Similarity Searching

Plagiarism is not allowed in the research journal. Therefore the similarity of research journal must be checked. Similarity is usually checked manually, so it needs long time for verification. The purpose of this research is to create a software for detecting similarity of research journal. Journal plagiarism detection software created in this research is done by implementing sherlock algorithm. Sherlock algorithm can detect document similarity by comparing similarity of each sentence inside a document with each other sentence in other documents. Sentence similarity detection is based on the same shared keyword between compared sentences. Result of test concluded that this software can detect similarity of research journal.

Mathematical modeling is an important step for developing many advanced technologies in various domains such as network security, data mining and etc…  This lecture introduces a process  that the speaker summarizes from his past practice of mathematical modeling and  algorithmic solutions in IT industry, as  an applied mathematician, algorithm specialist or software engineer , and even as an  entrepreneur.  A practical problem from DLP  system will be used as an example for creating math models and providing algorithmic solutions.

Similarity digesting is a class of algorithms and technologies that generate hashes from files and preserve file similarity.  They find applications in various  areas across security industry:  malware variant detection, spam filtering, computer forensic analysis, data loss  prevention and etc..  There are a few schemes and tools available that include ssdeep, sdhash and TLSH.  While being useful for detecting file similarity,  they define  similarity from different perspectives.  In other words, they take different approaches to describe what file similarity is about.  In order to compare those tools with better evaluation,  we introduce  a simple mathematical model to describe similarity that would cover  all three  schemes and beyond.  This model enables us to establish a theoretic framework  for  analyzing essential differences of  various similarity digesting algorithms & tools. As a result, a few tools are found to be complementary to each other so that we can use them in a hybrid approach in practice.  Data experiment results are provided to support the theoretic analysis.  In addition, we introduce a novel similarity digesting scheme that were designed based on the mathematical model.

This is one study about design of ornaments, as cultural  phenommena in the pre-writting Neolithic Vinca culture (Vinča), 6500–4500BC, Serbia, Balkan, EU. Text theorize Vinca culture ornamental motifs – vibration and swirl, linear or mandala formed, drawn in geometrically stile: straight lines at a sharp angle or at an angle of 90 degrees. As design, it is skillfully reduced, abstract thinking, simplifying things. Vinca Neolithic Society was good organized, big and creative, urbanized in scale of big villages, had cults and religion, shrines, and woman’s priestess and goddess (most woman ceramic figurines – idols, was fond, only few man). Vinca also began with industry ceramic production, mining and technology of copper casting as early as 7000 – 5000 BC, and trading (salt, jewelry, shells). Mysteriously, this society disappeared 4500 BC, never becoming “big civilization”.

Fake news is considered one of the main threats to the global information revolution in the world. They tend to cause serious damages on the reputation of persons or organizations, they also tend to cause many indirect effects on other aspects depending on the type of fake news. Fake financial news represents one of the serious types of fake news; their effects can cause direct and serious damages to the stock market. They have also another aspect that makes them more difficult to detect, such as the difficulty of detection due to similarities in the form with the real news, and the difficulty of checking numbers meanings when the news include specific numerical facts. However, they share common features with other fake news types. In this work, we present five models to detect fake financial news using sentiment analysis, news sources checking, objectivity check, checking against existing news and a fact-checking method. Datasets have been created especially for this project in addition to the online available data sources. Sentiment analysis model has been done using deep learning model in and it has achieved 87% accuracy, while the objectivity check has not achieved significant results. News sources analysis problem has been dealt with as a traditional term frequency problem, the solution achieved 94% accuracy value. Due to the lack of enough data sources, the fact-checking solution has ended up in creating a dataset that is ready for fact-checking against any relational dataset of periodic values such as the stock market. Finally, the similarity check against existing news has achieved 76% accuracy value.

Abstract
This research is intended to make a coding technique with a function similar to a plagiarism detector similar_text as text. By using the text function is similar, this research resulted in a detection of the document against the 10 (ten) of selected journal with, the document is the document with extension doc, docx, pdf, and txt. The document will be converted into html form and would henceforth be done making the string with the marker dots (.) And the comma (,) will be fetched a new string. By specifying a percentage string similarity of 90% then produced a text, in this case plagiarism is detected journal journal 1 with string similarity percentage of 48%. The use of similar texts can be classified as coding techniques for detection of plagiarism on its anti plagiarism detection applications..

