nils-herrmann/clean_scirepeval huggingface dataset:
Phytochemical determination and antibacterial activity of Punica granatum peel extracts against plant pathogenic bacteria

Plant pathogenic bacteria are recognized to be harmful microbes able to decrease the quantity and quality of crop production in the world. Punica granatum peel was screened for its potential use as biological control agent for plant pathogenic bacteria. P. granatum peel was successfully extract using n hexane, methanol and ethyl acetate by maceration. The highest yield obtained by ethyl acetate showed that ethyl acetate extracted more compounds that readily soluble to methanol and n hexane. For in vitro antibacterial activity, three different species of plant pathogenic bacteria were used namely Erwinia carotovorum subsp. Carotovorum, Ralstonia solanacearum, and Xanthomonas gardneri. For all crude extracts, four different concentrations 25, 50, 100 and 200 mg/ml were used in cup plate agar diffusion method. Streptomycin sulfate at concentration 30 mg/ml was used as positive control while each respective solvent used for peel extraction was used as negative control. The results obtained from in vitro studies showed only ethyl acetate extract possessed antibacterial activity tested on the plant pathogenic bacteria. Methanol and n hexane did not show any antibacterial activity against plant pathogenic bacteria selected where no inhibition zones were recorded. R. solanacearum recorded the highest diameter of inhibition zones for all range of concentrations introduced followed by E. carotovorum subsp. Carotovorum and X. gardneri. For the minimum inhbitory concentration (MIC) and minimum bactericidal concentration (MBC) only the ethyl acetate extract was subjected to the assay as only ethyl acetate extract exhibited antibacterial activity. The minimum concentration of ethyl acetate extract that was able to inhibit plant pathogenic bacteria was recorded at a concentration of 3.12 mg/ml which inhibited R. solancearum and E. carotovorum subsp. Carotovorum, followed by X. gardneri at concentration 6.25 mg/ml. For the minimum bactericidal concentration (MBC) the results showed that at the concentration of 12.5 mg/ml, the extract was still capable of killing the pathogenic bacteria, R. solanacearum, and P. caratovora sub.sp. caratovora while for the bacteria X. gardneri, the concentration that was able to kill the bacteria was 25 mg/ml. The qualitative estimation of phytochemical constituents within P. granatum L. ethyl acetate peel extracts had revealed the presence of tannins, flavonoids, phenols alkaloid, Saponins, and terpenoids. This study has demonstrated that Ethyl Acetate peel extracts of P. granatum has significant antibacterial activity against pathogenic plant bacterial, and it could be of high agricultural value.
Unsupervised multi graph cross modal hashing for large scale multimedia retrieval
Venue: Multimedia Tools and Applications

With the advance of internet and multimedia technologies, large scale multi modal representation techniques such as cross modal hashing, are increasingly demanded for multimedia retrieval. In cross modal hashing, three essential problems should be seriously considered. The first is that effective cross modal relationship should be learned from training data with scarce label information. The second is that appropriate weights should be assigned for different modalities to reflect their importance. The last is the scalability of training process which is usually ignored by previous methods. In this paper, we propose Multi graph Cross modal Hashing (MGCMH) by comprehensively considering these three points. MGCMH is unsupervised method which integrates multi graph learning and hash function learning into a joint framework, to learn unified hash space for all modalities. In MGCMH, different modalities are assigned with proper weights for the generation of multi graph and hash codes respectively. As a result, more precise cross modal relationship can be preserved in the hash space. Then Nystrom approximation approach is leveraged to efficiently construct the graphs. Finally an alternating learning algorithm is proposed to jointly optimize the modality weights, hash codes and functions. Experiments conducted on two real world multi modal datasets demonstrate the effectiveness of our method, in comparison with several representative cross modal hashing methods.
A rapid pattern recognition method for driving styles using clustering based support vector machines
Venue: 2016 American Control Conference (ACC)

A rapid pattern recognition approach to characterize driver's curve negotiating behavior is proposed. To shorten the recognition time and improve the recognition of driving styles, a k means clustering based support vector machine (kMC SVM) method is developed and used for classifying drivers into two types: aggressive and moderate. First, vehicle speed and throttle opening are treated as the feature parameters to reflect the driving styles. Second, to discriminate driver curve negotiating behaviors and reduce the number of support vectors, the k means clustering method is used to extract and gather the two types of driving data and shorten the recognition time. Then, based on the clustering results, a support vector machine approach is utilized to generate the hyperplane for judging and predicting to which types the human driver are subject. Lastly, to verify the validity of the kMC SVM method, a cross validation experiment is designed and conducted. The research results show that the kMC SVM is an effective method to classify driving styles with a short time, compared with SVM method.
Fast and de noise support vector machine training method based on fuzzy clustering method for large real world datasets

