nils-herrmann/clean_scirepeval huggingface dataset:
Feature Extraction and Classification of Brain Tumor using MRI

1 M.Tech. Student 2 Assistant Professor 1,2 Department of Electronics & Communication Engineering 1,2 VVIETMysuru Abstract— MRI scan of brain tumor gives the detailed information of brain compared to other scans. Brain tumor is an uncontrolled and an unwanted multiplication and growing of cells in the body Image processing in MRI of brain is highly essential due to accurate detection of the type of brain abnormality which can reduce the chance of fatal result. Segmentation technique plays a major role in the brain tumor detection. This paper outlines an efficient image segmentation technique for the different ventricles affected brain tumor images. K-means segmentation is used for the brain tumor detection and extraction. The extraction of texture features in the detected tumor has been achieved by using Gray Level Co-occurrence Matrix (GLCM). By selecting the significant features the Classification has been performed to labeling the images into normal and abnormal (tumor detected) using Support Vector Machine (SVM).
Prioritization Methods for Accelerating MDP Solvers

The performance of value and policy iteration can be dramatically improved by eliminating redundant or useless backups, and by backing up states in the right order. We study several methods designed to accelerate these iterative solvers, including prioritization, partitioning, and variable reordering. We generate a family of algorithms by combining several of the methods discussed, and present extensive empirical evidence demonstrating that performance can improve by several orders of magnitude for many problems, while preserving accuracy and convergence guarantees.
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering

In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. Although most of these models have claimed state-of-the-art performance, the original papers often reported on only one or two selected datasets. We provide a systematic study and show that (i) encoding contextual information by LSTM and inter-sentence interactions are critical, (ii) Tree-LSTM does not help as much as previously claimed but surprisingly improves performance on Twitter datasets, (iii) the Enhanced Sequential Inference Model (Chen et al., 2017) is the best so far for larger datasets, while the Pairwise Word Interaction Model (He and Lin, 2016) achieves the best performance when less data is available. We release our implementations as an open-source toolkit.
VMbuddies: Coordinating Live Migration of Multi-Tier Applications in Cloud Environments

Enabled by virtualization technologies, various multi-tier applications (such as web applications) are hosted by virtual machines (VMs) in cloud data centers. Live migration of multi-tier applications across geographically distributed data centers is important for load management, power saving, routine server maintenance and quality-of-service. Different from a single-VM migration, VMs in a multi-tier application are closely correlated, which results in a correlated VM migrations problem. Current live migration algorithms for single-VM cause significant application performance degradation because intermediate data exchange between different VMs suffers relatively low bandwidth and high latency across distributed data centers. In this paper, we design and implement a coordination system called VMbuddies for correlated VM migrations in the cloud. Particularly, we propose an adaptive network bandwidth allocation algorithm to minimize the migration cost in terms of migration completion time, network traffic and migration downtime. Experiments using a public benchmark show that VMbuddies significantly reduces the performance degradation and migration cost of multi-tier applications.
Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.



Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature 'Hurst' was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers - Bayesian Classifier,  Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.



Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.



The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.
A Reinforcement Learning Approach for Adaptive Single- and Multi-Document Summarization

Reinforcement Learning (RL) is a generic framework for modeling decision making processes and as such very suited to the task of automatic summarization. In this paper we present a RL method, which takes into account intermediate steps during the creation of a summary. Furthermore, we introduce a new feature set, which describes sentences with respect to already selected sentences. We carry out a range of experiments on various data sets – including several DUC data sets, but also scientific publications and encyclopedic articles. Our results show that our approach a) successfully adapts to data sets from various domains, b) outperforms previous RL-based methods for summarization and state-of-the-art summarization systems in general, and c) can be equally applied to singleand multidocument summarization on various domains and document lengths.
The scaled unscented transformation