Abstrak
Penelitian ini dimaksudkan untuk membuat sebuah teknik pengkodean dengan fungsi mirip similar_text sebagai pendeteksi plagiat sebuah teks. Dengan menggunakan fungsi teks yang serupa, penelitian ini menghasilkan sebuah deteksi dokumen terhadap 10 (sepuluh) jurnal yang dipilih dengan bahasa indonesia, dokumen yang merupakan dokumen dengan ekstensi doc, docx, pdf, dan txt. Dokumen tersebut akan dikonversi ke dalam bentuk html dan selanjutnya akan dilakukan pembuatan string dengan penanda titik (.) Dan koma (,) akan terambil sebuah string baru. Dengan menentukan persentase kemiripan string sebesar 90% maka dihasilkan sebuah teks, pada kasus ini jurnal yang terdeteksi plagiat adalah jurnal 1 dengan persentase kemiripan string sebesar 48%. Penggunaan teks serupa dapat digolongkan sebagai teknik pengkodean untuk deteksi plagiarisme pada penggunaaan aplikasi deteksi anti plagiat.

Popular methods for 3D protein structure similarity searching, especially those that generate high quality alignments such as Combinatorial Extension (CE) and Flexible structure AlignmenT by Chaining Aligned fragment pairs allowing Twists (FATCAT), are still very time-consuming. As a consequence, performing similarity searching against large repositories of structural data requires increased computational resources that are not always available. Cloud computing provides huge amounts of computational power that can be provisioned on a pay-as-you-go basis. We have developed the cloud-based system that allows scaling of the similarity searching process vertically and horizontally. Cloud4Psi (Cloud for Protein Similarity) was tested in the Microsoft Azure cloud environment and provided good, almost linearly proportional acceleration when scaled out onto many computational units. Availability: Cloud4Psi is available as Software as a Service (SaaS) for testing purposes at: http://cloud4psi.cloudapp.net/.

New link to  the project web site: http://zti.polsl.pl/w3/dmrozek/science/cloud4psi.htm

In the paper, we discuss the usage of constant and fuzzy similarity awards while establishing an optimal alignment between energy characteristics of two compared protein energy profiles. Single protein energy profile is a set of energy characteristics of various types of energy. The energy profile is determined for a given protein structure. We use these profiles to find protein molecules of the same structural protein family and inspect conformational modifications in their molecular structures as an effect of biochemical reactions or environmental influences. Energy profiles are received in the computational process based on the molecular mechanics theory. Afterwards, these profiles can be stored in the special purpose database (EDB) and used by the search engine to find similar fragments of protein structures on the energy level. To optimize the alignment path we use modified, energy-adapted Smith-Waterman method with one of the tested similarity awards.

Secondary structure representation of proteins provides important information regarding protein general construction and shape. This representation is often used in protein similarity searching. Since existing commercial database management systems do not offer integrated exploration methods for biological data e.g. at the level of the SQL language, the structural similarity searching is usually performed by external tools. In the paper, we present our newly developed PSS-SQL language, which allows searching a database in order to identify proteins having secondary structure similar to the structure specified by the user in a PSS-SQL query. Therefore, we provide a simple and declarative language for protein structure similarity searching.

