nils-herrmann/clean_scirepeval huggingface dataset:
A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification

The rapid growth of World Wide Web has led to explosive growth of information. As most of information is stored in the form of texts, text mining has gained paramount importance. With the high availability of information from diverse sources, the task of automatic categorization of documents has become a vital method for managing, organizing vast amount of information and knowledge discovery. Text classification is the task of assigning predefined categories to documents. The major challenge of text classification is accuracy of classifier and high dimensionality of feature space. These problems can be overcome using Feature Selection. Feature selection is a process of identifying a subset of the most useful features from the original entire set of features. Feature selection (FS) is a strategy that aims at making text document classifiers more efficient and accurate. Feature selection methods provide us a way of reducing computation time, improving prediction performance, and a better understanding of the data. This paper surveys of text classification, several approaches of text classification, feature selection methods and applications of text classifications.
Variants of travelling salesman problem: A survey

The Travelling Salesman Problem (TSP) is a well-known NP-hard problem exceedingly studied in the fields of operations research and computer science. In TSP, a salesman wants to visit each of a set of cities exactly once and return to the starting city with minimal distance travelled. The significance of the TSP is that it can be pertained on many practical applications in real life scenario. But, it is not always possible to apply TSP for all the real world applications because of different constraints and also variations of TSP might be desired in such real-life scenarios. Therefore, several variants of TSP have been proposed to manage with the application specific constraints. In this work, a comprehensive study on various categories of TSP variants such as Profit Based, Time Windows based, Maximal Based and Kinetic Based has been studied with respect to the problem formulation and applications.
An electromechanical interpretation of electrowetting

Electrowetting on dielectric-coated electrodes involves two independently observable phenomena: (i) the well-known voltage dependence of the apparent contact angle and (ii) a central electromechanical force that can be exploited to move and manipulate small liquid volumes on a substrate. The electromechanical force does not depend upon field-mediated changes of the contact angle; it is operative even if the liquid surface is constrained. Forces on liquid volumes may be determined using capacitance or the Maxwell stress tensor with no reference made to liquid surface profiles. According to this interpretation, a nonlinear mechanism manifesting a voltage threshold is responsible for both contact angle saturation and the observed clamping of the electromechanical force.
On maximum clique problems in very large graphs

We present an approach for clique and quasi-clique computations in very large multi-digraphs. We discuss graph decomposition schemes used to break up the problem into several pieces of manageable dimensions. A semiexternal greedy randomized adaptive search procedure (GRASP) for finding approximate solutions to the maximum clique problem and maximum quasiclique problem in very large sparse graphs is presented. We experiment with this heuristic on real data sets collected in the telecommunications industry. These graphs contain on the order of millions of vertices and edges.
Automatic Rumor Detection on Microblogs: A Survey

The ever-increasing amount of multimedia content on modern social media platforms are valuable in many applications. While the openness and convenience features of social media also foster many rumors online. Without verification, these rumors would reach thousands of users immediately and cause serious damages. Many efforts have been taken to defeat online rumors automatically by mining the rich content provided on the open network with machine learning techniques. Most rumor detection methods can be categorized in three paradigms: the hand-crafted features based classification approaches, the propagation-based approaches and the neural networks approaches. In this survey, we introduce a formal definition of rumor in comparison with other definitions used in literatures. We summary the studies of automatic rumor detection so far and present details in three paradigms of rumor detection. We also give an introduction on existing datasets for rumor detection which would benefit following researches in this area. We give our suggestions for future rumors detection on microblogs as a conclusion.
Kinematics and workspace analysis for a novel 6-PSS parallel manipulator

In this paper, a novel 6-PSS parallel manipulator is designed and the three dimensional structure model is obtained with the Solidworks. The inverse kinematics analysis is performed based on the designed geometric parameters. And according to this analysis, we obtain the theoretical simulation results of virtual structures with different positions and orientations by using MATLAB software. These simulation structures verify the feasibility of this novel 6-PSS parallel manipulator. With a given orientation, a numerical search method is adopted in finding the reachable workspace with the judgment conditions of physical constraints, and the relationship between the reachable workspace and the size of the structure is studied in virtual simulation. Therefore, the range including the largest reachable workspace is achieved and a possible further application of this novel 6-PSS parallel manipulator is proposed, especially in some fields of different structure size requirements.
A study of big data processing constraints on a low-power Hadoop cluster

