nils-herrmann/clean_scirepeval huggingface dataset:
Temporal Interaction Biased Community Detection in Social Networks
Venue: ADMA

Community detection in social media is a fundamental problem in social data analytics in order to understand user relationships and improve social recommendations. Although the problem has been extensively investigated, most of the research examined communities based on static structure in social networks. Our findings within large social networks such as Twitter, show that only a few users have interactions or communications within any fixed time interval. It is not difficult to see that it makes more potential sense to find such active communities that are biased to temporal interactions of social users, rather than relying solely on static structure. Communities detected with this new perspective will provide time variant social relationships or recommendations in social networks, which can greatly improve the applicability of social data analytics.
Estimating influence of social media users from sampled social networks
Venue: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Several indices for estimating the influence of social media users have been proposed. Most such indices are obtained from the topological structure of a social network that represents relations among social media users. However, several errors are typically contained in such social network structures because of missing data, false data, or poor node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of indices for estimating the influence of social media users. We compare the estimated influence of users, as obtained from a sampled social network, with their actual influence. Our experimental results show that using biased sampling methods, such as sample edge count, is a more effective approach than random sampling for estimating user influence, and that the use of random sampling to obtain the structure of a social network significantly affects the effectiveness of indices for estimating user influence, which may make indices useless.
The Function And Nature Of Imagery

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The Function And Nature Of Imagery

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KAMAR: A Lightweight Feistel Block Cipher Using Cellular Automata

Wireless Multimedia Sensor Network (WMSN) is an advancement of Wireless Sensor Network (WSN) that encapsulates WSN with multimedia information like image and video. The primary factors considered in the design and deployment of WSN are low power consumption, high speed and memory requirements. Security is indeed a major concern, in any communication system. Consequently, design of compact and high speed WMSN with cryptography algorithm for security, without compromising on sensor node performance is a challenge and this paper proposes a new lightweight symmetric key encryption algorithm based on 1 D cellular automata theory. Simulations are performed using MatLab and synthesized using Xilinx ISE. The proposed approach supports both software and hardware implementation and provides better performance compared to other existing algorithms in terms of number of slices, throughput and other hardware utilization.
RUNX1 truncation resulting from a cryptic and novel t(6;21)(q25;q22) chromosome translocation in acute myeloid leukemia: A case report
Venue: Oncology reports

Fluorescence in situ hybridization examination of a pediatric AML patient whose bone marrow cells carried trisomy 4 and FLT3 ITD mutation, demonstrated that part of the RUNX1 probe had unexpectedly moved to chromosome band 6q25 indicating a cryptic t(6;21)(q25;q22) translocation. RNA sequencing showed fusion of exon 7 of RUNX1 with an intergenic sequence of 6q25 close to the MIR1202 locus, something that was verified by RT PCR together with Sanger sequencing. The RUNX1 fusion transcript encodes a truncated protein containing the Runt homology domain responsible for both heterodimerization with CBFB and DNA binding, but lacking the proline serine and threonine rich (PST) region which is the transcription activation domain at the C terminal end. Which genetic event +4, FLT3 ITD, t(6;21) RUNX1 truncation or other, undetected acquired changes) was more pathogenetically important in the present case of AML, remains unknown. The case illustrates that submicroscopic chromosomal rearrangements may accompany visible numerical changes and perhaps should be actively looked for whenever a single trisomy is found. An active search for them may provide both pathogenetic and prognostic novel information.
A Fluorescence Polarization Biophysical Assay for the Naegleria DNA Hydroxylase Tet1
Venue: ACS medicinal chemistry letters

The discovery of the 5 methylcytosine (5mC) oxidation by the ten eleven translocation (Tet) protein family was an important advancement in our understanding of DNA modified epigenetics. Potent inhibitors of these proteins are greatly desired for both the understanding of the functions of these enzymes and to serve as eventual therapeutic leads. So far, the discovery of such small molecules with high affinity has been quite limited. Original tools to screen for activity are greatly needed in order to accelerate this process. Here we present a novel fluorescent probe, and the results of a fluorescence polarization based binding assay for Naegleria Tet1, a homologue to mammalian Tet. A fluorescence polarization based competition assay was also established and applied to the rapid and quantitative measurement of the binding affinity of the cofactor aKG and several known Tet1 inhibitors.
Efficient Solar Thermal Energy Harvest Driven by Interfacial Plasmonic Heating Assisted Evaporation.
Venue: ACS applied materials interfaces

