6,840 research outputs found
Developing an in house vulnerability scanner for detecting Template Injection, XSS, and DOM-XSS vulnerabilities
Web applications are becoming an essential part of today's digital world. However, with the increase in the usage of web applications, security threats have also become more prevalent. Cyber attackers can exploit vulnerabilities in web applications to steal sensitive information or take control of the system. To prevent these attacks, web application security must be given due consideration.
Existing vulnerability scanners fail to detect Template Injection, XSS, and DOM-XSS vulnerabilities effectively. To bridge this gap in web application security, a customized in-house scanner is needed to quickly and accurately identify these vulnerabilities, enhancing manual security assessments of web applications.
This thesis focused on developing a modular and extensible vulnerability scanner to detect Template Injection, XSS, and DOM-based XSS vulnerabilities in web applications. Testing the scanner against other free and open-source solutions on the market showed that it outperformed them on Template injection vulnerabilities and nearly all on XSS-type vulnerabilities. While the scanner has limitations, focusing on specific injection vulnerabilities can result in better performance
Industry 4.0 And Short-Term Outlook for AEC Industry Workforce
Technology is uniquely transforming our society to a significant degree. This transformation has been described as Industry 4.0 and encompasses machine learning, computerization, automation, artificial intelligence, and robotics. Industry 4.0 is currently impacting the United States’ workplace and is projected in continue uniquely changing our society over the next twenty years or so. Looking specifically at the AEC industry, this paper researches how the AEC industry workplace could be impacted by Industry 4.0 over the next several years. The hypothesis that jobs more at risk for automation should see low or negative growth and lower wages over the next several years was tested by using U.S. Bureau of Labor Statistics (BLS) occupational wage data and growth projections to create an opportunity value for each occupation, and then evaluating the relationship between the opportunity value and probability of automation. A statistical significance was found between the two variables. The hypothesis that certain skills are particularly associated with high growth/high wage jobs versus low growth/low wage jobs was tested by scraping important skills/qualities from the individual occupational webpages hosted by the U.S. Bureau of Labor Statistics, and then comparing the approximately top 80% of skills scraped between the two groups. Certain skills/qualities were found to be particularly associated with each group. Finally, the occupations associated with the AEC industry were compared with the findings from the first two hypotheses. The discoveries were that the AEC industry is potentially more susceptible to Industry 4.0 than other industries. This research is of significance because research into how the AEC industry workplace will be impacted by Industry 4.0 over the next several years was not found in the research background, and it has implications on potential career choices, skill requirements, and areas of research and development. Recommendations for future work include utilizing new data sources, Monte Carlo simulations, cohort analysis, and cluster analysis to make more specific forecasts on Industry 4.0’s impact on the AEC industry.M.S
Identifying long non-coding RNA in the chicken transcriptome
The transcriptome remains a vast under explored space in genomics. Unlike the genome which is linear in nature, the use of alternative transcription start, end, and splicing sites in eukaryotes creates the possibility of near infinite differentially expressed RNA. While many expressed messenger RNA have been identified through the proteins that they produce, there is still very little known about the world of long non-coding RNA (lncRNA).
Long non-coding RNA are a vast unknown space and represent one of the largest frontiers of transcriptomics. While little is known about this class of RNA as a whole, there have been specific lncRNA which have been found to be crucial components of biological development. Given the characteristics of lncRNA there may also be a sub-class that is involved in cell differentiation and speciation. In order to explore lncRNA and generate high throughput predictions of their functions, I used the chicken as a model and applied comparative genomics using newly assembled genomes from other avian species.
Long non-coding RNA present the almost perfect scenario for evading detection from previous RNA discovery methods. They have been shown to be poorly conserved across species, with generally low expression levels and no downstream product that is immediately identifiable. Given these factors, previous RNA detection methods such as expressed sequence tags and RNA sequencing cannot provide reliable evidence for the mass identification of lncRNA.