Big Data processing with Hadoop has been emerging recently, both on the computing cloud and enterprise deployment. However, wide-spread security exploits may hurt the reputation of public clouds. If Hadoop on the cloud is not an option, an organization has to build its own Hadoop clusters. But having a data center is not worth for a small organization both in terms of building and operating costs. Another viable solution is to build a cluster with low-cost ARM system-on-chip boards. This paper presents a study of a Hadoop cluster for processing Big Data built atop 22 ARM boards. The Hadoop's MapReduce was replaced by Spark and experiments on three different hardware configurations were conducted to understand limitations and constraints of the cluster. From the experimental results, it can be concluded that processing Big Data on an ARM cluster is highly feasible. The cluster could process a 34 GB Wikipedia article file in acceptable time, while generally consumed the power 0.061-0.322 kWh for all benchmarks. It has been found that I/O of the hardware is fast enough, but the power of CPUs is inadequate because they are largely spent for the Hadoop's I/O.
MC64: A Web Platform to Test Bioinformatics Algorithms in a Many-Core Architecture

New analytical methodologies, like the so-called “next-generation sequencing” (NGS), allow the sequencing of full genomes with high speed and reduced price. Yet, such technologies generate huge amounts of data that demand large raw computational power. Many-core technologies can be exploited to overcome the involved bioinformatics bottleneck. Indeed, such hardware is currently in active development. We have developed parallel bioinformatics algorithms for many-core microprocessors containing 64 cores each. Thus, the MC64 web platform allows executing high-performance alignments (NeedlemanWunsch, Smith-Waterman and ) of long sequences. The MC64 platform can be accessed via web browsers, allowing easy resource integration into thirdparty tools. Furthermore, the results obtained from the MC64 include timeperformance statistics that can be compared with other platforms.
Fast multi-view segment graph kernel for object classification

Object classification is an important issue in multimedia information retrieval. Usually, we can use images from multiple views (or multi-view images) to describe an object for classification. However, two issues remain unsolved. First, exploiting the spatial relations of local features from different view images for object classification. Second, accelerating the multi-view object classification process. To solve these two problems, we propose fast multi-view segment graph kernel (FMSGK). Given a set of multi-view images for an object, we segment each of them in terms of its color intensity distribution. And interand intra-view segment graphs are constructed to describe the spatial relations of the segments between and within view images respectively. Then, these two types of graphs are integrated into a so-called multi-view segment graph. And the kernel between objects is computed by accumulating all matchings’ of walk structures between their corresponding multi-view segment graphs. Since computing the kernel directly is highly time-consuming, an accelerating algorithm is derived. Finally, a multi-class support vector machine (SVM) (Duda et al., 2000 ; Wang et al., 2008 [32]; Dai and Mai, 2012 ) is trained based on the computed kernels for object classification. The experimental results on three data sets validate the effectiveness of our approach. & 2012 Elsevier B.V. All rights reserved.
Association of the OCA2 Polymorphism His615Arg with Melanin Content in East Asian Populations: Further Evidence of Convergent Evolution of Skin Pigmentation

The last decade has witnessed important advances in our understanding of the genetics of pigmentation in European populations, but very little is known about the genes involved in skin pigmentation variation in East Asian populations. Here, we present the results of a study evaluating the association of 10 Single Nucleotide Polymorphisms (SNPs) located within 5 pigmentation candidate genes (OCA2, DCT, ADAM17, ADAMTS20, and TYRP1) with skin pigmentation measured quantitatively in a sample of individuals of East Asian ancestry living in Canada. We show that the non-synonymous polymorphism rs1800414 (His615Arg) located within the OCA2 gene is significantly associated with skin pigmentation in this sample. We replicated this result in an independent sample of Chinese individuals of Han ancestry. This polymorphism is characterized by a derived allele that is present at a high frequency in East Asian populations, but is absent in other population groups. In both samples, individuals with the derived G allele, which codes for the amino acid arginine, show lower melanin levels than those with the ancestral A allele, which codes for the amino acid histidine. An analysis of this non-synonymous polymorphism using several programs to predict potential functional effects provides additional support for the role of this SNP in skin pigmentation variation in East Asian populations. Our results are consistent with previous research indicating that evolution to lightly-pigmented skin occurred, at least in part, independently in Europe and East Asia.
A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education

0950-7051/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.knosys.2010.03.010 * Corresponding author.  Software Quality L and Technology, Hellenic Open University, 12–15 Tsa 26222, Greece. Tel.: +3
Network virtualization and software defined networking for cloud computing: a survey

