nils-herrmann/clean_scirepeval huggingface dataset:
Predicting Heart disease from Medical Data by Applying Naive Bayes and Apriori Algorithm
Year: 2013

Data mining could simply be explained at the non trivial extraction of the potentially useful, inherent, and previously unknown information from data. The foremost feature of data mining technique is that it provides a user oriented approach to hidden and novel patterns in the data. Data mining techniques have played a major role in the designing and development of the support system exclusively because of its abilities for discovering hidden patterns and relationships in the medical data. The purpose of this research study is to compare different data mining algorithms that could be used for the prediction of heart disease.
Understanding and assessing georisks in Central Africa: a prerequisite to design efficient decision support tools towards a better resilience
Year: 2016

The densely populated area extended from the North Kivu province in Democratic Republic of the Congo (DRC) to North Burundi and East Rwanda is vulnerable to several geohazards, such as landslides triggered by geodynamical processes (climate, seismicity, volcanism) and possibly worsen by anthropic actions. Located in the East African rift valley, the region is also characterized by a strong seismicity, with an increasing number of people and infrastructures exposed. In addition, east DRC hosts the two most active African volcanoes: Nyiragongo and Nyamulagira. Their activity can have serious impacts, as in 2002 when Nyiragongo directly endangers the ~800.000 inhabitants of Goma city, located ~15 km to the south. Linked to passive volcanic degassing, SO2 and CO2 discharge may also increase the population vulnerability. GeoRisCA is a project, funded by the Belgian scientific policy, which aims at studying the geo risks in this region (DRC, Rwanda, Burundi) in order to support regional and local risk management. A key contribution of GeoRisCA is to work together with the national and local institutions on assessing and outlining the vulnerabilities by taking into account exposure to perturbations and adaptive capacity or resilience of the systems. On one hand, the exposure is identified as the potential degree of loss of a given element or set of elements at risk; i.e. the susceptibility of people, infrastructures and buildings with respect to a hazard. On the other hand, the resilience of the individual, the household, the community, is its adaptive capacity to absorb disturbance and reorganize into a fully functioning system by anticipation, response, adaptation and recovery. It focuses mainly on land use and on socio economic and demographic factors that increase or attenuate the impacts of hazards, and evolve with time (e.g. improved education, increased income, denser social networks and evolution of coping mechanisms) The output of GeoRisCA will be a comprehensive but simple methodology for reliable geo risks measurement and mapping, designed as decision supporting tools for existing disasters preparedness and mitigation institutions.
Kinetics of non oxidative propane dehydrogenation on Cr2O3 and the nature of catalyst deactivation from first principles simulations
Year: 2020

As the global demand for propene (propylene) is increasing, classic commercial production processes are becoming unable to keep up. Non oxidative dehydrogenation, although hitherto underutilised industrially, has been put forward as a viable and green alternative, which is already used in a few commercial processes. In this work, we present detailed first principles calculations of this reaction over a chromium oxide catalyst, which is the cornerstone of the Catofin (r) process. A complete reaction pathway for the dehydrogenation of propane to propene and ultimately to propyne (methylacetylene) was considered. Cracking, which can yield C1 and C2 hydrocarbons, and the deactivation of the catalyst because of coking were also included and modelled. We used density functional theory calculations with the Hubbard model to study the structure of the involved intermediates, their adsorption and their interconversion to explain how chromium oxide catalysts facilitate this reaction and which processes cause their deactivation. We showed that the interaction of the hydrocarbons and molecular hydrogen with the catalytic surface is rather weak, resulting in low surface coverages, but increasing with multiple bonds present in hydrocarbons. Having constructed the potential energy surface with all the intermediates and the transition states linking them, we proposed a kinetic model for the reaction. Kinetic Monte Carlo simulations were performed at experimentally relevant temperatures (700 1000 degC) pressures (up to 10 bar) and inlet mixture compositions to study the kinetics of the reaction and discover the rate determining steps. As the reaction is highly endothermic, considerable conversions only occur at high temperatures. The accumulation of propene and propyne in the reaction mixture adversely affects the reaction rate and selectivity. Higher pressures increase the reaction rate but also increase the rate of coke formation, which poisons the catalyst. Deactivation of the catalyst has a strong temperature dependence and is caused by the accumulation of C* and CH3CC* on the surface, which are hard to remove even with hydrogen.
SMART CITIES: IMPACT OF RENEWABLE ENERGY CONSUMPTION, INFORMATION AND COMMUNICATION TECHNOLOGIES AND E GOVERNANCE ON CO2 EMISSION
Year: 2020