Classifying large and real world datasets is a challenging problem in machine learning algorithms. Among the machine learning methods, the support vector machine (SVM) is a well known approach with high generalization ability. Unfortunately, while the number of training data increases and the data contain noise, the performance of SVM signicantly decreases. In this paper, a fast and de noise two stage method for training SVMs to deal with large, real world datasets is proposed. In therst stage, data that contain noises or are suspected to be noisy are identied and eliminated from the genuine training dataset. The process of elimination and identication is based on the movement of the center of the convex hull data in the training dataset. The convex hull data are computed via the QHull algorithm. On the other hand, the well known fuzzy clustering method (FCM) is applied to compress and reduce the size of the training dataset. Finally, the reduced and puried cluster centers are used for training the SVM. A set of experiments is conducted on the four benchmarking datasets of the UCI database. Moreover, the amount of training time and the generalization of the proposed approach are compared with FCM SVM and normal SVM. The results indicate that the proposed method reduces the amount of training time and has a considerable success in removing noisy data from the training dataset. Therefore, the proposed method can achieve a higher generalization performance in comparison with the other methods in large, real world datasets.
A hybrid method for estimating the process change point using support vector machine and fuzzy statistical clustering
Venue: Appl. Soft Comput.

new hybrid method is developed for estimating the process change point in x control chart.An effective sets of features were extracted and the control chart patterns (CCP) are classified using support vector machines (SVM).Change point estimation methods were implemented for detecting the process change point.The proposed hybrid method provides a more accurate estimate of the process change point. Control charts are the most popular process monitoring techniques designed to determine whether a process is in a state of statistical control or not. When a process change occurs, the control chart exhibits an out of control signal. In most cases, the signal is followed by a substantial amount of delay. To address this drawback, supplementary techniques have been considered to be employed along with the control charts to identify the exact time of the process change. This paper presents a hybrid method for estimating the change point on x chart when neither the change type nor its magnitude are known. For this purpose, two sets of features with the most discriminatory power between x chart patterns are selected. Then the feature vectors are extracted from the control chart patterns (CCPs) and served as an input to a classification scheme which is comprised of several support vector machine (SVM) classifiers. After parameters tuning, the classifiers are built and used for classifying the CCPs and identifying the change type. Once the change type is determined, the fuzzy statistical clustering (FSC) and the maximum likelihood (ML) estimators are employed to identify the process change point. The performance of selected features, the classification scheme, and the change point estimators are evaluated by conducting several simulation studies. Empirical results show that in identifying the change types, the proposed classification scheme is more accurate than two recent CCP classification methods. The results also confirm that the proposed hybrid method offers an accurate estimate of the process change point, as compared to the most recent methods developed for the change point estimation.
A modified Support Vector Clustering method for document categorization
Venue: 2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)

In this paper, we propose a novel text categorization method based on modified Support Vector Clustering (SVC) SVC is a density based clustering approach, which handles the arbitrary shape clusters effectively. The main drawback of traditional SVC is that it treats unclassified documents as outliers. To overcome this problem, we employed Fuzzy C Means (FCM) to cluster unclassified documents. The modified SVC (SVC FCM) is applied to categorize text documents. The proposed method consists of three steps: In the first step, Regularized Locality Preserving Indexing (RLPI) is applied on Term Document Matrix (TDM) to reduce dimensionality of features. In second step, we use SVC to find base cluster centers of documents. Finally, we use FCM to cluster unclassified documents. To evaluate the performance of the proposed method, we conducted experiments on standard 20 NewsGroup dataset.
LDBC Graphalytics: A Benchmark for Large Scale Graph Analysis on Parallel and Distributed Platforms
Venue: Proc. VLDB Endow.