Wacana kisah Adam memiliki kaitan erat dengan konsep asal-usul manusia pertama. Mayoritas masyarakat, baik muslim atau pun non-muslim bersepakat bahwa Adam merupakan manusia pertama yang hidup di dunia. Keberadaannya sebagai manusia pertama berarti menyimpan banyak rahasia terkait jati diri manusia dan menjadi fenomena utama dalam memotret kehidupan manusia. Berbagai tokoh, muslim maupun non-muslim telah bangkit untuk meneliti wacana tersebut, bahkan sampai abad ke 20 M, wacana ini masih menjadi perdebatan panas di kancah akademik. Faktanya, pada abad tersebut, wacana manusia pertama menjadi wacana teologis serta kontroversial dengan wacana Barat yang sedang mengalami perkembangan di bidang sains. Apalagi wacana yang ada di Barat perkembangannnya dipengaruhi oleh filsafat positivisme, sebuah filsafat yang mendambakan rasionalitas ilmu pengetahuan. 
Dengan berlangsungnya wacana manusia pertama sampai Abad 20 M, para tokoh Islam yang hidup di era sains juga menuangkan pemikirannya. Mereka meneliti wacana ini berangkat dari Al-Quran, yang 14 abad sebelumnya sudah membicarakannya. Di sini, posisi mereka adalah untuk membuktikan bahwa Al-Quran tetap relevan dengan dunia kontemporer. Al-Maraghi dan Bint al-Sya>t}i> adalah mufassir modern yang pernah berbicara tentang masalah ini. Dengan semangat bahwa Al-Quran mampu menjawab tantangan zaman, kedua mufassir tersebut memperlihatkan mentalitas-mentalitas keilmuan dengan memperkenalkan corak, metode serta pendekatan baru dalam mengkaji Al-Quran. 
Walaupun Al-Maraghi dan Bint al-Sya>t}i> hidup sezaman, namun keduanya memiliki gaya pembacaan atau penafsiran yang berbeda terhadap Al-Quran. Penafsiran Al-Maraghi memiliki corak adabi> ijtima>’i, sedangkan penfasiran Bint al-Sya>t}i> memiliki corak sastrawi. Dalam penafsirannya, mereka mencoba keluar dari gaya penafsiran ulama sebelumnya. Mereka menafsirkan kisah Adam bukan dalam bentuk data sejarah yang hanya memaparkan data-data sejarah secara naratif an sich, tetapi juga memaparkan problematika-problematika kemanusiaan. Selain itu, yang menonjol dari penafsiran mereka adalah banyaknya pemaparan nilai-nilai penting yang terkandung dalam Al-Quran tanpa memasukkan isra>i>liyya>t.
Secara garis besar sistematika penafsirannya menempuh dua langkah. Pertama respon akademik, yang merupakan tanggapan dan kritik terhadap wacana yang tengah berkembang. Kedua, praktek penafsiran terhadap ayat-ayat terkait. Dalam penelitan ini penulis konsen pada Q.S. Al-Baqarah ayat 31-39. Dari ayat tersebut, penulis menemukan 9 sub pembahasan yang tercakup dalam dua pokok pembahasan. Pertama, Penciptaan Khalifah dan Respon Malaikat. Kedua, Pembelajaran atas Adam dan Kehidupan di Surga.

In this paper we are trying to combine interests of authors and collaborators into one non-mutually exclusive concept and logic flow aiming to create a framework for searching of a migration of words from one cluster to another; this enables us to define a semantic shift well before it become obvious. We show how introduced graph-logic model can be applied for analysis of migration of the meaning of sentences indicating, quite often, a paradigm shifts. Using Artificial Intelligence as an example we illustrate development of AI from philosophy of mind to science, science fiction and technology, including games in science, technology, and further education. Several examples how proposed model with supportive searching framework applied in mentioned areas detecting evolving processes are presented.

The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather general case where the similarity criterion defines a metric space, instead of the more restricted case of a vector space. Many solutions have been proposed in different areas, in many cases without cross-knowledge. Because of this, the same ideas have been reconceived several times, and very different presentations have been given for the same approaches. We present some basic results that explain the intrinsic difficulty of the search problem. This includes a quantitative definition of the elusive concept of "intrinsic dimensionality." We also present a unified view of all the known proposals to organize metric spaces, so as to be able to understand them under a common framework....

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes

This study aims at modeling the semantic similarity between metaphor terms by means of a distributional method based on a Big Data stream: Flickr tags. As explained in the article, this distributional model, Flickr Distributional Tagspace (FDT), captures primarily relational similarity between concept pairs, that is, between tags that appear in similar tagsets (and therefore in similar pictures). A long established view in metaphor theory claims that metaphors pertain to the conceptual dimension of meaning, but while different models aim at explaining how language constructs and represents metaphorical conceptual structures, we still know very little about how other modalities (for example, images) achieve metaphor construction and expression. A comprehensive theory, which argues in favor of the conceptual nature of metaphor, cannot afford to be biased toward the analysis and modeling of one specific modality of expression, thus neglecting potential modality-specific differences. The present study, conducted through FDT, found that visual and linguistic metaphors behave differently, in that the similarity between two aligned concepts in a visual metaphor appears to be significantly higher than the similarity between two concepts aligned in a linguistic metaphor (which, in turn, does not differ substantially from the similarity between two randomly paired concepts). These findings suggest that the relational similarity between two metaphor terms (captured and modeled through FDT) is crucial for visual metaphors but not for linguistic metaphors. An additional content analysis, also reported here, shows that the type of semantic information encoded in the related tags (i.e., the contexts on which the contingency matrices of this distributional method are built) differs, in relation to the modality of the metaphor: while situation-related and entity-related features are typically associated with concepts aligned in visual metaphors, introspections, and taxonomic features are typically associated with concepts aligned in linguistic metaphors.

