Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.

Stability of Ampelometric Characteristics of Vitis vinifera L. cv. 'Syrah' and 'Sauvignon blanc' Leaves: Impact of Within-vineyard Variability and Pruning Method/Bud Load

Historically, grapevine (Vitis vinifera L.) leaf characterisation has been a driving force in the identification of cultivars. In this study, ampelometric (foliometric) analysis was done on leaf samples collected from hand-pruned, mechanically pruned and minimally pruned ‘Sauvignon blanc’ and ‘Syrah’ vines to estimate the impact of within-vineyard variability and a change in bud load on the stability of leaf properties. The results showed that within-vineyard variability of ampelometric characteristics was high within a cultivar, irrespective of bud load. In terms of the O.I.V. coding system, zero to four class differences were observed between minimum and maximum values of each characteristic. The value of variability of each characteristic was different between the three levels of bud load and the two cultivars. With respect to bud load, the number of shoots per vine had a significant effect on the characteristics of the leaf laminae. Single leaf area and lengths of veins changed significantly for both cultivars, irrespective of treatment, while angle between veins proved to be a stable characteristic. A large number of biometric data can be recorded on a single leaf; the data measured on several leaves, however, are not necessarily unique for a specific cultivar. The leaf characteristics analysed in this study can be divided into two groups according to the response to a change in bud load, i.e. stable (angles between the veins, depths of sinuses) and variable (length of the veins, length of the petiole, single leaf area). The variable characteristics are not recommended to be used in cultivar identification, unless the pruning method/bud load is known.

Efficient asynchronous scheduling algorithms for cost-effective buffered crossbar switches

Buffered crossbar switches have recently attracted considerable attention as the next generation of high speed interconnects. They are a special type of crossbar switches with an exclusive buffer at each crosspoint of the crossbar. They demonstrate unique advantages over traditional unbuffered crossbar switches, such as high throughput, low latency, and asynchronous packet scheduling. However, since crosspoint buffers are expensive on-chip memories, it is desired that each crosspoint has only a small buffer. This dissertation proposes a series of practical algorithms and techniques for efficient packet scheduling for buffered crossbar switches. To reduce the hardware cost of such switches and make them scalable, we considered partially buffered crossbars, whose crosspoint buffers can be of an arbitrarily small size. Firstly, we introduced a hybrid scheme called Packet-mode Asynchronous Scheduling Algorithm (PASA) to schedule best effort traffic. PASA combines the features of both distributed and centralized scheduling algorithms and can directly handle variable length packets without Segmentation And Reassembly (SAR). We showed by theoretical analysis that it achieves 100% throughput for any admissible traffic in a crossbar with a speedup of two. Moreover, outputs in PASA have a large probability to avoid the more time-consuming centralized scheduling process, and thus make fast scheduling decisions. Secondly, we proposed the Fair Asynchronous Segment Scheduling (FASS) algorithm to handle guaranteed performance traffic with explicit flow rates. FASS reduces the crosspoint buffer size by dividing packets into shorter segments before transmission. It also provides tight constant performance guarantees by emulating the ideal Generalized Processor Sharing (GPS) model. Furthermore, FASS requires no speedup for the crossbar, lowering the hardware cost and improving the switch capacity. Thirdly, we presented a bandwidth allocation scheme called Queue Length Proportional (QLP) to apply FASS to best effort traffic. QLP dynamically obtains a feasible bandwidth allocation matrix based on the queue length information, and thus assists the crossbar switch to be more work-conserving. The feasibility and stability of QLP were proved, no matter whether the traffic distribution is uniform or non-uniform. Hence, based on bandwidth allocation of QLP, FASS can also achieve 100% throughput for best effort traffic in a crossbar without speedup.

Host and network optimizations for performance enhancement and energy efficiency in data center networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

Algorithms for efficient transaction management and consistent queries in client -server semantic object-oriented parallel databases

