En deskriptivt baserad uppsats, med kortare semistrukturerade intervjuer online som komplement, för att ge ytterligare tyngd åt argumenten. Frågeställningarna som tas upp är huruvida väntan har samma innebörd idag som förr i världen? vilka olika faktorer kan exempelvis bidra till accelerationen i samhället idag? Hur och på vilket sätt påverkar dessa faktorer individen? Hur kan individen uppleva en sjukskrivning p.g.a. stress och utmattningssyndrom? likheter/skillnader? Hur ser livet ut före resp. efter en sjukskrivning p.g.a. stress och utmattningssyndrom? Hur påverkas synen på den egna hälsan av en sjukskrivning p.g.a. stress och utmattningssyndrom? Hur kan man med enkla medel ta hand om sig själv och sin egen hälsa på ett fördelaktigt sätt? Dessa frågeställningar har gett svaren att väntan inte alls har samma innebörd idag som förr i världen, att teknologi och informationsteknik rusar ifrån oss, sköljer över oss och gör att vi inte sällan drabbas av en kronisk distraktion, vilken i sämsta fall leder till en kraschlandning i sjukskrivning p.g.a. stress och utmattningssyndrom. Tiden före/efter en kraschlandning upplevs som väldigt kontrasterande och begreppet "hälsa" får oftast en helt annan innebörd efter en kraschlandning än vad den hade innan. Naturenn själv och fysisk aktivitet är två av de mest hälsofrämjande företeelserna som finns och för att öka kunskapen och förståelsen för stress och utmattningssyndrom så är det viktigt att prata om det, inte skämmas för eller tysta ner det, gärna ta upp det redan i grundskolan och låta personer som själva kraschat i sjukskrivningar p.g.a. stress och utmattningssyndrom föreläsa och berätta om hur symtomen smyger sig på, vilka symtom det handlar om och vad som kan hända om man inte bromsar i tid.

Data mining plays an important role in a variety of fields including bioinformatics, multimedia, business intelligence, marketing, and medical diagnosis. Analysis of today’s huge and complex data involves several data mining algorithms including clustering and classification. The computational complexity of machine learning and data mining algorithms, that are frequently used in today’s applications such as embedded systems, makes the design of efficient hardware architectures for these algorithms a challenging issue for the development of such systems. The aim of this work is to optimize the performance of hardware acceleration for data mining applications in terms of speed and area. Most of the previous accelerator architectures proposed in the literature have been obtained using ad hoc techniques that do not allow for design space exploration, some did not consider the size (number of samples) and dimensionality (number of features in each sample) of the datasets. To obtain practical architectures that are amenable for hardware implementation, size and dimensionality of input datasets are taken into consideration in this work. For one-dimensional data, algorithm-level optimizations are investigated to design a fast and area-efficient hardware accelerator for clustering one-dimensional datasets using the well-known K-Means clustering algorithm. Experimental results show that the optimizations adopted in the proposed architecture result in faster convergence of the algorithm using less hardware resources while maintaining the quality of clustering results. The computation of similarity distance matrices is one of the computational kernels that are generally required by several machine learning and data mining algorithms to measure the degree of similarity between data samples. For these algorithms, distance calculation is considered a computationally intensive task that accounts for a significant portion of the processing time. A systematic methodology is presented to explore the design space of 2-D and 1-D processor array architectures for similarity distance computation involved in processing datasets of different sizes and dimensions. Six 2-D and six 1-D processor array architectures are developed systematically using linear scheduling and projection operations. The obtained architectures are classified based on the size and dimensionality of input datasets, analyzed in terms of speed and area, and compared with previous architectures in the literature. Motivated by the necessity to accommodate large-scale and high-dimensional data, nonlinear scheduling and projection operations are finally introduced to design a scalable processor array architecture for the computation of similarity distance matrices. Implementation results of the proposed architecture show improved compromise between area and speed. Moreover, it scales better for large and high-dimensional datasets since the architecture is fully parameterized and only has to deal with one data dimension in each time step. / Graduate / 2019-12-31