Fake news is considered one of the main threats to the global information revolution in the world. They tend to cause serious damages on the reputation of persons or organizations, they also tend to cause many indirect effects on other aspects depending on the type of fake news. Fake financial news represents one of the serious types of fake news; their effects can cause direct and serious damages to the stock market. They have also another aspect that makes them more difficult to detect, such as the difficulty of detection due to similarities in the form with the real news, and the difficulty of checking numbers meanings when the news include specific numerical facts. However, they share common features with other fake news types. In this work, we present five models to detect fake financial news using sentiment analysis, news sources checking, objectivity check, checking against existing news and a fact-checking method. Datasets have been created especially for this project in addition to the online available data sources. Sentiment analysis model has been done using deep learning model in and it has achieved 87% accuracy, while the objectivity check has not achieved significant results. News sources analysis problem has been dealt with as a traditional term frequency problem, the solution achieved 94% accuracy value. Due to the lack of enough data sources, the fact-checking solution has ended up in creating a dataset that is ready for fact-checking against any relational dataset of periodic values such as the stock market. Finally, the similarity check against existing news has achieved 76% accuracy value.

The application of data mining (DM) in healthcare is increasing. Healthcare organizations generate and collect large voluminous and heterogeneous information daily and DM helps to uncover some interesting patterns, which leads to the manual tasks elimination, easy data extraction directly from records, to save lives, to reduce the cost of medical services and to enable early detection of diseases. These patterns can help healthcare specialists to make forecasts, put diagnoses, and set treatments for patients in health facilities. This work overviews DM methods and main issues. Three case studies illustrate DM in healthcare applications: (i) In-Vitro Fertilization; (ii) Content-Based Image Retrieval (CBIR); and (iii) Organ transplantation.

We have been studying different kinds of papers about web pages clustering techniques with many
algorithms. Almost papers published in the Web are used popular algorithms. Among them similarity
methods and clustering methods are very popular algorithms for extraction data in the Web. We use some
methods to summarize by means of well known those methods. Some papers are applied to different
methods. It is motivated to collect and summarize what are differences between them. This paper presents a
comprehensive analysis of different methods and intends to be more recognized those methods. Most of
sections are well composed for detail explanations about papers studied as a list of numbers and a table.
Today web pages are saturated on the Internet. Content of web pages are extracted from cluster of web
page using related methods. Clustering systems are useful to extract knowledge. The results obtained show
that the status of using similarity and clustering method provides the best results for people who study
about web.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes.
v

In recent years, online social networks have become a part of everyday life for millions of individuals. Also, data analysts have found a fertile field for analyzing user behavior at individual and collective levels, for academic and commercial reasons. On the other hand, there are many risks for user privacy, as information a user may wish to remain private becomes evident upon analysis. However, when data is anonymized to make it safe for publication in the public domain, information is inevitably lost with respect to the original version, a significant aspect of social networks being the local neighborhood of a user and its associated data. Current anonymization techniques are good as identifying risks and minimizing them, but not so good at maintaining local contextual data which relate users in a social network. Thus, improving this aspect will have a high impact on the data utility of anonymized social networks. Also, there is a lack of systems which facilitate the work of a data analyst in anonymizing this type of data structures and performing empirical experiments in a controlled manner on different datasets. Hence, in the present work we address these issues by designing and implementing a sophisticated synthetic data generator together with an anonymization processor with strict privacy guarantees and which takes into account the local neighborhood when anonymizing. All this is done for a complex dataset which can be fitted to a real dataset in terms of data profiles and distributions. In the empirical section we perform experiments to demonstrate the scalability of the method and the improvement in terms of reduction of information loss with respect to approaches which do not consider the local neighborhood context when anonymizing.

MAS4PSi (Multi-Agent System For Protein Similarity searching) is a system that allows fast, scalable and reliable protein structure similarity searching. Protein structure similarity searching is crucial in conducting research on a variety of biological processes, and other areas that have their basis in these biological processes. The purpose of this article is to define and present possible scenarios of using the MAS4PSi system in a broadly understood medical diagnostics, both in the design of experiments supporting new research, as well as the typical diagnostic stage.