Large read-only or read-write transactions with a large read set and a small write set constitute an important class of transactions used in such applications as data mining, data warehousing, statistical applications, and report generators. Such transactions are best supported with optimistic concurrency, because locking of large amounts of data for extended periods of time is not an acceptable solution. The abort rate in regular optimistic concurrency algorithms increases exponentially with the size of the transaction. The algorithm proposed in this dissertation solves this problem by using a new transaction scheduling technique that allows a large transaction to commit safely with significantly greater probability that can exceed several orders of magnitude versus regular optimistic concurrency algorithms. A performance simulation study and a formal proof of serializability and external consistency of the proposed algorithm are also presented.^ This dissertation also proposes a new query optimization technique (lazy queries). Lazy Queries is an adaptive query execution scheme which optimizes itself as the query runs. Lazy queries can be used to find an intersection of sub-queries in a very efficient way, which does not require full execution of large sub-queries nor does it require any statistical knowledge about the data.^ An efficient optimistic concurrency control algorithm used in a massively parallel B-tree with variable-length keys is introduced. B-trees with variable-length keys can be effectively used in a variety of database types. In particular, we show how such a B-tree was used in our implementation of a semantic object-oriented DBMS. The concurrency control algorithm uses semantically safe optimistic virtual "locks" that achieve very fine granularity in conflict detection. This algorithm ensures serializability and external consistency by using logical clocks and backward validation of transactional queries. A formal proof of correctness of the proposed algorithm is also presented. ^

Nursing Activities Score: avaliação da carga de trabalho de enfermagem em unidade de terapia intensiva adulto

The sizing of nursing human resources is an essential management tool to meet the needs of the patients and the institution. Regarding to the Intensive Care Unit, where the most critical patients are treated and the most advanced life-support equipments are used, requiring a high number of skilled workers, the use of specific indicators to measure the workload of the team becomes necessary. The Nursing Activities Score is a validated instrument for measuring nursing workload in the Intensive Care Unit that has demonstrated effectiveness. It is a cross-sectional study with the primary objective of assessing the workload of nursing staff in an adult Intensive Care Unit through the application of the Nursing Activities Score. The study was conducted in a private hospital specialized in the treatment of patients with cancer, which is located in the city of Natal (Rio Grande do Norte – Brazil). The study was approved by the Research Ethics Committee of the hospital (Protocol number 558.799; CAAE 24966013.7.0000.5293). For data collection, a form of sociodemographic characteristics of the patients was used; the Nursing Activities Score was used to identify the workload of nursing staff; and the instrument of Perroca, which classifies patients and provides data related to the their need for nursing care, was also used. The collected data were analyzed using a statistical package. The categorical variables were described by absolute and relative frequency, while the number by median and interquartile range. Considering the inferential approach, the Spearman test, the Wald chi-square, Kruskal Wallis and Mann-Whitney test were used. The statistically significant variables were those with p values <0.05. The evaluation of the overall averages of NAS, considering the first 15 days of hospitalization, was performed by the analysis of Generalized Estimating Equations (GEE), with adjust for the variable length of hospitalization. The sample consisted of 40 patients, in the period of June to August 2014. The results showed a mean age of 62,1 years (±23,4) with a female predominance (57,5%). The most frequent type of treatment was clinical (60,0%), observing an average stay of 6,9 days (±6,5). Considering the origin, most patients (35%) came from the Surgical Center. There was a mortality rate of 27,5%. 277 measures of NAS score and Perroca were performed, and the averages of 69,8% (±24,1) and 22,7% (±4.2) were obtained, respectively. There was an association between clinical outcome and value of the Nursing Activities Score in 24 hours (p <0.001), and between the degree of dependency of patients and nursing workload (rp 0,653, p<0,001). The achieved workload of the nursing staff, in the analyzed period, was presented high, showing that hospitalized patients required a high demand for care. These findings create subsidies for sizing of staff and allocation of human resources in the sector, in order to achieve greater safety and patient satisfaction as a result of intensive care, as well as an environment conducive to quality of life for the professionals

Implication du domaine intracellulaire du précurseur de la protéine amyloïde dans la modulation de la plasticité synaptique