In the first chapter I explore the characteristics of Iso-Seq (Pacific Biosciences long read RNA sequencing technology) and methods for processing the data to improve long non-coding RNA identification. I also explore the use of non-traditional cDNA library preparation methods including cDNA normalization and 5’ cap selection. I found that the ability of long read RNA sequencing to provide full length transcript sequences allows for more robust methods of lncRNA prediction.
In the second chapter, I explore the data processing of long reads. I use a dataset generated by Pacific Biosciences using the Universal Human Reference RNA as an example of ideal long read data. By using data based on the human transcriptome, I was able to compare my results with information from one of the most well annotated and studied transcriptomes. I demonstrate the Transcriptome Annotation by Modular Algorithms (TAMA) software that I developed and how it can be used to explore the non-coding RNA within the transcriptome.
In the third chapter, I explore the transcriptome constructed from Iso-Seq data on different chicken tissue samples. I used the TAMA software along with other tools to make pipelines optimized for lncRNA discovery and to perform functional annotation. Using these methodologies I identified over 300,000 putative transcript models corresponding to over 50,000 genes. Of these over 100,000 transcript models appear to be lncRNA which correspond to over 38,000 gene loci. The majority of these are predicted as sense exonic and mono-exonic lncRNA. While it will require further investigation to produce sufficient evidence that these RNA are not the result of transcriptional noise, I have identified a subset of these which appear to have functional importance given their co-expression with known genes.
I demonstrate that while lncRNA appear to be generally lowly expressed, they often express in a tissue-specific manner which suggests a possible role in tissue differentiation.
From these investigations, I have found that there are potentially thousands of unannotated lncRNA within the chicken transcriptome with characteristics that require new technologies such as long read sequencing to identify.
These novel lncRNA include a subset which could have functional roles in the regulation of cell differentiation
The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning
Suppose we are given access to independent samples from distribution
and we wish to output one of them with the goal of making the output
distributed as close as possible to a target distribution . In this work
we show that the optimal total variation distance as a function of is given
by over the class of all pairs with a
bounded -divergence . Previously, this question was
studied only for the case when the Radon-Nikodym derivative of with
respect to is uniformly bounded. We then consider an application in the
seemingly very different field of smoothed online learning, where we show that
recent results on the minimax regret and the regret of oracle-efficient
algorithms still hold even under relaxed constraints on the adversary (to have
bounded -divergence, as opposed to bounded Radon-Nikodym derivative).
Finally, we also study efficacy of importance sampling for mean estimates
uniform over a function class and compare importance sampling with rejection
sampling
Analysis, Design, and Implementation of a training center for variable-speed drive assembly production : Case ABB Oy
In manufacturing constant developments in production, processes, and layouts are required to respond towards increased production volume, quality, and customer requirements while meeting production targets and objectives. The case company of this thesis is ABB Ltd Drives Manufacturing Unit, which specializes in variable-speed drive production. ABB has recognized the need for re-designing a new and effective training center that supports One-piece flow assembly production since the old model is based on a cell production method. The training center is used for the training and integration of the company's new and experienced assemblers.
The aim of the research is to analyze the current training concept, design a new technical solution, and create a detailed implementation plan. Thus, the following research questions were developed: RQ1: How to develop and re-design a training center that supports the assembler for One-piece flow method production of variable-speed drives? RQ2: How to design and create the best possible layout and solution to guarantee safety, flexibility, ergonomics, clear flow, and the maximum utilization of space? RQ3: How to implement a training center that does not disrupt the main production lines and makes that way operations more efficient?
To achieve the objectives, the waste, bottlenecks, and issues of the current design were first identified by observing the training process and organizing focus groups and workshops with the production line and logistics (customer), and with the project team. Work-time studies were also conducted to solve the flow, outputs, cycle time, and waste time of the current process. These data collection methods aided in identifying potential improvement opportunities for the new design. The layout design process was committed by utilizing Lean principles and the Systematic layout planning procedure. AutoCAD was used to create and map various layout structures, options, and alternatives. The design process required the tendering of two layout location options, which were solved using the quantitative multiple attribute decision-making method, Weighted decision matrix (WDM), with voting based on the scoring of various criteria and features.