Tanulmányomban Heidegger Parmenidész-értelmezéseit elemzem abból a szempontból, hogy
vajon azok mennyiben hozhatók összefüggésbe a hermeneutikai szövegekből is ismeretes,
úgynevezett hasonlósági elv (hasonló a hasonlót érti meg) heideggeri értelmezéseivel.
Heidegger Parmenidész 3. fragmentumának értelmezéseit kronológiai sorrendben elemzem,
és így igyekszem végig követni vele azt a gondolkodói utat, amely végül a heideggeri
kontextusban lefordíthatatlan német Selbe-fogalomhoz és az összetartozás, összetartozóság
fogalmaihoz vezetett. Mindeközben, jelezni kívánom azokat a fordulatokat, elágazásokat is,
amelyek Heidegger mind Parmenidésszel, mind a hasonlósági elvvel kapcsolatos álláspontját
jellemzik, de azt a különbséget is, amely érzékelhetően azok heideggeri olvasata és az
úgynevezett idealista olvasat között húzódik. A tanulmány kevésbé explicit célkitűzése pedig,
hogy a hasonlósági elvvel kapcsolatos, jövőbeni, mélyebb számvetés előkészületeinél
segédkezzen.

In the present time, due to attractive features of cloud computing, the massive amount of data has been stored in the cloud. Though cloud-based services offer many benefits but privacy and security of the sensitive data is a big issue. These issues are resolved by storing sensitive data in encrypted form. Encrypted storage protects the data against unauthorized access, but it weakens some basic and important functionality like search operation on the data, i.e.  searching the required data by the user on the encrypted data requires data to be decrypted first and then search, so this eventually, slows down the process of searching. To achieve this many encryption schemes have been proposed, however, all of the schemes handle exact Query matching but not Similarity matching. While user uploads the file, features are extracted from each document. When the user fires a query, trapdoor of that query is generated and search is performed by finding the correlation among documents stored on cloud and query keyword, using Locality Sensitive Hashing.

—We introduce a new probabilistic proximity search algorithm for range and K-nearest neighbor (K-NN) searching in both coordinate and metric spaces. Although there exist solutions for these problems, they boil down to a linear scan when the space is intrinsically high dimensional, as is the case in many pattern recognition tasks. This, for example, renders the K-NN approach to classification rather slow in large databases. Our novel idea is to predict closeness between elements according to how they order their distances toward a distinguished set of anchor objects. Each element in the space sorts the anchor objects from closest to farthest to it and the similarity between orders turns out to be an excellent predictor of the closeness between the corresponding elements. We present extensive experiments comparing our method against state-of-the-art exact and approximate techniques, both in synthetic and real, metric and nonmetric databases, measuring both CPU time and distance computations. The experiments demonstrate that our technique almost always improves upon the performance of alternative techniques, in some cases by a wide margin.

Recent developments in computer science enable algorithms previously perceived as too time-consuming to now be efficiently used for applications in bioinformatics and life sciences. This work focuses on proteins and their structures, protein structure similarity searching at main representation levels and various techniques that can be used to accelerate similarity searches. Divided into four parts, the first part provides a formal model of 3D protein structures for functional genomics, comparative bioinformatics and molecular modeling. The second part focuses on the use of multithreading for efficient approximate searching on protein secondary structures. The third and fourth parts concentrate on finding 3D protein structure similarities with the support of GPUs and cloud computing. Parts three and four both describe the acceleration of different methods. The text will be of interest to researchers and software developers working in the field of structural bioinformatics and biomedical databases.