Syftet med föreliggande undersökning är att få mer kunskap om hur undervisning av elever med höga resultat i matematik bedrivs i årskurserna 4–6, samt att få en inblick om vilka anpassningar studiens lärareanvänder sig av för att tillgodose högpresterande elevers undervisningsbehov. Vi vill även undersöka om det finns någon vedertagen definition för högpresterande elever i matematik. Undersökningen genomförs med hjälp av kvalitativa intervjuer med tio mellanstadielärare. Studien utgår i huvudsak från ett sociokulturellt perspektiv och hermeneutiskt inspirerat analysarbete. Resultatet visar att det inte finns någon allmänt accepterad definition på̊ vad högpresterande elever är, men att lärare använder andra begrepp för elever som presterar på̊ hög nivå̊. Vidare visar resultatet att respondenterna hade kunskaper om accelerering och berikning men att dessa tekniker inte i så stor utsträckning användes på̊ ett framgångsrikt sätt utifrån den högpresterande elevens behov. Förklaringen till det ligger mestadels i bristen på̊ didaktiska ämneskunskaper och planeringstid.

In the last few years, security has become one of the key challenges in computing systems. Failures in the secure operations of these systems have led to massive information leaks and cyber-attacks. Case in point, the identity leaks from Equifax in 2016, Spectre and Meltdown attacks to Intel and AMD processors in 2017, Cyber-attacks on Facebook in 2018. These recent attacks have shown that the intruders attack different layers of the systems, from low-level hardware to software as a service(SaaS). To protect the systems, the defense mechanisms should confront the attacks in the different layers of the systems. In this work, we propose four security mechanisms for computing systems: (i ) using backside imaging to detect Hardware Trojans (HTs) in Application Specific Integrated Circuits (ASICs) chips, (ii ) developing energy-efficient reconfigurable cryptographic engines, (iii) examining the feasibility of malware detection using Hardware Performance Counters (HPC). Most of the threat models assume that the root of trust is the hardware running beneath the software stack. However, attackers can insert malicious hardware blocks, i.e. HTs, into the Integrated Circuits (ICs) that provide back-doors to the attackers or leak confidential information. HTs inserted during fabrication are extremely hard to detect since their overheads in performance and power are below the variations in the performance and power caused by manufacturing. In our work, we have developed an optical method that identifies modified or replaced gates in the ICs. We use the near-infrared light to image the ICs because silicon is transparent to near-infrared light and metal reflects infrared light. We leverage the near-infrared imaging to identify the locations of each gate, based on the signatures of metal structures reflected by the lowest metal layer. By comparing the imaged results to the pre-fabrication design, we can identify any modifications, shifts or replacements in the circuits to detect HTs. With the trust of the silicon, the computing system must use secure communication channels for its applications. The low-energy cost devices, such as the Internet of Things (IoT), leverage strong cryptographic algorithms (e.g. AES, RSA, and SHA) during communications. The cryptographic operations cause the IoT devices a significant amount of power. As a result, the power budget limits their applications. To mitigate the high power consumption, modern processors embed these cryptographic operations into hardware primitives. This also improves system performance. The hardware unit embedded into the processor provides high energy-efficiency, low energy cost. However, hardware implementations limit flexibility. The longevity of theIoTs can exceed the lifetime of the cryptographic algorithms. The replacement of the IoT devices is costly and sometimes prohibitive, e.g., monitors in nuclear reactors.In order to reconfigure cryptographic algorithms into hardware, we have developed a system with a reconfigurable encryption engine on the Zedboard platform. The hardware implementation of the engine ensures fast, energy-efficient cryptographic operations. With reliable hardware and secure communication channels in place, the computing systems should detect any malicious behaviors in the processes. We have explored the use of the Hardware Performance Counters (HPCs) in malware detection. HPCs are hardware units that count micro-architectural events, such as cache hits/misses and floating point operations. Anti-virus software is commonly used to detect malware but it also introduces performance overhead. To reduce anti-virus performance overhead, many researchers propose to use HPCs with machine learning models in malware detection. However, it is counter-intuitive that the high-level program behaviors can manifest themselves in low-level statics. We perform experiments using 2 ∼ 3 × larger program counts than the previous works and perform a rigorous analysis to determine whether HPCs can be used to detect malware. Our results show that the False Discovery Rate of malware detection can reach 20%. If we deploy this detection system on a fresh installed Windows 7 systems, among 1,323 binaries, 198 binaries would be flagged as malware.