This paper describes a generalisation of the unscented transformation (UT) which allows sigma points to be scaled to an arbitrary dimension. The UT is a method for predicting means and covariances in nonlinear systems. A set of samples are deterministically chosen which match the mean and covariance of a (not necessarily Gaussian-distributed) probability distribution. These samples can be scaled by an arbitrary constant. The method guarantees that the mean and covariance second order accuracy in mean and covariance, giving the same performance as a second order truncated filter but without the need to calculate any Jacobians or Hessians. The impacts of scaling issues are illustrated by considering conversions from polar to Cartesian coordinates with large angular uncertainties.
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning

This paper presents a semantic parsing and reasoning approach to automatically solving math word problems. A new meaning representation language is designed to bridge natural language text and math expressions. A CFG parser is implemented based on 9,600 semi-automatically created grammar rules. We conduct experiments on a test set of over 1,500 number word problems (i.e., verbally expressed number problems) and yield 95.4% precision and 60.2% recall.
The Maximum Clique Problem

Given an undirected graph G = ( V, E ), a clique S is a subset of V such that for any two elements u, v ∈ S, ( u, v ) ∈ E. Using the notation ES to represent the subset of edges which have both endpoints in clique S, the induced graph GS = ( S, ES ) is complete. Finding the largest clique in a graph is an NP-hard problem, called the maximum clique problem (MCP). Cliques are intimately related to vertex covers and independent sets. Given a graph G, and defining E* to be the complement of E, S is a maximum independent set in the complementary graph G* = ( V, E* ) if and only if S is a maximum clique in G. It follows that V – S is a minimum vertex cover in G*. There is a separate weighted form of MCP that we will not consider further here.
Error Link Detection and Correction in Wikipedia

The hyperlink structure of Wikipedia forms a rich semantic network connecting entities and concepts, enabling it as a valuable source for knowledge harvesting. Wikipedia, as crowd-sourced data, faces various data quality issues which significantly impacts knowledge systems depending on it as the information source. One such issue occurs when an anchor text in a Wikipage links to a wrong Wikipage, causing the error link problem. While much of previous work has focused on leveraging Wikipedia for entity linking, little has been done to detect error links.
 In this paper, we address the error link problem, and propose algorithms to detect and correct error links. We introduce an efficient method to generate candidate error links based on iterative ranking in an Anchor Text Semantic Network. This greatly reduces the problem space. A more accurate pairwise learning model was used to detect error links from the reduced candidate error link set, while suggesting correct links in the same time. This approach is effective when data sparsity is a challenging issue. The experiments on both English and Chinese Wikipedia illustrate the effectiveness of our approach. We also provide a preliminary analysis on possible causes of error links in English and Chinese Wikipedia.
72 dB SNR, 240 Hz Frame Rate Readout IC With Differential Continuous-Mode Parallel Architecture for Larger Touch-Screen Panel Applications

This paper presents a mutual capacitive touch screen panel (TSP) readout IC (ROIC) with a differential continuousmode parallel operation architecture (DCPA). The proposed architecture achieves a high product of signal-to-noise ratio (SNR) and frame rate, which is a requirement of ROIC for large-sized TSP. DCPA is accomplished by using the proposed differential sensing method with a parallel architecture in a continuousmode. This architecture is implemented using a continuous-type transmitter for parallel signaling and a differential-architecture receiver. A continuous-type differential charge amplifier removes the common-mode noise component, and reduces the self-noise by the band-pass filtering effect of the continuous-mode charge amplifier. In addition, the differential parallel architecture cancels the timing skew problem caused by the continuous-mode parallel operation and effectively enhances the power spectrum density of the signal. The proposed ROIC was fabricated using a 0.18-μm CMOS process and occupied an active area of 1.25 mm2. The proposed system achieved a 72 dB SNR and 240 Hz frame rate with a 32 channel TX by 10 channel RX mutual capacitive TSP. Moreover, the proposed differential-parallel architecture demonstrated higher immunity to lamp noise and display noise. The proposed system consumed 42.5 mW with a 3.3-V supply.
How will NFV/SDN transform service provider opex?