Most work in similarity search takes place in a domain far distant from the actual semantics of a space. Given a metric and a set of values, there is a wealth of research in how to efficiently perform search. However almost without exception, the aspect of mapping the results to the "real" closeness semantic the metric is intended to model is ignored. In part, this is often due to the lack of an unbiased ground truth, which is extremely difficult to establish in large collections. Within this context, large scale near-duplicate detection provides a realistic and challenging task on which different techniques can be compared. On one hand, it entails a subtle differentiation between actual near duplicates, and image pairs which are visually similar, but not semantically related. On the other hand, since the number of image pairs grows quadratically with the size of the collection, it requires both effective and computationally efficient search techniques. MirFlickr1M is a collection of one million images. Collected for research purposes as a benchmark for image tagging, the original selection of images was guided by certain factors, but no checking for similarity was performed at the time. By chance, however, a large number of near-duplicate clusters do occur, along with a small set of identical image clusters. These similar images exist for "natural" reasons. As an example, some are images of the same highly predictable subject and context (for example the moon); some are alterations of others within the collection after cropping, re-hueing etc., some are subsequent shots taken in quick succession from a single camera, etc. In the MirFlickr Near Duplicate (MFND) dataset, around 10,000 known similar clusters have been identified and checked by the proposers of this Session. There is strong statistical evidence that this is the large majority of all the near-duplicate images within the set. The remainder of the collection therefore contains over 10 11 visually similar, but not semantically related, image pairs. Hence, differentiating actual near-duplicate images from visually similar images provides a subtle and challenging task.

By increasing popularity of SOC, using Web services in applications has increased too. SOC creates a loosely coupled environment in which the actual execution environment might differ significantly from the one with the presupposed conditions during application design. Therefore, although an appropriate Web service might have been selected, by passing time, the Web service may not be efficient enough or may not be applicable under specific conditions. For service-oriented systems to be flexible and self-adaptive, it is necessary to automatically select and use a similar service instead of the one which causes the above mentioned problems. Finding a similar service means specifying the proper services which fulfill the same requirements as those fulfilled by the problematic service. In most of the previous works, a number of the best services (k) are selected and ordered based on functional similarity. The user must select one of these services based on his/her preferences. One important metric in selecting a similar service is considering QoS properties and user preferences about QoS. Because of the importance of this issue, in the present paper, an architecture is proposed in which, in addition to functional similarity, QoS properties and user preferences are also considered in selecting a similar service.

In the recent years, service-based approaches for sharing of data among repositories and online learning are rising to prominence because of their potential to meet the requirements in the area of high performance computing. Developing education based grid services and assuring high availability, reliability and scalability are demanding in web service architectures. On the other hand, grid computing provides flexibility towards aggregating distributed CPU, memory, storage, data and supports large number of distributed resource sharing to provide the full potential for education like applications to share the knowledge that can be attainable on any single system. However, the literature shows that the potential of grid resources for educational purposes is not being utilized yet. In this paper, an education based grid framework architecture that provides promising platform to support sharing of geographically dispersed learning content among universities is developed. It allows students, faculty and researchers to share and gain knowledge in their area of interest by using e-learning, searching and distributed repository services among universities from anywhere, anytime. Globus toolkit 5.2.5 (GTK) software is used as grid middleware that provides resource access, discovery and management, data movement, security, and so forth. Furthermore, this work uses the OGSA-DAI that provides database access and operations. The resulting infrastructure enables users to discover education services and interact with them using the grid portal.