In this paper we introduce LDBC Graphalytics, a new industrial grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective comparison of graph analysis platforms. Its test harness produces deep metrics that quantify multiple kinds of system scalability, such as horizontal/vertical and weak/strong, and of robustness, such as failures and performance variability. The benchmark comes with open source software for generating data and monitoring performance. We describe and analyze six implementations of the benchmark (three from the community, three from the industry) providing insights into the strengths and weaknesses of the platforms. Key to our contribution, vendors perform the tuning and benchmarking of their platforms.
Critical success factors in electronic document and records management systems implementation at the Ministry of Trade and Industry in Botswana

The quest by the Government of Botswana to leverage the benefits brought about by the utilisation of information and communication technologies (ICTs) in the delivery of public services has seen an increase in the implementation of electronic document and records management systems (EDRMS) in the public sector. According to the Commonwealth of Australia (2011) an EDRMS is a software application that manages a range of digital information, including word processed documents, spreadsheets, emails, images and scanned documents. It combines the features of an electronic records management system and an electronic document management system. The Ministry of Trade and Industry (MTI) implemented an EDRMS, called the document workflow management system (DWMS) for its electronic records. The literature on the implementation of EDRMS shows that certain factors are critical for the adoption of EDRMS, which are referred to as critical success factors. They include top management support, good recordkeeping awareness and practice, early development of a records classification scheme, adequate and ongoing training and support, and well thought through change management strategies. This paper reports on the findings of an empirical study that investigated factors affecting the adoption and use of DWMS by action officers and records officers at the MTI. Although the study was mainly positivistic in nature and was supplemented by an interpretivist approach, this paper reports only the findings from interviews and documentary reviews. The study revealed that change management was poorly handled; top management support was partially lacking; a functional records classification scheme was hastily developed and put to use as part of DWMS implementation; training of system users was inadequate, to mention just a few. This paper posits that the EDRMS implementation is not just about dealing with technological factors as there are other non technological factors that are critical for its success and these need to be well understood and appreciated by organisations as they contribute to the success of EDRMS implementation.
Concrete softening effects on the axial capacity of RC jacketed circular columns

Among the different strengthening techniques to repair RC structures, reinforced concrete (RC) jacketing is one of the most commonly adopted, especially for columns. Its wide application is due to its easy application and relatively reduced cost with respect to other methods (e.g. FRP wrapping, Shape Memory Alloy active confinement) The target of RC jacketing is to increase axial, flexural capacity and ductility of weak existing members by means of two main effects: confinement action provided by the jacket and composite action between external jacket and inner concrete. Different theoretical studies have been carried out to calculate the strength enhancement due to confinement action and most of these are based on the adaptation of classical models for confinement evaluation in RC members. However, experimental investigations on compressive behaviour of RC jacketed sections have shown that the actual axial capacity could be substantially lower than that analytically evaluated with classical confinement models. This fact could be explained by taking into account the presence of tensile stresses developing in the jacket, due to the different dilatation of the inner core and the external jacket. This phenomenon is relevant especially in members where the concrete properties of the jacket are different with respect to those of the core, causing the premature failure of the external layer. This paper presents a simplified approach able to evaluate these effects. In particular RC jacketed circular columns are analysed as case study and the member is studied by considering the different concrete properties of core, jacket and cover. Circumferential and radial stresses are firstly calculated under the assumption of linear elastic behaviour and plane strain state. The model is extended in the non linear range by adopting a secant constitutive law. Finally, comparisons are made with experimental data available in the literature, showing good agreement and explaining some experimental results.
Longest interval between zeros of the tied down random walk, the Brownian bridge and related renewal processes

The probability distribution of the longest interval between two zeros of a simple random walk starting and ending at the origin, and of its continuum limit, the Brownian bridge, was analyzed in the past by Rosen and Wendel, then extended by the latter to stable processes. We recover and extend these results using simple concepts of renewal theory, which allows to revisit past or recent works of the physics literature. We also discuss related problems and open questions.
Regional Local Development Strategies Benefiting from Open Data and Open Tools and an Outlook on the Renewable Energy Sources Contribution

The New Cohesion Policy opens to an integrated place based approach for the improvement of territorial and social cohesion. The issue of territorial impact assessment of regional development policies highlight that data availability, open access to datasets in "near real time" participation, knowledge sharing, and assumed importance within the planning process. In this paper we present an application of spatial analysis techniques for evaluation of territorial effects of EU funds starting from open data by 'open coesione' The final scope is to have the opportunity to develop an assessment in the framework of a public debate. According to this issue we developed an empirical approach for context analysis using open data assumed as a relevant source of information for the measurement of diffused concerns. The application regards an internal area of the Basilicata Region: the Agri Valley, a complex contest in which environmental and agricultural vocation conflicts with the recent development of oil extraction industries (the actual main territorial specialization) The research produced an integrated approach in order to highlight the energy development domain for Agri Valley. In a first stage the balance between fossil/traditional and renewable energies development should consider the traditional manufacts and practice in order to push on community identity and social commitment. This implies for the planner the requirement of managing social conflicts and promoting effective procurement in territorial management. The paper describes as a strategy of conflicts resolution an integrated project called an "Energy Museum" The "Conclusions" at the end of this chapter discusses further applications and perspectives for improving regional development planning, considering the exploitation of open data sources and spatial analysis in one hand and on the Renewable Energy Sources on the other.
Estimation of the Global Solar Energy Potential and Photovoltaic Cost with the use of Open Data