The plasmonic heating effect of noble nanoparticles has recently received tremendous attention for various important applications. Herein, we report the utilization of interfacial plasmonic heating assisted evaporation for efficient and facile solar thermal energy harvest. An airlaid paper supported gold nanoparticle thin film was placed at the thermal energy conversion region within a sealed chamber to convert solar energy into thermal energy. The generated thermal energy instantly vaporizes the water underneath into hot vapors that quickly diffuse to the thermal energy release region of the chamber to condense into liquids and release the collected thermal energy. The condensed water automatically flows back to the thermal energy conversion region under the capillary force from the hydrophilic copper mesh. Such an approach simultaneously realizes efficient solar to thermal energy conversion and rapid transportation of converted thermal energy to target application terminals. Compared to conventional external photothermal conversion design, the solar thermal harvesting device driven by the internal plasmonic heating effect has reduced the overall thermal resistance by more than 50% and has demonstrated more than 25% improvement of solar water heating efficiency.
Specific microbiome dependent mechanisms underlie the energy harvest efficiency of ruminants
Venue: The ISME Journal

Ruminants have the remarkable ability to convert human indigestible plant biomass into human digestible food products, due to a complex microbiome residing in the rumen compartment of their upper digestive tract. Here we report the discovery that rumen microbiome components are tightly linked to cows' ability to extract energy from their feed, termed feed efficiency. Feed efficiency was measured in 146 milking cows and analyses of the taxonomic composition, gene content, microbial activity and metabolomic composition was performed on the rumen microbiomes from the 78 most extreme animals. Lower richness of microbiome gene content and taxa was tightly linked to higher feed efficiency. Microbiome genes and species accurately predicted the animals' feed efficiency phenotype. Specific enrichment of microbes and metabolic pathways in each of these microbiome groups resulted in better energy and carbon channeling to the animal, while lowering methane emissions to the atmosphere. This ecological and mechanistic understanding of the rumen microbiome could lead to an increase in available food resources and environmentally friendly livestock agriculture.
Adaptive connected dominating set discovering algorithm in energy harvest sensor networks
Venue: IEEE INFOCOM 2016 The 35th Annual IEEE International Conference on Computer Communications

A Wireless Sensor Network consists of a number of sensors. The energy of each sensor is limited which limits network lifetime. There are many existing energy efficiency algorithms to prolong network lifetime. Basically, there are two kinds of methods. One is energy efficiency management, such as duty cycling using virtual backbones. The other one is energy provision, such as energy harvest from the environment. In this paper, we introduce a new problem, CDSEH, to combine these two methods together. We also propose a new standard to define the network lifetime of a WSN. We prove that the CDSEH problem is NP Complete and propose two approximate algorithms accordingly. Extensive simulation results are shown to validate the performance of our algorithms.
Wearable energy smart ribbons for synchronous energy harvest and storage
Venue: Nature communications

A promising energy source for many current and future applications is a ribbon like device that could simultaneously harvest and store energy. Due to the high flexibility and weavable property, a fabric/matrix made using these ribbons could be highly beneficial for powering wearable electronics. Unlike the approach of using two separate devices, here we report a ribbon that integrates a solar cell and a supercapacitor. The electrons generated by the solar cell are directly transferred and stored on the reverse side of its electrode which in turn also functions as an electrode for the supercapacitor. When the flexible solar ribbon is illuminated with simulated solar light, the supercapacitor holds an energy density of 1.15 mWh cm 3 and a power density of 243 mW cm 3. Moreover, these ribbons are successfully woven into a fabric form. Our all solid state ribbon unveils a highly flexible and portable self sufficient energy system with potential applications in wearables, drones and electric vehicles.
Factors Influence the Utilization of Community Participation (POSBINDU)