Would this method for uncovering hidden objects, feature discovery, feature selection and supervised learning work? According to my understanding perfect prediction by computation optimization must be impossible as long as essential input training data features for supervised learning are still lacking. Therefore, I emphasize not to invest much effort into algorithmic computational optimization before the feature discovery process allows selecting all needed features through variation before realistic predictions can be achieved by enhancing computations and parameters. For me this simple progression from completed feature selection to optimized algorithm is essential for more rapid discoveries. That is why I am still wondering why I have seen so many papers focusing on improving computational predictions without any kind of prior considerations about having properly completed the absolutely essential exhaustive feature selection process without which no subsequent computations can lead to satisfactorily predictions, which are reasonably consistent with our experimental observations and measurements. I am afraid that I am still the only one, to whom the writing above can make sense. I had expected much more enthusiasm, excitement and optimism about very likely accelerating our discovery rate by first focusing on uncovering still hidden objects and features through diverse variations of conditions, procedures, methods, techniques and measurements, followed by the exhaustive selection for all relevant needed features, followed by designing, developing, combining and optimizing the computational steps of our machine learning algorithm until our predictions match our experimentally obtained observations. Once this major machine learning objective has been achieved we have reached its final status beyond which we cannot improve it unless we can generate conditions, which cause our previously perfectly predicting machine learning algorithm to obviously fails because it is an absolute prerequisite for discovering more relevant training reflected by more dimensions. According to my current understanding any newly discovered essential input data feature inevitably causes a rise in the dimensionality of input data components, which must be considered together but never in isolation. For example, if I train with 100 input features our input variable must consist of exactly 100 components or parts, which together form a new level of single measuring points, which tends to be much different in controlling its manipulations and their overall effects from anything, which could possibly get anticipated, when trying to add up the effects of its 100 parts to a new total. This new total tends to consist of much different dimensions and often refers to completely unrelated kind of data than when combining all 100 components consisting of exactly the same input values for every of its 100 variables but by considering all 100 components like a single indivisible unit of measurement points, which often results in completely different kinds of unrelated seeming properties/features, which are not even closely reflecting the results, which we'd obtain if we executed each dimension on its own in isolation and sequential order. For example, stopping translation prematurely by three consecutive tryptophans has a much different impact, i.e. stop, then when translating each of the tryptophan in isolation separated by other amino acids, since this causes the nascent polypeptide chain to grow. Each tRNA charged with tryptophan, which complements mRNA triplets, causes the peptide to grow by a single tryptophan, which gets added to it. So when you try 2 tryptophans the polypeptide grows by two amino acids. But when you try 3 consecutive tryptophans, then-counter-intuitively-instead of the expected growth by 3 amino acids-translation prematurely stops. Stopping translation prematurely is of a much different dimension, level, effect and data kind, then when keep adding more amino acids to the growing peptide chain. If we consider the effect of complementary binding of a tRNA to its mRNA codon our peptide grows by one amino acid any charged tRNA adds another amino acid of one of 20 categorical values. Normally no amino acid can cause the translation to step prematurely, not even two amino acids as a pair. But three amino acids as an indivisible triplet, which must be considered as a new single value requiring all three tryptophans to be sequentially to be present, like a single indivisible data block, which must not be divided only in triplets, because only triplets, but no pair or singlet, can stop translation prematurely. Another example is predicting overall cellular protein composition. It depends on how many mRNA strands coding for a particular protein are in the cytoplasm. There is proportionality between number of cytoplasmic mRNA strands and total protein. Therefore, if the cell needs to double protein abundance it could double transcription and keeps everything else the same. But a much better and less step intensive, more economic way of doubling protein concentration is to double the length of the poly-(A)-tail. Extending the length of the poly-(A)-mRNA-tail may require about 100 additional adenines whereas doubling transcription requires about at least 500-instead of only 100-new nucleotides in addition to all needed transcriptional modification steps with their elaborate synthesis machinery. If the dividing yeast must raise its lipid synthesis by more than 10-fold during the short M-phase, it could increase transcription by a factor of 10, it could make the poly-(A)-mRNA-tail 10 times longer, or it could synthesized 10 times more new ribosomes to increase the ribosomal translation by a factor of 10 simply by reducing the distance of free uncovered mRNA nucleotides between adjacent ribosomes translating running down the same mRNA strand. If more than one ribosome is translating the same mRNA strand simultaneously, it is called a poly-ribosome. Hence, having 10 times more ribosomes binding to the same mRNA strand at the same time increases translation by a factor of 10 without needing any additional transcription. Above I have given three easy examples to get 10 times more proteins. Although all 3 methods have the same final result, i.e. 10 times more proteins, their mode of action, their required essential features, their dimensions and their minimally required parts, which must be considered like a single value, are totally different.

Bytewise approximate matching algorithms have in recent years shown significant promise in detecting files that are similar at the byte level. This is very useful for digital forensic investigators, who are regularly faced with the problem of searching through a seized device for pertinent data. A common scenario is where an investigator is in possession of a collection of "known-illegal" files (e.g., a collection of child abuse material) and wishes to find whether copies of these are stored on the seized device. Approximate matching addresses shortcomings in traditional hashing, which can only find identical files, by also being able to deal with cases of merged files, embedded files, partial files, or if a file has been changed in any way. Most approximate matching algorithms work by comparing pairs of files, which is not a scalable approach when faced with large corpora. This paper demonstrates the effectiveness of using a "Hierarchical Bloom Filter Tree" (HBFT) data structure to reduce the running time of collection-against-collection matching, with a specific focus on the MRSH-v2 algorithm. Three experiments are discussed, which explore the effects of different configurations of HBFTs. The proposed approach dramatically reduces the number of pairwise comparisons required, and demonstrates substantial speed gains, while maintaining effectiveness.

In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product quality and safety, hydrologic and geologic observing systems, pollution management, and others.