Software defined networking and network function virtualization are widely deemed two critical pillars of the future service provider network. The expectation for significant operations cost savings from network programmability, open APIs, and operations automation is frequently mentioned as one of the primary benefits of the NFV/SDN vision. Intuitively, the flexibility and simplification values attributed to NFV/SDN lead the industry to the conclusion that operating expenses will decrease. This article provides a view into the operational costs of a typical service provider and discusses how the NFV/SDN attributes can be expected to influence the business equation. The drivers of OPEX change, the directionality of the change, and the categories of OPEX most affected based on our analysis from interactions with a number of service providers worldwide are presented in a structured analysis.
The silent fading of an academic search engine: the case of Microsoft Academic Search

The goal of this working paper is to summarize the main empirical evidences provided by the scientific community as regards the comparison between the two main citation-based academic search engines: Google Scholar (GS) and Microsoft Academic Search (MAS), paying special attention to the following issues: coverage; correlations between journal rankings; and usage of these academic search engines. Additionally, self-elaborated data is offered, which are intended to provide current evidence about the popularity of these tools on the Web, by measuring the number of rich files (PDF, PPT and DOC) in which these tools are mentioned, the amount of external links that both products receive, and the search queries’ frequency from Google Trends. The poor results obtained by MAS led us to an unexpected and unnoticed discovery: Microsoft Academic Search is outdated since 2013. Therefore, the second part of the working paper aims at advancing some data demonstrating this lack of update. For this purpose we gathered the number of total records indexed by MAS since 2000. The data shows an abrupt drop in the number of documents indexed from 2,346,228 in 2010 to 8,147 in 2013. This decrease is offered according to 15 thematic areas as well. In view of these problems it seems logical not only that MAS was poorly used to search for articles by academics and students (who mostly use Google or Google Scholar), but virtually ignored by bibliometricians.
Attack Graph Based Evaluation of Network Security

The perspective directions in evaluating network security are simulating possible malefactor’s actions, building the representation of these actions as attack graphs (trees, nets), the subsequent checking of various properties of these graphs, and determining security metrics which can explain possible ways to increase security level. The paper suggests a new approach to security evaluation based on comprehensive simulation of malefactor’s actions, construction of attack graphs and computation of different security metrics. The approach is intended for using both at design and exploitation stages of computer networks. The implemented software system is described, and the examples of experiments for analysis of network security level are considered.
Fast Pedestrian Detection for Mobile Devices

In this paper we present a fast and robust solution for pedestrian detection that can run in real time conditions even on mobile devices with limited computational power. An optimization of the channel features based multiscale detection schemes is proposed by using 8 detection models for each half octave scales. The image features have to be computed only once each half octave and there is no need for feature approximation. We use multiscale square features for training the multiresolution pedestrian classifiers. The proposed solution achieves state of art detection results on Caltech pedestrian benchmark at over 100 FPS using a CPU implementation, being the fastest detection approach on the benchmark. The solution is fast enough to perform under real time conditions on mobile platforms, yet preserving its robustness. The full detection process can run at over 20 FPS on a quad-core ARM CPU based smartphone or tablet, being a suitable solution for limited computational power mobile devices or embedded platforms.
Segmentation and recognition of multi-food meal images for carbohydrate counting

In this paper, we propose novel methodologies for the automatic segmentation and recognition of multi-food images. The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients. Initially the plate is segmented using pyramidal mean-shift filtering and a region growing algorithm. Then each of the resulted segments is described by both color and texture features and classified by a support vector machine into one of six different major food classes. Finally, a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem. The experimental results prove the effectiveness of the proposed method achieving a segmentation accuracy of 88.5% and recognition rate equal to 87%.
Frama-C: A software analysis perspective