Network virtualization is the key to the current and future success of cloud computing. In this article, we explain key reasons for virtualization and briefly explain several of the networking technologies that have been developed recently or are being developed in various standards bodies. In particular, we explain software defined networking, which is the key to network programmability. We also illustrate SDN¿s applicability with our own research on OpenADN - application delivery in a multi-cloud environment.
Verification of an Implementation of Tomasulo's Algorithm by Compositional Model Checking

An implementation of an out-of-order processing unit based on Tomasulo’s algorithm is formally verified using compositional model checking techniques. This demonstrates that finite-state methods can be applied to such algorithms, without recourse to higher-order proof systems. The paper introduces a novel compositional system that supports cyclic environment reasoning and multiple environment abstractions per signal. A proof of Tomasulo’s algorithm is outlined, based on refinement maps, and relying on the novel features of the compositional system. This proof is fully verified by the SMV verifier, using symmetry to reduce the number of assertions that must be verified.
Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data

Despite remarkable successes achieved by Differential InSAR, estimations of low tectonic strain rates remain challenging in areas where deformation and topography are correlated, mainly because of the topography‐related atmospheric phase screen (APS). In areas of high relief, empirical removal of the stratified component of the APS may lead to biased estimations of tectonic deformation rates. Here we describe a method to correct interferograms from the effects of the spatial and temporal variations in tropospheric stratification by computing tropospheric delay maps coincident with SAR acquisitions using the ERA‐ Interim global meteorological model. The modeled phase delay is integrated along vertical profiles at the ERA‐I grid nodes and interpolated at the spatial sampling of the interferograms above the elevation of each image pixel. This approach is validated on unwrapped interferograms. We show that the removal of the atmospheric signal before phase unwrapping reduces the risk of unwrapping errors in areas of rough topography.
Breaking CAPTCHAs on the Dark Web 11 February , 2018

On the Dark Web, several websites inhibit automated scraping attempts by employing CAPTCHAs. Scraping important content from a website is possible if these CAPTCHAs are solved by a web scraper. For this purpose, a Machine Learning tool is used, TensorFlow and an Optical Character Recognition tool, Tesseract to solve simple CAPTCHAs. Two sets of CATPCHAs, which are also used on some Dark Web websites, were generated for testing purposes. Tesseract achieved a success rate of 27.6% and 13.7% for set 1 and 2, respectively. A total of three models were created for TensorFlow. One model per set of CAPTCHAs and one model with the two sets mixed together. TensorFlow achieved a success rate of 94.6%, 99.7%, and 70.1% for the first, second, and mixed set, respectively. The initial investment to train TensorFlow can take up to two days to train for a single type of CAPTCHA, depending on implementation efficiency and hardware. The CAPTCHA images, including the answers, are also a requirement for training TensorFlow. Whereas Tesseract can be used on-demand without need for prior training.
Two practical man-in-the-middle attacks on Bluetooth secure simple pairing and countermeasures

We propose two new Man-In-The-Middle (MITM) attacks on Bluetooth Secure Simple Pairing (SSP). The attacks are based on the falsification of information sent during the input/output capabilities exchange and also the fact that the security of the protocol is likely to be limited by the capabilities of the least powerful or the least secure device type. In addition, we devise countermeasures that render the attacks impractical, as well as improvements to the existing Bluetooth SSP in order to make it more secure. Moreover, we provide a comparative analysis of the existing MITM attacks on Bluetooth.
Learned helplessness in humans: critique and reformulation.

The learned helplessness hypothesis is criticized and reformulated. The old hypothesis, when applied to learned helplessness in humans, has two major problems: (a) It does not distinguish between cases in which outcomes are uncontrollable for all people and cases in which they are uncontrollable only for some people (univervsal vs. personal helplessness), and (b) it does not explain when helplessness is general and when specific, or when chronic and when acute. A reformulation based on a revision of attribution theory is proposed to resolve these inadequacies. According to the reformulation, once people perceive noncontingency, they attribute their helplessness to a cause. This cause can be stable or unstable, global or specific, and internal or external. The attribution chosen influences whether expectation of future helplessness will be chronic or acute, broad or narrow, and whether helplessness will lower self-esteem or not. The implications of this reformulation of human helplessness for the learned helplessness model of depression are outlined.
7 Toward a Universal Underspecified Semantic Representation

We define Canonical Form Minimal Recursion Semantics (CF-MRS) and prove that all the well-formed MRS structures generated by the MRS semantic composition algorithm are in this form. We prove that the qeq relationships are equivalent to outscoping relations when MRS structures are in this form. This result fills the gap between some underspecification formalisms and motivates defining a Canonical Form Underspecified Representation (CF-UR) which brings those underspecification formalisms together.
A solution to room-by-room coverage for autonomous cleaning robots