The result was a Flexible 6-phase U-model one-piece flow training center that allows assemblers to be trained in both one-piece flow and cell production methods. The new design's scope of work was delivered to the supplier, numerous negotiations were held to achieve the best final solution, and the new training center was ordered. In the end, a detailed implementation plan with an estimated schedule was created and a future action list was established. The new design fulfils the objectives and eliminates all issues, waste, and bottlenecks while also ensuring safety, ergonomics, flexibility, a clear flow, and a high-quality training process. With the new design, the efficiency, quality, and output of training and production operations will improve.Teollisuuden alalla tuotantojärjestelmiä, prosesseja ja layouteja on jatkuvasti kehitettävä sekä
modifioitava reagoidakseen kasvaneisiin tuotantomääriin sekä laatu- ja asiakasvaatimuksiin ja
saavuttaakseen asetetut tuotantotavoitteet ja päämäärät. Tämän opinnäytetyön toimeksiantaja
on ABB Oy Drives Manufacturing -yksikkö, joka on erikoistunut taajuusmuuttajatuotantoon. Toimeksiantaja on tunnistanut tarpeen uuden ja tehokkaamman koulutuslinjan suunnitteluun One-piece flow malliseen taajuusmuuttajien kokoonpanotuotantoon, sillä vanha tuotantomalli perustuu solutuotantomenetelmään. Koulutuslinjaa käytetään niin uusien kuten jo talossa olevien vanhojen kokoonpanoasentajien koulutukseen ja integrointiin.
Tutkimuksen tavoitteena on analysoida nykyinen koulutuskonsepti, suunnitella uusi tekninen
ratkaisu ja laatia yksityiskohtainen implementointisuunnitelma. Tavoitteiden saavuttamista varten on kehitetty seuraavat kolme tutkimuskysymystä: RQ1: Kuinka kehittää ja suunnitella koulutuslinja, joka tukee asentajia One-piece flow malliseen kokoonpanotuotantoon? RQ2: Miten suunnitella ja luoda paras mahdollinen layout ja ratkaisu, joka takaa turvallisuuden, joustavuuden, ergonomian, selkeän virtauksen ja maksimaalisen tilankäytön? RQ3: Kuinka implementoida koulutuslinja, joka ei häiritse päätuotantolinjoja ja tehostaa siten operaatioiden tehokkuutta?
Saavuttaakseen tavoitteet, nykyisen koulutuskonseptin aiheuttamat pullonkaulat, ongelmat ja
hukka tunnistettiin ensin havainnoimalla koulutusprosessia ja järjestämällä haastatteluja sekä
työpajoja tuotantolinjan ja logistiikan (asiakkaan) sekä projektiryhmän kanssa. Nykyisen prosessin virtauksen, ulostulon, tahti -ja hukka-ajan selvittämiseksi suoritettiin myös työaikatutkimuksia. Nämä tiedonkeruumenetelmät auttoivat kehitysmahdollisuuksien tunnistamisessa uutta ratkaisua varten. Layout suunnitteluprosessi toteutettiin Lean-periaatteita ja systemaattista layout suunnittelua käyttäen. AutoCAD layout suunnittelusovellusta käytettiin erilaisien asettelurakenteiden ja vaihtoehtojen luomiseen sekä kartoittamiseen. Suunnitteluprosessi edellytti kahden layout-sijaintivaihtoehdon kilpailuttamista. Lopputulos ratkaistiin äänestämällä kvantitatiivisen päätöksentekomatriisin (WDM) avulla, joka perustui eri kriteerien ja ominaisuuksien pisteytykseen. Tulokseksi saatiin joustava 6-vaiheinen U-mallinen One-piece flow koulutuslinja, jonka avulla asentajia voidaan kouluttaa sekä One-piece flow että solutuotantomallisesti. Uuden koulutuslinjan työn laajuus -dokumentti toimitettiin toimittajalle sekä lukuisia neuvotteluja käytiin parhaan loppuratkaisun saavuttamiseksi, jonka jälkeen uusi koulutuslinja tilattiin. Lopuksi koostettiin yksityiskohtainen implementointisuunnitelma arvioituineen aikatauluineen ja laadittiin toimenpidelista tulevaisuutta varten. Uusi ratkaisu täyttää asetetut tavoitteet ja eliminoi kaikki ongelmat, hukat ja pullonkaulat sekä takaa turvallisuuden, ergonomian, joustavuuden, selkeän virtauksen ja laadukkaan koulutusprosessin. Uuden ratkaisun myötä koulutuksen ja operaatioiden tehokkuus, laatu ja tuottavuus paranevat
Gamma-Ray Spectroscopy of Neutron-Deficient Nuclides 129Nd, 131Pm and 132Pm
The newly commissioned MARA recoil separator has been coupled with the efficient high-purity germanium (HPGe) JUROGAM γ-ray spectrometer and a suite of focal-plane detector systems to facilitate detailed studies of in-beam and isomeric delayed radiation emitted by various types of nuclides. Excited quantum states were populated in the highly neutron-deficient nuclides 129Nd, 131Pm and 132Pm utilising the fusion-evaporation reaction 58Ni + 78Kr > 136 Gd∗.