There is an increasing demand for renewable electricity sources, due to the global efforts to reduce CO 2 emissions. Despite the promising effects, only a limited amount of electricity is currently produced globally from solar power. In order to help countries realize the importance of tapping into solar energy, it is crucial to reveal the potential amount of electricity that could be thus produced. For this reason, open data were used to produce an interactive web map of the global solar energy potential. For the calculation of the potential, the top down approach, generally used in the literature, was modified by introducing a better way of calculating rooftop areas, and accounting for temperature, which highly reduces PV panels' efficiency. Mean annual temperature data were introduced to improve its accuracy, and an approach to estimate rooftop and facade areas as a function of GDP was developed. The current global solar potential technically available was estimated at about 613 PWh/y. Furthermore, the cost of photovoltaic generation was computed and extremely low values, 0.03 0.2 /kWh, were derived.
Automated Support to Capture and Validate Security Requirements for Mobile Apps
Venue: APRES

Mobile application usage has become widespread and significant as it allows interactions between people and services anywhere and anytime. However, issues related to security have become a major concern among mobile users as insecure applications may lead to security vulnerabilities that make them easily compromised by hackers. Thus, it is important for mobile application developers to validate security requirements of mobile apps at the earliest stage to prevent potential security problems. In this paper, we describe our automated approach and tool, called MobiMEReq that helps to capture and validate the security attributes requirements of mobile apps. We employed the concept of Test Driven Development (TDD) with a model based testing strategy using Essential Use Cases (EUCs) and Essential User Interface (EUI) models. We also conducted an evaluation to compare the performance and correctness of our tool in various application domains. The results of the study showed that our tool is able to help requirements engineers to easily capture and validate security related requirements of mobile applications.
The Beautiful Risk of Education

and reason was turned back on itself only to demonstrate that rationality leads nowhere (p. 118) Citizens, trapped or not in the iron cage of bureaucracy, lost the sense of meaning in their lives: culture as participation had been replaced by knowledge as construction. This diagnosis of the triumph of modern technocracy is quite perceptive, but one major actor is missing: capitalism. It is tangentially introduced later under the guise of 'commercial society' (p. 120) one misses, however, the correlation between technocracy and capitalism that has been discussed by authors such as Russell, Mumford, Ellul and Gorz. From their perspective, technique has not lost its telos: a capitalistic telos has been substituted for the humanistic telos. Is education a matter of instruction in citizenship or in individualism? This anthology constitutes an excellent introduction to the current state of university education and to most historical facets of its crisis. Converging themes include the correlation between, on the one hand, liberal arts, Humboldtian university, culture, progressive civilization and robust democracy and, on the other, skill training, corporate university, nihilism, consumerism and friendly fascism. Unfortunately, no contributor produces a sharp judgement on the nature of technique (or on its technoscientific heir) and on its link with capitalism, a double conundrum that is at the core of the debated issue.
Follow me and like my beautiful selfies: Singapore teenage girls' engagement in self presentation and peer comparison on social media
Venue: Comput. Hum. Behav.

This study explores teenage girls' narrations of the relationship between self presentation and peer comparison on social media in the context of beauty. Social media provide new platforms that manifest media and peer influences on teenage girls' understanding of beauty towards an idealized notion. Through 24 in depth interviews, this study examines secondary school girls' self presentation and peer comparison behaviors on social network sites where the girls posted self portrait photographs or "selfies" and collected peer feedback in the forms of "likes," "followers," and comments. Results of thematic analysis reveal a gap between teenage girls' self beliefs and perceived peer standards of beauty. Feelings of low self esteem and insecurity underpinned their efforts in edited self presentation and quest for peer recognition. Peers played multiple roles that included imaginary audiences, judges, vicarious learning sources, and comparison targets in shaping teenage girls' perceptions and presentation of beauty. Findings from this study reveal the struggles that teenage girls face today and provide insights for future investigations and interventions pertinent to teenage girls' presentation and evaluation of self on social media. This study examines teenage girls' self presentation and peer comparison on SNSs.Teenage girls conform to peer norms when presenting and making sense of beauty.Edited self presentation on SNSs is a means of seeking peer recognition.Peer comparison is considered unhealthy but unavoidable.Likes and followers are important measures of peer attention and validation.