Renewable energy is not only cost effective but also plays vital role in decreasing the pollution. Most of the developing countries struggle with the energy supply for the cities. The introduction of renewable energy resources will not only meet the increased city demand of energy but also helps in making the smart city. Information and communication technologies help in efficient communication and work using different soft wares. The better the Information and communication technologies services in the country the better will be the chance of generating the smart cities in Asia. Emission of CO2 contributes to the pollution and thus, the global warming. If the government, make better policies and communicate it with e governance with the people of the country then there will be the chances of making the smart cities in the Asia. The aim of this study was to assess the impact of three dependent variables including state legitimacy, democracy and public services on the independent variables on the quality of the education. The data was collected from ten different Asian countries including are incudes Pakistan, India, China, Bangladesh, Nepal, Afghanistan, Iran, Turkey, Maldives, Indonesia, Saudi Arab, Kazakhstan, Syria, Qatar and Iraq. The timeline of the study is 26 years from 2019 and the data was collected from The Economic Form, The World Bank reports as well as the Transparency International. The analysis was performed by using IBM SPSS and various statistical tests were applied including LLC, cointegration test diagnostic checks, correlation matrix and PCSE estimation test and sysGMM. The results of our study showed positive association of dependent variables on the formation of smart cities in Asia. The results of our study have social, political and economic implications.
Factorization Machines with libFM
Venue: TIST
Year: 2012

Factorization approaches provide high accuracy in several important prediction problems, for example, recommender systems. However, applying factorization approaches to a new prediction problem is a nontrivial task and requires a lot of expert knowledge. Typically, a new model is developed, a learning algorithm is derived, and the approach has to be implemented. Factorization machines (FM) are a generic approach since they can mimic most factorization models just by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain. libFM is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC) This article summarizes the recent research on factorization machines both in terms of modeling and learning, provides extensions for the ALS and MCMC algorithms, and describes the software tool libFM.
The Real World Weight Cross Entropy Loss Function: Modeling the Costs of Mislabeling
Venue: IEEE Access
Year: 2020

In this paper, we propose a new metric to measure goodness of fit for classifiers: the Real World Cost function. This metric factors in information about a real world problem, such as financial impact, that other measures like accuracy or F1 do not. This metric is also more directly interpretable for users. To optimize for this metric, we introduce the Real World Weight Cross Entropy loss function, in both binary classification and single label multiclass classification variants. Both variants allow direct input of real world costs as weights. For single label, multiclass classification, our loss function also allows direct penalization of probabilistic false positives, weighted by label, during the training of a machine learning model. We compare the design of our loss function to the binary cross entropy and categorical cross entropy functions, as well as their weighted variants, to discuss the potential for improvement in handling a variety of known shortcomings of machine learning, ranging from imbalanced classes to medical diagnostic error to reinforcement of social bias. We create scenarios that emulate those issues using the MNIST data set and demonstrate empirical results of our new loss function. Finally, we discuss our intuition about why this approach works and sketch a proof based on Maximum Likelihood Estimation.
Improving Medical Code Prediction from Clinical Text via Incorporating Online Knowledge Sources
Venue: WWW
Year: 2019

Clinical notes contain detailed information about health status of patients for each of their encounters with a health system. Developing effective models to automatically assign medical codes to clinical notes has been a long standing active research area. Despite a great recent progress in medical informatics fueled by deep learning, it is still a challenge to find the specific piece of evidence in a clinical note which justifies a particular medical code out of all possible codes. Considering the large amount of online disease knowledge sources, which contain detailed information about signs and symptoms of different diseases, their risk factors, and epidemiology, there is an opportunity to exploit such sources. In this paper we consider Wikipedia as an external knowledge source and propose Knowledge Source Integration (KSI) a novel end to end code assignment framework, which can integrate external knowledge during training of any baseline deep learning model. The main idea of KSI is to calculate matching scores between a clinical note and disease related Wikipedia documents, and combine the scores with output of the baseline model. To evaluate KSI, we experimented with automatic assignment of ICD 9 diagnosis codes to the emergency department clinical notes from MIMIC III data set, aided by Wikipedia documents corresponding to the ICD 9 codes. We evaluated several baseline models, ranging from logistic regression to recently proposed deep learning models known to achieve the state of the art accuracy on clinical notes. The results show that KSI consistently improves the baseline models and that it is particularly successful in assignment of rare codes. In addition, by analyzing weights of KSI models, we can gain understanding about which words in Wikipedia documents provide useful information for predictions.
Parallel Algorithms for Multirelational Data Mining: Application to Life Science Problems
Venue: Resource Management for Big Data Platforms
Year: 2016