Alzheimer's disease is the most common type of dementia in the elderly; it is characterized by early deficits in learning and memory formation and ultimately leads to a generalised loss of higher cognitive functions. While amyloid beta (Aβ) and tau are traditionally associated with the development of Alzheimer disease, recent studies suggest that other factors, like the intracellular domain (APP-ICD) of the amyloid precursor protein (APP), could play a role. In this study, we investigated whether APP-ICD could affect synaptic transmission and synaptic plasticity in the hippocampus, which is involved in learning and memory processes. Our results indicated that overexpression of APP-ICD in hippocampal CA1 neurons leads to a decrease in evoked AMPA-receptor and NMDA-receptor dependent synaptic transmission. Our study demonstrated that this effect is specific for APP-ICD since its closest homologue APLP2-ICD did not reproduce this effect. In addition, APP-ICD blocks the induction of long term potentiation (LTP) and leads to increased of expression and facilitated induction of long term depression (LTD), while APLP2-ICD shows neither of these effects. Our study showed that this difference observed in synaptic transmission and plasticity between the two intracellular domains resides in the difference of one alanine in the APP-ICD versus a proline in the APLP2-ICD. Exchanging this critical amino-acid through point-mutation, we observed that APP(PAV)-ICD had no longer an effect on synaptic plasticity. We also demonstrated that APLP2(AAV)-ICD mimic the effect of APP-ICD in regards of facilitated LTD. Next we showed that the full length APP-APLP2-APP (APP with a substitution of the Aβ component for its homologous APLP2 part) had no effect on synaptic transmission or synaptic plasticity when compared to the APP-ICD. However, by activating caspase cleavage prior to induction of LTD or LTP, we observed an LTD facilitation and a block of LTP with APP-APLP2-APP, effects that were not seen with the full length APLP2 protein. APP is phosphorylated at threonine 668 (Thr668), which is localized directly after the aforementioned critical alanine and the caspase cleavage site in APP-APLP2-APP. Mutating this Thr668 for an alanine abolishes the effects on LTD and restores LTP induction. Finally, we showed that the facilitation of LTD with APP-APLP2-APP involves ryanodine receptor dependent calcium release from intracellular stores. Taken together, we propose the emergence of a new APP intracellular domain, which plays a critical role in the regulation of synaptic plasticity and by extension, could play a role in the development of memory loss in Alzheimer’s disease.

Exact Probability Distribution versus Entropy

The problem addressed concerns the determination of the average numberof successive attempts of guessing a word of a certain length consisting of letters withgiven probabilities of occurrence. Both first- and second-order approximations to a naturallanguage are considered. The guessing strategy used is guessing words in decreasing orderof probability. When word and alphabet sizes are large, approximations are necessary inorder to estimate the number of guesses. Several kinds of approximations are discusseddemonstrating moderate requirements regarding both memory and central processing unit(CPU) time. When considering realistic sizes of alphabets and words (100), the numberof guesses can be estimated within minutes with reasonable accuracy (a few percent) andmay therefore constitute an alternative to, e.g., various entropy expressions. For manyprobability distributions, the density of the logarithm of probability products is close to anormal distribution. For those cases, it is possible to derive an analytical expression for theaverage number of guesses. The proportion of guesses needed on average compared to thetotal number decreases almost exponentially with the word length. The leading term in anasymptotic expansion can be used to estimate the number of guesses for large word lengths.Comparisons with analytical lower bounds and entropy expressions are also provided.

The “Great Masquerader" Strikes Again: Secondary Syphilis Presentation with Erythema Multiforme (EM)-Like Lesions

Syphilis is a sexually transmitted disease known to present with highly variable manifestations, especially when left untreated. Patients who present to Internal Medicine Departments with fever and a rash are always a diagnostic challenge since mild viral diseases and life-threatening bacterial infections may manifest themselves similarly. In the following case presentation, we describe a patient with 1 month’s duration of fever and rash on the palms of the hand and soles of the feet, in the form of erythema multiforme (EM)-like lesions. His disease was diagnosed as secondary syphilis, once again justifying its name: the “great masquerader".

Implication du domaine intracellulaire du précurseur de la protéine amyloïde dans la modulation de la plasticité synaptique