In the study of 129Nd, three new isomeric states were observed at excitation energies of 1893, 2109 and 2284 keV, respectively. The state at 2284 keV was measured to have a half-life of 679 ± 60 ns. Excited states existing above this level were measured using the JUROGAM spectrometer and characterised within the framework of the cranked shell model.
The study of 131Pm focused on detailed in-beam γ-ray measurements, resulting in extensions to the yrast band at high and low spin. To accommodate the lowest spin states, reinterpretation of the band in terms of its deformation aligned nature resulted in reassignment of the yrast band to Nilsson orbital [532]5/2−. The lowest spin state of 5/2− is then proposed to be the ground state, in agreement with theoretical studies.
Finally, band extensions at high and low spin were made in the study of doubly odd 132Pm. Two low-spin isomeric states were measured, with half-lives of 187 ± 4 ns and 19.9 ± 0.5 μs. γ radiation observed to depopulate these states is proposed to feed the ground state of the this nucleus, allowing unambiguous assignment of absolute excitation energies for two of the four observed bands
Emotion in abolitionist literature during the British slavery debate, 1770-1833
Emotive rhetoric was a significant element of British abolitionist literature in the late-eighteenth and early-nineteenth centuries. Abolitionists argued that Black and White people experienced the same emotions, though differences emerged in the way that those emotions were expressed, which was somewhat dictated by contemporary social norms. The argument of emotional equality aimed to encourage the British public to sympathise with the emotional – rather than just the physical – suffering endured by enslaved individuals, in the hope that this would inspire abolitionist action. In this thesis, I argue that emotive rhetoric was used not only to portray the humanity of enslaved individuals, but also to encourage British readers to demonstrate their own humanity, emotional sensibility, and morality by campaigning to abolish first the slave trade and then slavery. Existing scholarship on the use of emotions in British abolitionist literature tends to explore each emotion separately, particularly sorrow. This study expands upon such scholarship by exploring the relationships between the different emotions that feature in abolitionist literature: specifically joy, happiness, sorrow, anger, fear, shame and guilt. It also explores White and Black writers alongside one another, as well as males and females from different class and religious backgrounds, in order to reflect the diversity of the abolitionist movement in line with recent debates about ‘Black Romanticism’ and ‘transatlantic romanticisms’. The diverse nature of the abolitionist movement meant that a number of different approaches to emotive rhetoric emerged. Whilst most abolitionists advocated emotional equality between different races, they did not all believe that this racial equality extended to intellectual capabilities. This thesis shall consider these complex nuances within the abolitionist movement, and explore the extent to which abolitionist literature challenged – or upheld – the racial hierarchy of White supremacy. Despite these differences, abolition was predominantly depicted as an act of national self-interest, rather than just an act of benevolent philanthropy. This thesis shall therefore argue that abolitionists used emotive rhetoric to argue that abolition would benefit not only the enslaved, but also their enslavers, and the entire British nation, as abolition would allow Britain and its citizens to redeem themselves from the sins of slavery, and thereby re-establish its sense of morality
Knowledge extraction from unstructured data