Data Mining (DM) algorithms are able to construct models from available data that can be very useful for both business and science. However, a powerful representation language is required to express the highly complex models that stem from structured data. Multirelational algorithms can then take advantage of this representation for both data and models. The drawback is that for very large or highly complex domains multirelational algorithms may require long running times. This problem can be substantially reduced using parallel implementations. In this chapter, we present a survey on parallel approaches to run Inductive Logic Programming (ILP) a flavor of multirelational algorithms. We also analyze different scheduling approaches for those implementations and describe two applications where the proposed approaches may be very useful.
A Status of Universal Design in Danish Architectural Policies.
Venue: Studies in health technology and informatics
Year: 2018

In 2009, Denmark ratified the Convention on the Rights of Persons with Disabilities and thereby acknowledged that Denmark, through policies, plans and programmes, is to work for equal opportunities for persons with disabilities and promote Universal Design in the development of standards, guidelines and e.g. architectural policies. An architectural policy is an acknowledged tool for enhancing the quality of buildings and their surroundings. An important quality of architecture is the ability to include everybody, regardless of gender, sexuality, ethnicity, culture or disability. In 1994, 2004 and again in 2014, Denmark published a national architectural policy. Inclusion as an architectural quality was included, but the focus and terminology changed with every new policy. Based on a desk study about architectural policies, this paper presents how Universal Design is included in Danish municipal architectural policies. On a local level, 34 of 98 Danish municipalities have formulated an architectural policy. None of the architectural policies applies Universal Design, but a few operate with the concept of accessibility, primarily focusing on accessibility for all. This paper discusses how the absence of Universal Design in the architectural policies can be interpreted and how Universal Design can be introduced into future policies.
Research progress of graphene based nanomaterials for the environmental remediation
Year: 2020

Graphene is a two dimensional nanomaterial with huge surface area, high carrier mobility and high mechanical strength. Because of its great potential in nanotechnology and environmental protection, it has attracted much attention in environmental and energy fields since its discovery in 2004. Although graphene is a star material, many reviews have introduced its use in terms of energy, the research progress in the field of environment, especially water pollution control, has been rarely reported. Here, we review exhaustively the research progress of graphene based materials in environmental pollution remediation in the past ten years. Firstly, the advantages and classification of graphene were introduced. Secondly, the research progress and main achievements of graphene and its composites in the fields of photocatalytic degradation, pollutant adsorption and water treatment were emphatically described, and the mechanism of action in the above fields was summarized. Finally, we discuss the problems existing in the preparation and summarize the application of graphene in the environment.
Invariant Risk Minimization Games
Venue: ICML
Year: 2020

The standard risk minimization paradigm of machine learning is brittle when operating in environments whose test distributions are different from the training distribution due to spurious correlations. Training on data from many environments and finding invariant predictors reduces the effect of spurious features by concentrating models on features that have a causal relationship with the outcome. In this work, we pose such invariant risk minimization as finding the Nash equilibrium of an ensemble game among several environments. By doing so, we develop a simple training algorithm that uses best response dynamics and, in our experiments, yields similar or better empirical accuracy with much lower variance than the challenging bi level optimization problem of Arjovsky et al. (2019) One key theoretical contribution is showing that the set of Nash equilibria for the proposed game are equivalent to the set of invariant predictors for any finite number of environments, even with nonlinear classifiers and transformations. As a result, our method also retains the generalization guarantees to a large set of environments shown in Arjovsky et al. (2019) The proposed algorithm adds to the collection of successful game theoretic machine learning algorithms such as generative adversarial networks.
Fuzzy Based Implementation of Multi Keyword Ranked Search over Encrypted Cloud Data in Secure Cloud Environment
Year: 2017