We introduce RoomsSeg, a novel method for segmenting occupancy grid maps into regions that represent rooms and corridors in the real world. The segmentation is utilized for systematic room-by-room cleaning on autonomous vacuum cleaners running in private homes. RoomsSeg is based on automated clutter removal and watershed segmentation on grid maps. Segmented regions are merged into rooms by semantic decision rules. Presented experimental results clearly indicate the efficiency and accuracy of the approach when compared with state of the art methods. When deployed on cleaning robots, a substantial decrease in mission time can be achieved.
Data Base Sublanguage Founded on the Relational Calculus

Three principal types of language for data base manipulation are identified: the low-level, procedure-oriented (typified by the CODASYL-proposed DML), the intermediate level, algebraic (typified by the Project MAC MacAIMS language), and the high level, relational calculus-based data sublanguage, an example of which is described in this paper. The language description is informal and stresses concepts and principles. Following this, arguments are presented for the superiority of the calculus-based type of data base sub-language over the algebraic, and for the algebraic over the low-level procedural. These arguments are particularly relevant to the questions of inter-system compatibility and standardization.
Big Data Reduction Methods: A Survey

Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.
BIG DATA ANALYTICS IN HEALTHCARE : A SURVEY Gemson

Like Oxygen, the world is surrounded by data today. The quantity of data that we harvest and eat up is thriving aggressively in the digitized world. Increasing use of new innovations and social media generate vast amount of data that can earn splendid information if properly analyzed. This large dataset generally known as big data, do not fit in traditional databases because of its’ rich size. Organizations need to manage and analyze big data for better decision making and outcomes. So, big data analytics is receiving a great deal of attention today. In healthcare, big data analytics has the possibility of advanced patient care and clinical decision support. In this paper, we review the background and the various methods of big data analytics in healthcare. This paper also elaborates various platforms and algorithms for big data analytics and discussion on its advantages and challenges. This survey winds up with a discussion of challenges and future directions.
Tissue dissolution by sodium hypochlorite: effect of concentration, temperature, agitation, and surfactant.

Sodium hypochlorite is the most commonly used endodontic irrigant because of its antimicrobial and tissue-dissolving activity. The aim of this study was to evaluate and compare the effects of concentration, temperature, and agitation on the tissue-dissolving ability of sodium hypochlorite. In addition, a hypochlorite product with added surface active agent was compared with conventional hypochlorite solutions.



Three sodium hypochlorite solutions from two different manufacturers in concentrations of 1%, 2%, 4%, and 5.8% were tested at room temperature, 37 degrees C, and 45 degrees C with and without agitation by ultrasonic and sonic energy and pipetting. Distilled and sterilized tap water was used as controls. Pieces of bovine muscle tissue (68 +/- 3 mg) were placed in 10 mL of each solution for five minutes. In selected samples, agitation was performed for one, two, or four 15-second periods per each minute. The tissue specimens were weighed before and after treatment, and the percentage of weight loss was calculated. The contact angle on dentin of the three solutions at concentrations of 1% and 5.8% was measured.



Weight loss (dissolution) of the tissue increased almost linearly with the concentration of sodium hypochlorite. Higher temperatures and agitation considerably enhanced the efficacy of sodium hypochlorite. The effect of agitation on tissue dissolution was greater than that of temperature; continuous agitation resulted in the fastest tissue dissolution. Hypochlorite with added surface active agent had the lowest contact angle on dentin and was most effective in tissue dissolution in all experimental situations.



Optimizing the concentration, temperature, flow, and surface tension can improve the tissue-dissolving effectiveness of hypochlorite even 50-fold.
A review of issues and challenges in designing Iris recognition Systems for noisy imaging environment

Iris recognition is a challenging task in a noisy imaging environment. Nowadays researcher's primary focus is to develop reliable Iris recognition System that can work in noisy imaging environment and to increase the iris recognition rate on different iris database. But there are major issues involved in designing such systems like occlusion by eyelashes, eyelids, glass frames, off-angle imaging, presence of contact lenses, poor illumination, motion blur, close-up of iris image acquired at a large standoff distance and specular reflections etc. Because of these issues the quality of acquired iris image gets affected. The performance of the iris based recognition system will deteriorate abruptly, when the iris mask is not accurate. This results in lower recognition rate. In this review paper different challenges in designing iris recognition systems for noisy imaging environment are reviewed and methodologies involved in overcoming these issues are discussed. At the end, some measures to improve the accuracy of such systems are suggested.