Posbindu is a form of public participation to conduct early detection and monitoring of risk factors for non communicable diseases (NCD) and where it was carried out in as an integrated manner, routine and periodic event. Posbindu itself aimed to promote the community participation in prevention and early detection of risk factors for non communicable disease (NCD) Non communicable diseases is the biggest killer in the world,causing approximately 60% of global deaths. More than 9 millions of all deaths related to NCD occurred under the age 60 years old, and 90% premature deaths incidence occurs in countries where significant number of population were low income population. In Indonesia, death related to NCD are growing in an alarming rate, from approximately 41% at the year 1995, striking 59,5% at year 2007. According to Basic Health Survey of 2007, NCD related deaths ranked number 6 in a top ten Death list.The growing rate of NCD prevalence are to become a serious threat upon national Development, death risk from disease related condition will negatively impact human resources nationwide, which the effect will surely not limited to health aspect only but expanded to economic aspect.Meanwhile, Posbindu program had not reach its popularity as it should have been. In some region, posbindu visits by locals has decreased. At the study area, existing data showed that, only as much as 10% of the local population was using Posbindu service. In many regions, Posbindu were deemed to merge with geriatric social health care, as the major user was the geriatric population.
A Review on the use of Augmented Reality to Generate Safety Awareness and Enhance Emergency Response

Augmented reality applications annotate real world with computer generated graphics. They make the surrounding environment interactive by overlaying digital 3D models or some plain text information over and around the tangible objects in its radius. Augmented reality finds its uses in many diverse fields; one such field is discussed in this paper: Safety. This review paper has conducted a study on various existing augmented reality systems that have been successful in implementing a way to provide the user with the knowledge pertaining to safety practices or parting knowledge to the user when it comes to learning how to react in case of emergencies. The ARLIST project (F. Pretto et al, 2008) focused on enhancing Life Support training by projecting audio and visual content on manikins in an attempt to provide a realistic scenario for the trainees. To generate safety awareness in an earthquake prone country Japan; an augmented furniture falling simulation was implemented by generating 3D furniture models onto strategically placed markers in real environment. An in car augmented reality system was designed for averting possible collisions with surrounding environment and pedestrians and A computer vision based tracking method was used to realise an augmented reality application that could enhance the safety for jobs involving physical danger (like construction jobs) backed by a method of information presentation. The paper concludes with establishing the need for a comprehensive system that incorporates both safety awareness and safety training instead of just one of these pair of features.
Measurements of general relativity precessions in the field of the Earth with laser ranged satellites and the LARASE program
Venue: 2016 IEEE Metrology for Aerospace (MetroAeroSpace)

The LAser Ranged Satellites Experiment (LARASE) is a new experiment whose main purpose is to provide precise and accurate measurements of gravitation in the weak field and slow motion (WFSM) limit of Einstein's theory of general relativity by means of a very precise laser tracking of geodetic satellites in orbit around the Earth. Beside the good quality of the tracking observations of the satellites orbit, guaranteed by the powerful Satellite Laser Ranging (SLR) technique of the International Laser Ranging Service (ILRS) also the quality of the dynamical models implemented in a software code plays a fundamental role in order to obtain precise and accurate measurements of relativistic physics. The models have to account for the perturbations provoked by both gravitational and non gravitational perturbations in such a way to reduce as well as possible the difference between the observed range, from the tracking, and the computed one, from the models. In particular, LARASE is an experiment that aims to improve the dynamical models of the present best laser ranged satellites in order to perform a precise and accurate orbit determination. This represents a first step towards new refined tests and measurements of GR in the field of the Earth. After a brief presentation of the main relativistic measurements which constitute the main goals of LARASE, the results obtained during last year will be discussed in terms of the improvements reached in the satellites orbit modelling and in their precise orbit determination.
Ensemble of keyword extraction methods and classifiers in text classification
Venue: Expert Syst. Appl.