Cloud computing enables the paradigm of data outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service is a challenging task. Even though searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. Hence it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to the keywords. The works on searchable encryption focused on single keyword search or Boolean keyword search and the result produced by them are rarely sorted. An effective method proposed for this challenging problem is privacy preserving search over encrypted cloud data. This method establishes a set of strict privacy requirements for such a secure cloud data utilization system through MRSE. Among various multi keyword semantics, this method chooses the efficient similarity measure of coordinate matching| Then according to Top K Query method the sorted results are produced. The privacy is preserved by the chunk of data stored in a various server's. Then further improvisation is taken to introduce low overhead on computation and communication in future. ; top k query; encryption; MRSE
Kisisel Verilerin Korunmasi Kanunu Ve Turk Ceza Kanunu Baglaminda Kisisel Verilerin Ceza Normlariyla Korunmasi (Protection of Personal Data with Criminal Norms in the Context of Protection of Personal Data Law and Turkish Criminal Code)
Year: 2016

Belirli veya kimligi belirlenebilir gercek kisiye iliskin tum veriler olarak tanimlanabilecek kisisel verilerin korunmasi, insan haklarindan olan ozel hayat ve aile hayatina saygi hakki bakimindan onem arz eder. Kisisel verilerin korunmasi konusu gunumuzde gittikce onem kazanmakta, hem bireyler gunluk yasamlarinda bu konuda pek cok yakinmada bulunmakta hem de konu ust duzey yargi organlarinin hukuksal tavir almalarina neden olmaktadir. Bu dogrultuda Avrupa Konseyi ve Avrupa Birligi basta olmak uzere pek cok ulusal ustu orgut ve devlet tarafindan kisisel verilerin korunmasi alani duzenlenmistir. Turkiye, gec kalinmis olsa da bu alani duzenleyen 6698 sayili Kisisel Verilerin Korunmasi Kanunu'nu kabul etmistir. Kisisel verilerin ceza normlariyla korunmasi ise Turk Ceza Kanunundaki suc tipleriyle saglanmaktadir. Bu baglamda ele alinmasi gereken suc tipleri: TCK 135 138. maddelerde duzenlenen Kisisel Verilerin Kaydedilmesi sucu, Verileri Hukuka Aykiri Olarak Verme Veya Ele Gecirme sucu ve Verilerin Yok Edilmemesi sucu ve 6698 sayili Yasanin 17/2 maddesinde duzenlenen Kisisel Verilerin Silinmemesi veya Anonim Hale Getirilmemesi sucudur.  Protection of personal data, which can be defined as all kind of information relating to an identified or identifiable real person, is important in respect of the right to privacy and the right to respect for family life. The issue of protecting personal data is becoming more and more important nowadays; both individuals have many complaints arising from this subject in daily life and high level judicial organs take a legal attitude in this manner. In this direction, the field of the protection of personal data has been regulated by many supra national organizations especially the Council of Europe and the European Union and states. Turkey has accepted the Law numbered 6698 on the Protection of Personal Data which regulates this area, albeit too late. Protection of personal data by criminal norms is provided by special provisions in the Turkish Criminal Code. The types of crime that should be addressed in this context are: The offense of Recording Personal Data, the offense of Illegal Delivery or Acqusition Of Personal Data and the offense of Non Destruction of Data held in articles 135 138 of Turkish Criminal Code and the offense of Non Destruction or Non Anonymization of Personal Data held in article 17/2 of the Law numbered 6698.
Whatever Happened to the International Convention Against Doping in Sport: The United States Ratified It, But Then What?
Year: 2017

In April of 2008 the United States ratified the UNESCO International Convention against Doping in Sport. The Convention addresses national and international level activities, education and research, and monitoring related to doping. The ratification raised questions about the potential impact on sport entities in the United States because the Convention directs member nations to take steps, including legislative, to combat doping. However, since ratification the United States' compliance level with the Convention had remained unchanged at 78.6% until 2015, when it increased to 89.3% The past lower overall compliance was due to low compliance levels in several categories, including: Financial Measures; Professional Codes of Conduct;  All of these areas are dealt with by private entities in the United States and the government takes little to no active role. Due to constitutional concerns, the government is unlikely to get involved in these areas in the future, thus depending on improvements by private entities, leaving the United States unable to fully comply with the