O principal objetivo desse trabalho foi investigar a possibilidade da detecção sub-ppb (10-9 g/g) de elementos químicos presentes em amostras líquidas, analisadas por meio da espectrometria de raios-X, seja por STXRF (Synchrotron Total reflection X-ray Fluorescence), ou PIXE (Particle Induced X-ray Emission). Na ausência de padrões certificados, foram preparados em laboratório padrões líquidos multielementares com concentrações variando de 1 a 20 ppm, posteriormente micropipetados sobre substrato de Lucite, constituindo assim amostras adequadas para análises STXRF. Visando controlar a manipulação volumétrica, foi utilizado como padrão interno ítrio, em função do qual foi determinada a curva de sensibilidade relativa para o método. Após pré-concentração das amostras, estas foram micropipetadas sobre substratos de Lucite, Mylar e Kimfoil e as análises foram realizadas no Laboratório Nacional de Luz Síncontron LNLS, em Campinas, SP e no Laboratório de Análise de Materiais por feixes Iônicos LAMFI, no IFUSP. Os limites de detecção para ambos os métodos foram determinados e comparados. Como esperado, a STXRF apresenta limites de detecção uma ordem de grandeza inferior ao PIXE para elementos com 20<Z<40, alcançando, após pré-concentração, um limite inferior de 0,3 ppb para o Zn. A comparação entre medidas elementares absolutas de mesmas amostras pelos métodos STXRF e PIXE indicou substantiva sub avaliação das incertezas analíticas, mas apenas um pequeno desvio sistemático entre resultados de ambos os métodos. Para demonstrar o método, foram analisadas amostras de água do Rio Toledo, afluente do Rio S. Francisco e fonte de água da cidade de Toledo no oeste do estado do Paraná (24°43´S; 53°44´W), trabalho realizado em colaboração com o Núcleo de Biotecnologia e Desenvolvimento de Processos Químicos NBQ da Universidade Estadual do Oeste do Paraná UNIOESTE. Finalmente foi verificada a possibilidade de preparar as amostras micropipetadas para a STXRF sobre substratos de filme fino (Mylar com 2,4 m de espessura e Kimoil com 2 m) ao invés dos tradicionais discos espessos de acrílico, resultando na redução de um fator 3 no limite de detecção. / The main goal of this work was to investigate the feasibility of sub-ppb (10-9 g/g) detection of trace elements in liquid samples analyzed by STXRF (Synchrotron Total reflection X-ray Fluorescence) and by PIXE (Particle Induced X-ray Emission). For STXRF calibration, and due to the absence of certified liquid standards, a set of multielementary liquid standards was prepared in house, with concentrations varying from 1 to 20 ppm in water, and then micro-pipetted on regular 30 mm diameter Lucite discs. Ytrium was used as an internal standard and the relative sensitivity curves were found relative to it. Real water samples for trace element analysis, were first preconcentrated (to a factor of 5) and then micropipetted on Lucite and also on Lylar and Kimfoil thin films. All the STRXF and PIXE analysis were made respectively in the Laboratório Nacional de Luz Síncrotron LNLS , in Campinas, SP, and in the Laboratório de Análise de Materiais por feixes Iônicos LAMFI, USP. As expected, the minimum elementary detection limits for STXRF for elements with 20<Z<40, were one order of magnitude lower than the PIXE ones, reaching, after pre-concentration, 0.3 ppb for Zn. Comparison of STXRF and PIXE data of the same samples, showed some slight differences in absolute concentrations, but an overall and important underestimation of the experimental uncertainties. To exemplify the use of trace-element analysis in liquids, several samples from Toledo River, the main water supply of Toledo City (24°43´S; 53°44´W), were collected and analyzed over one year, in a collaboration with the Núcleo de Biotecnologia e Desenvolvimento de Processos Químicos NBQ from the Universidade Estadual do Oeste do Paraná UNIOESTE. The STXRF analysis of samples pipetted on Mylar and Kimfol, though not dense substrates, showed to be possible, and resulted in a 3 fold reduction of the minimum detection limits for STXRF analysis.