Frama-C is a source code analysis platform that aims at conducting verification of industrial-size C programs. It provides its users with a collection of plug-ins that perform static analysis, deductive verification, and testing, for safety- and security-critical software. Collaborative verification across cooperating plug-ins is enabled by their integration on top of a shared kernel and datastructures, and their compliance to a common specification language. This foundational article presents a consolidated view of the platform, its main and composite analyses, and some of its industrial achievements.
A multiple model PHD approach to tracking of cars under an assumed rectangular shape

This paper presents an extended target tracking method for tracking cars in urban traffic using data from laser range sensors. Results are presented for three real world datasets that contain multiple cars, occlusions, and maneuver changes. The car's shape is approximated by a rectangle, and single track steering models are used for the target kinematics. A multiple model approach is taken for both the dynamics modeling and the measurement modeling. A comparison to ground truth shows that the estimation errors are generally very small: on average the absolute error is less than half a degree for the heading. Multiple cars are handled using a multiple model PHD filter, where a variable probability of detection is integrated to enable tracking of occluded cars.
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.
Complexity Theoretic Lower Bounds for Sparse Principal Component Detection

In the context of sparse principal component detection, we bring evidence towards the existence of a statistical price to pay for computational efficiency. We measure the performance of a test by the smallest signal strength that it can detect and we propose a computationally efficient method based on semidefinite programming. We also prove that the statistical performance of this test cannot be strictly improved by any computationally efficient method. Our results can be viewed as complexity theoretic lower bounds conditionally on the assumptions that some instances of the planted clique problem cannot be solved in randomized polynomial time.
Knowledge, management, and knowledge management in business operations

The purpose of this research is to help knowledge managers systematically grasp ‘‘knowledge about management knowledge’’ and get a ‘‘deep and full’’ understanding of the nature, scope and methodologies of knowledge management.  Through presenting a variety of perspectives on knowledge, management, and knowledge management, the article explores the essence of knowledge management in organizations from a perspective of critical systems thinking.  Knowledge management in business organizations has the task of managing the activities of knowledge workers or the transformation and interaction of organizational ‘‘static substance knowledge’’ and ‘‘dynamic process knowledge’’ for ‘‘products, services, and practical process innovation’’ and, at the same time, ‘‘creating new or justifying existing organizational systematic knowledge’’. Knowledge management is not simply about recording and manipulating explicit knowledge, but needs to address that which is implicit, and from which benefit can therefore be derived only through process rather than content.  The comprehensive review and classification of various management theories will expand both knowledge managers’ and knowledge workers’ understanding of the subject and provide a foundation for building a knowledge management toolkit in practice.
A learning-based algorithm for improved bandwidth-awareness of adaptive streaming clients

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for Over-The-Top video streaming. A HAS video consists of multiple segments, encoded at multiple quality levels. Allowing the client to select the quality level for every segment, a smoother playback and a higher Quality of Experience (QoE) can be perceived. Although results are promising, current quality selection heuristics are generally hard coded. Fixed parameter values are used to provide an acceptable QoE under all circumstances, resulting in suboptimal solutions. Furthermore, many commercial HAS implementations focus on a video-on-demand scenario, where a large buffer size is used to avoid play-out freezes. When the focus is on a live TV scenario however, a low buffer size is typically preferred, as the video play-out delay should be as low as possible. Hard coded implementations using a fixed buffer size are not capable of dealing with both scenarios. In this paper, the concept of reinforcement learning is introduced at client side, allowing to adaptively change the parameter configuration for existing rate adaptation heuristics. Bandwidth characteristics are taken into account in the decision process, thus allowing to improve the client's bandwidth-awareness. Focus in this paper is on actively reducing the average buffer filling, evaluating results for two heuristics: the Microsoft IIS Smooth Streaming heuristic and the QoE-driven Rate Adaptation Heuristic for Adaptive video Streaming by Petrangeli et al. We show that using the proposed learning-based approach, the average buffer filling can be reduced by 8.3% compared to state of the art, while achieving a comparable level of QoE.