Alzheimer's disease is the most common type of dementia in the elderly; it is characterized by early deficits in learning and memory formation and ultimately leads to a generalised loss of higher cognitive functions. While amyloid beta (Aβ) and tau are traditionally associated with the development of Alzheimer disease, recent studies suggest that other factors, like the intracellular domain (APP-ICD) of the amyloid precursor protein (APP), could play a role. In this study, we investigated whether APP-ICD could affect synaptic transmission and synaptic plasticity in the hippocampus, which is involved in learning and memory processes. Our results indicated that overexpression of APP-ICD in hippocampal CA1 neurons leads to a decrease in evoked AMPA-receptor and NMDA-receptor dependent synaptic transmission. Our study demonstrated that this effect is specific for APP-ICD since its closest homologue APLP2-ICD did not reproduce this effect. In addition, APP-ICD blocks the induction of long term potentiation (LTP) and leads to increased of expression and facilitated induction of long term depression (LTD), while APLP2-ICD shows neither of these effects. Our study showed that this difference observed in synaptic transmission and plasticity between the two intracellular domains resides in the difference of one alanine in the APP-ICD versus a proline in the APLP2-ICD. Exchanging this critical amino-acid through point-mutation, we observed that APP(PAV)-ICD had no longer an effect on synaptic plasticity. We also demonstrated that APLP2(AAV)-ICD mimic the effect of APP-ICD in regards of facilitated LTD. Next we showed that the full length APP-APLP2-APP (APP with a substitution of the Aβ component for its homologous APLP2 part) had no effect on synaptic transmission or synaptic plasticity when compared to the APP-ICD. However, by activating caspase cleavage prior to induction of LTD or LTP, we observed an LTD facilitation and a block of LTP with APP-APLP2-APP, effects that were not seen with the full length APLP2 protein. APP is phosphorylated at threonine 668 (Thr668), which is localized directly after the aforementioned critical alanine and the caspase cleavage site in APP-APLP2-APP. Mutating this Thr668 for an alanine abolishes the effects on LTD and restores LTP induction. Finally, we showed that the facilitation of LTD with APP-APLP2-APP involves ryanodine receptor dependent calcium release from intracellular stores. Taken together, we propose the emergence of a new APP intracellular domain, which plays a critical role in the regulation of synaptic plasticity and by extension, could play a role in the development of memory loss in Alzheimer’s disease.

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

The effects of attractiveness and underlying components on the motivational salience and the memorability of face photographs

Facial attractiveness is a particularly salient social cue that influences many important social outcomes. Using a standard key-press task to measure motivational salience of faces and an old/new memory task to measure memory for face photographs, this thesis investigated both within-woman and between-women variations in response to facial attractiveness. The results indicated that within-woman variables, such as fluctuations in hormone levels, influenced the motivational salience of facial attractiveness. However, the between-women variable, romantic relationship status, did not appear to modulate women’s responses to facial attractiveness. In addition to attractiveness, dominance also contributed to both the motivational salience and memorability of faces. This latter result demonstrates that, although attractiveness is an important factor for the motivational salience of faces, other factors might also cause faces to hold motivational salience. In Chapter 2, I investigated the possible effects of women’s salivary hormone levels (estradiol, progesterone, testosterone, and estradiol-to-progesterone ratio) on the motivational salience of facial attractiveness. Physically attractive faces generally hold greater motivational salience, replicating results from previous studies. Importantly, however, the effect of attractiveness on the motivational salience of faces was greater in test sessions where women had high testosterone levels. Additionally, the motivational salience of attractive female faces was greater in test sessions where women had high estradiol-to-progesterone ratios. While results from Chapter 2 suggested that the motivational salience of faces was generally positively correlated with their physical attractiveness, Chapter 3 explored whether physical characteristics other than attractiveness contributed to the motivational salience of faces. To address this issue, I first had the faces rated on multiple traits. Principal component analysis of third-party ratings of faces for these traits revealed two orthogonal components that were highly correlated with trustworthiness and dominance ratings respectively. Both components were positively and independently related to the motivational salience of faces. While Chapter 2 and 3 did not examine the between-woman differences in response to facial attractiveness, Chapter 4 examined whether women’s responses to facial attractiveness differed as a function of their romantic partnership status. As several researchers have proposed that partnership status influences women’s perception of attractiveness, in Chapter 4 I compared the effects of men’s attractiveness on partnered and unpartnered women’s performance on two response measures: memory for face photographs and the motivational salience of faces. Consistent with previous research, women’s memory was poorer for face photographs of more attractive men and more attractive men’s faces held greater motivational salience. However, in neither study were the effects of attractiveness modulated by women’s partnership status or partnered women’s reported commitment to or happiness with their romantic relationship. A key result from Chapter 4 was that more attractive faces were harder to remember. Building on this result, Chapter 5 investigated the different characteristics that contributed to the memorability of face photographs. While some work emphasizes relationships with typicality, familiarity, and memorability ratings, more recent work suggests that ratings of social traits, such as attractiveness, intelligence, and responsibility, predict the memorability of face photographs independently of typicality, familiarity, and memorability ratings. However, what components underlie these traits remains unknown, as well as whether these components relate to the actual memorability of face photographs. Principal component analysis of all these face ratings produced three orthogonal components that were highly correlated with trustworthiness, dominance, and memorability ratings, respectively. Importantly, each of these components also predicted the actual memorability of face photographs.