Data availability is becoming more essential, considering the current growth of web-based data. The data available on the web are represented as unstructured, semi-structured, or structured data. In order to make the web-based data available for several Natural Language Processing or Data Mining tasks, the data needs to be presented as machine-readable data in a structured format. Thus, techniques for addressing the problem of capturing knowledge from unstructured data sources are needed. Knowledge extraction methods are used by the research communities to address this problem; methods that are able to capture knowledge in a natural language text and map the extracted knowledge to existing knowledge presented in knowledge graphs (KGs). These knowledge extraction methods include Named-entity recognition, Named-entity Disambiguation, Relation Recognition, and Relation Linking. This thesis addresses the problem of extracting knowledge over unstructured data and discovering patterns in the extracted knowledge. We devise a rule-based approach for entity and relation recognition and linking. The defined approach effectively maps entities and relations within a text to their resources in a target KG. Additionally, it overcomes the challenges of recognizing and linking entities and relations to a specific KG by employing devised catalogs of linguistic and domain-specific rules that state the criteria to recognize entities in a sentence of a particular language, and a deductive database that encodes knowledge in community-maintained KGs. Moreover, we define a Neuro-symbolic approach for the tasks of knowledge extraction in encyclopedic and domain-specific domains; it combines symbolic and sub-symbolic components to overcome the challenges of entity recognition and linking and the limitation of the availability of training data while maintaining the accuracy of recognizing and linking entities. Additionally, we present a context-aware framework for unveiling semantically related posts in a corpus; it is a knowledge-driven framework that retrieves associated posts effectively. We cast the problem of unveiling semantically related posts in a corpus into the Vertex Coloring Problem. We evaluate the performance of our techniques on several benchmarks related to various domains for knowledge extraction tasks. Furthermore, we apply these methods in real-world scenarios from national and international projects. The outcomes show that our techniques are able to effectively extract knowledge encoded in unstructured data and discover patterns over the extracted knowledge presented as machine-readable data. More importantly, the evaluation results provide evidence to the effectiveness of combining the reasoning capacity of the symbolic frameworks with the power of pattern recognition and classification of sub-symbolic models
Challenges and perspectives of hate speech research
This book is the result of a conference that could not take place. It is a collection of 26 texts that address and discuss the latest developments in international hate speech research from a wide range of disciplinary perspectives. This includes case studies from Brazil, Lebanon, Poland, Nigeria, and India, theoretical introductions to the concepts of hate speech, dangerous speech, incivility, toxicity, extreme speech, and dark participation, as well as reflections on methodological challenges such as scraping, annotation, datafication, implicity, explainability, and machine learning. As such, it provides a much-needed forum for cross-national and cross-disciplinary conversations in what is currently a very vibrant field of research
Leveraging a machine learning based predictive framework to study brain-phenotype relationships
An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the overarching question of how to best structure and run experiments ambiguous. In this work, I cover two explicit pieces of this larger question, the relationship between data representation and predictive performance and a case study on issues related to data collected from disparate sites and cohorts. I then present the Brain Predictability toolbox, a soft- ware package to explicitly codify and make more broadly accessible to researchers the recommended steps in performing a predictive experiment, everything from framing a question to reporting results. This unique perspective ultimately offers recommen- dations, explicit analytical strategies, and example applications for using machine learning to study the brain
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