The purpose of this project is to create a theoretical dynamic analysis model to assess the dynamic response of light, wood-framed structures before and after fire conditions. This information is useful for predicting the damage to structural integrity due to a fire. The mass and stiffness matrices used for the dynamic model are derived from a standard residential building created for an existing project at WPI funded through the DHS/FEMA/USFA Assistance to Firefighters Grant program. The damping matrix is derived via the Rayleigh Damping Method using the mass and stiffness matrices obtained through SAP. Then, theoretical impact forces are applied to the developed dynamic model, and the acceleration response is estimated using Matlab. Both acceleration time history and frequency responses are used as the evaluation method. Finally, the dynamic model is integrated with a fire simulation model to investigate the impact of fire conditions on dynamic responses of residential buildings. The results show that frequencies can shift due to the structural degradation due to fire.

Accelerometers are devices that have been recently used to assess the performance of weightlifters in training. This case study used vertical acceleration data to assess multiple snatch attempts in a single lifter during a training session. The lifter exhibited variability in a number of measures, including peak acceleration and peak velocity. The measures calculated from the acceleration-time data did not show consistent trends between made and missed lifts. More work is needed identify variables of interest for snatch performance.

The purpose of this study was to determine the attitudes of teachers in rural locales towards giftedness and gifted education. Gagné and Nadeau’s (1991) Opinions About the Gifted and Their Education was used as the survey instrument. A total of 78 teachers from four public school districts in Kentucky classified as rural participated in the study. The respondents indicated slightly positive attitudes toward the need for specialized instruction for gifted students and the social value of giftedness but slightly negative mindsets towards acceleration. These results as a whole mirrored the findings of several previous studies utilizing the same survey instrument. However, individual teacher’s attitudes varied widely, with some very negative responses and some more positive, rendering the results determined using averaged scores from the full sample a somewhat inaccurate indicator of broad-scope, overall teacher mindset towards gifted education. When comparing the attitudes towards acceleration of teachers who had graduated from the district in which they teach to those who graduated from a district outside of where they teach, a significantly more negative attitude was indicated in teachers who remained within their home districts. Further study is recommended to determine if this could be an influence of more traditional and anti-intellectual mindsets often found in rural communities and if it has any effect on the quality of services and programming opportunities available to gifted students in rural locales.

Focusing on teacher learning, this study follows fifteen teachers in the crux of comprehensive school reform. These "regular" classroom teachers are the ubiquitous players of this theatre of school reform. "Regular" teacher is defined as a typical classroom teacher who is not actively involved in the district's school reform project or one who hasn't taken an active leadership role. The teachers in this study work in the challenging environment of a poor, diverse urban school district that was in its third year of a comprehensive school reform program, the Accelerated Schools Project. Fifteen teachers volunteered to take part in a teaching laboratory where they met, planned, taught, assessed and reflected on their practice. The study tells, analyzes and speculates about their journey. The Accelerated Schools Project (ASP) is a national comprehensive school improvement model that provides professional development to schools. The study described the experiences of regular classroom teachers who engaged in a yearlong professional development program that is part of the ASP service to schools. This study employs qualitative research methods in a multiple case study analysis. By examining the teaching practices of regular classroom teachers who are often depicted as "closing the door" to the outside influences of school, district, state or federal policy, the study seeks to fully understand the planning, teaching, assessing and reflecting of classroom teachers who are caught in the center of school reform. The key findings of this study suggest teacher practice for all teacher cohorts (novice, mid-career and veteran) was influenced by participation in the Powerful Learning Laboratory. Each aspect of teaching (planning, teaching, assessing and reflection) was influenced, with differing emphasis by each cohort. The findings suggest the Powerful Learning Lab is a positive professional development experience for teachers, and that teacher learning labs should remain an integral part of the Accelerated Schools Project.

A new development has evolved in Sweden where is more common that individuals chose to study higher educations at universities and colleges. At the same time, society has evolved to become increasingly characterized by individualization and acceleration. This study aims to illustrate different attitudes towards higher education among individuals who either wish or do not wish to study. The study has a sociological approach, and the basis for analysis is, above all, theories of individualization. The study shows that common attitudes towards higher education exist among both individuals who either wish or do not wish to study. The effects of individualization have contributed to an increasing pressure around the idea that the individual should educate himself rather than if he wants it (Rosa, 2013). At the same time there is an idea that education should be planned within a specific age to be able to live a desirable and stable life after 30 years old. Individuals are socialized through school into an idea of making themselves employable and learning to think and act in a certain competitive spirit. Many also argue that higher education will automatically result in a safer future and above all with more choices in life.