Text classification is a domain with high dimensional feature space.Extracting the keywords as the features can be extremely useful in text classification.An empirical analysis of five statistical keyword extraction methods.A comprehensive analysis of classifier and keyword extraction ensembles.For ACM collection, a classification accuracy of 93.80% with Bagging ensemble of Random Forest. Automatic keyword extraction is an important research direction in text mining, natural language processing and information retrieval. Keyword extraction enables us to represent text documents in a condensed way. The compact representation of documents can be helpful in several applications, such as automatic indexing, automatic summarization, automatic classification, clustering and filtering. For instance, text classification is a domain with high dimensional feature space challenge. Hence, extracting the most important/relevant words about the content of the document and using these keywords as the features can be extremely useful. In this regard, this study examines the predictive performance of five statistical keyword extraction methods (most frequent measure based keyword extraction, term frequency inverse sentence frequency based keyword extraction, co occurrence statistical information based keyword extraction, eccentricity based keyword extraction and TextRank algorithm) on classification algorithms and ensemble methods for scientific text document classification (categorization) In the study, a comprehensive study of comparing base learning algorithms (Naive Bayes, support vector machines, logistic regression and Random Forest) with five widely utilized ensemble methods (AdaBoost, Bagging, Dagging, Random Subspace and Majority Voting) is conducted. To the best of our knowledge, this is the first empirical analysis, which evaluates the effectiveness of statistical keyword extraction methods in conjunction with ensemble learning algorithms. The classification schemes are compared in terms of classification accuracy, F measure and area under curve values. To validate the empirical analysis, two way ANOVA test is employed. The experimental analysis indicates that Bagging ensemble of Random Forest with the most frequent based keyword extraction method yields promising results for text classification. For ACM document collection, the highest average predictive performance (93.80% is obtained with the utilization of the most frequent based keyword extraction method with Bagging ensemble of Random Forest algorithm. In general, Bagging and Random Subspace ensembles of Random Forest yield promising results. The empirical analysis indicates that the utilization of keyword based representation of text documents in conjunction with ensemble learning can enhance the predictive performance and scalability of text classification schemes, which is of practical importance in the application fields of text classification.
DWT SVD and DCT based robust and blind watermarking scheme for copyright protection
Venue: Multimedia Tools and Applications

In this article, a new DWT SVD and DCT with Arnold Cat Map encryption based robust and blind watermarking scheme is proposed for copyright protection. The proposed scheme solves the most frequently occurring watermarking security problems in Singular Value Decomposition (SVD) based schemes which are unauthorized reading and false positive detection. This scheme also optimizes fidelity and robustness characteristics. The grey image watermark splits into two parts using four bits MSBs and four bits LSBs of each pixel. Discrete Cosine Transform (DCT) coefficients of these MSBs and LSBs values are embedded into the middle singular value of each block having size 4 x 4 of the host image's one level Discrete Wavelet Transform (DWT) sub bands. The reason for incorporating Arnold Cat Map in the proposed scheme is to encode the watermark image before embedding it in the host image. The proposed scheme is a blind scheme and does not require the choice of scaling factor. Thus, the proposed scheme is secure as well as free from the false positive detection problem. The proposed watermarking scheme is tested for various malicious and non malicious attacks. The experimental results demonstrate that the scheme is robust, imperceptible and secure to several attacks and common signal processing operations.
Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images
Venue: Appl. Soft Comput.

Display Omitted Classification of normal, benign and malignant mammograms is proposed.Gabor wavelet coupled with Locality Sensitive Discriminant Analysis is used.Achieved accuracy of 98.69% for kNN classifier with eight features. Breast cancer is one of the prime causes of death in women. Early detection may help to improve the survival rate to a great extent. Mammography is considered as one of the most reliable methods to prescreen of breast cancer. However, reading the mammograms by radiologists is laborious, taxing, and prone to intra/inter observer variability errors. Computer Aided Diagnosis (CAD) helps to obtain fast, consistent and reliable diagnosis. This paper presents an automated classification of normal, benign and malignant breast cancer using digitized mammogram images. The proposed method used Gabor wavelet for feature extraction and Locality Sensitive Discriminant Analysis (LSDA) for data reduction. The reduced features are ranked using their F values and fed to Decision Tree (DT) Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) k Nearest Neighbor (k NN) Naive Bayes Classifier (NBC) Probabilistic Neural Network (PNN) Support Vector Machine (SVM) AdaBoost and Fuzzy Sugeno (FSC) classifiers one by one to select the highest performing classifier using minimum number of features. The proposed method is evaluated using 690 mammogram images taken from a benchmarked Digital Database for Screening Mammography (DDSM) dataset. Our developed method has achieved mean accuracy, sensitivity, specificity of 98.69% 99.34% and 98.26% respectively for k NN classifier using eight features with 10 fold cross validation. This system can be employed in hospitals and polyclinics to aid the clinicians to cross verify their manual diagnosis.