv are both fast-consumed repositories, with millions of gamecasts being uploaded, viewed, and soon forgotten; (ii) both the gamecasts and the creators exhibit highly skewed popularity, with a significant heavy tail phenomenon; and (iii) the upload and download preferences of creators and spectators are different: while the creators emphasize their individual skills, the spectators appreciate team-wise tactics. Our findings provide important knowledge for infrastructure and service improvement, for example, in the design of proper resource allocation mechanisms that consider future gamecasting and in the tuning of incentive policies that further help player retention.