Liquid Mixtures Involving Hydrogenated and Fluorinated Alcohols: Thermodynamics, Spectroscopy, and Simulation

This article reports a combined thermodynamic, spectroscopic, and computational study on the interactions and structure of binary mixtures of hydrogenated and fluorinated substances that simultaneously interact through strong hydrogen bonding. Four binary mixtures of hydrogenated and fluorinated alcohols have been studied, namely, (ethanol + 2,2,2-trifluoroethanol (TFE)), (ethanol + 2,2,3,3,4,4,4-heptafluoro-1-butanol), (1-butanol (BuOH) + TFE), and (BuOH + 2,2,3,3,4,4,4-heptafluoro-1-butanol). Excess molar volumes and vibrational spectra of all four binary mixtures have been measured as a function of composition at 298 K, and molecular dynamics simulations have been performed. The systems display a complex behavior when compared with mixtures of hydrogenated alcohols and mixtures of alkanes and perfluoroalkanes. The combined analysis of the results from different approaches indicates that this results from a balance between preferential hydrogen bonding between the hydrogenated and fluorinated alcohols and the unfavorable dispersion forces between the hydrogenated and fluorinated chains. As the chain length increases, the contribution of dispersion increases and overcomes the contribution of H-bonds. In terms of the liquid structure, the simulations suggest the possibility of segregation between the hydrogenated and fluorinated segments, a hypothesis corroborated by the spectroscopic results. Furthermore, a quantitative analysis of the infrared spectra reveals that the presence of fluorinated groups induces conformational changes in the hydrogenated chains from the usually preferred all-trans to more globular arrangements involving gauche conformations. Conformational rearrangements at the CCOH dihedral angle upon mixing are also disclosed by the spectra.

Do Global CO2 Emissions from Fossil-Fuel Consumption Exhibit Long Memory? A Fractional Integration Analysis

In this article we use an autoregressive fractionally integrated moving average approach to measure the degree of fractional integration of aggregate world CO2 emissions and its five components – coal, oil, gas, cement, and gas flaring. We find that all variables are stationary and mean reverting, but exhibit long-term memory. Our results suggest that both coal and oil combustion emissions have the weakest degree of long-range dependence, while emissions from gas and gas flaring have the strongest. With evidence of long memory, we conclude that transitory policy shocks are likely to have long-lasting effects, but not permanent effects. Accordingly, permanent effects on CO2 emissions require a more permanent policy stance. In this context, if one were to rely only on testing for stationarity and non-stationarity, one would likely conclude in favour of non-stationarity, and therefore that even transitory policy shocks

Response times in computerized adaptive testing: a method for cheating detection

In the field of educational and psychological measurement, the shift from paper-based to computerized tests has become a prominent trend in recent years. Computerized tests allow for more complex and personalized test administration procedures, like Computerized Adaptive Testing (CAT). CAT, following the Item Response Theory (IRT) models, dynamically generates tests based on test-taker responses, driven by complex statistical algorithms. Even if CAT structures are complex, they are flexible and convenient, but concerns about test security should be addressed. Frequent item administration can lead to item exposure and cheating, necessitating preventive and diagnostic measures. In this thesis a method called "CHeater identification using Interim Person fit Statistic" (CHIPS) is developed, designed to identify and limit cheaters in real-time during test administration. CHIPS utilizes response times (RTs) to calculate an Interim Person fit Statistic (IPS), allowing for on-the-fly intervention using a more secret item bank. Also, a slight modification is proposed to overcome situations with constant speed, called Modified-CHIPS (M-CHIPS). A simulation study assesses CHIPS, highlighting its effectiveness in identifying and controlling cheaters. However, it reveals limitations when cheaters possess all correct answers. The M-CHIPS overcame this limitation. Furthermore, the method has shown not to be influenced by the cheaters’ ability distribution or the level of correlation between ability and speed of test-takers. Finally, the method has demonstrated flexibility for the choice of significance level and the transition from fixed-length tests to variable-length ones. The thesis discusses potential applications, including the suitability of the method for multiple-choice tests, assumptions about RT distribution and level of item pre-knowledge. Also limitations are discussed to explore future developments such as different RT distributions, unusual honest respondent behaviors, and field testing in real-world scenarios. In summary, CHIPS and M-CHIPS offer real-time cheating detection in CAT, enhancing test security and ability estimation while not penalizing test respondents.