Miracle-dz/UniFAR-FTUs huggingface dataset:
Civic Skills and Civic Education: An Empirical Assessment

Title of dissertation: CIVIC SKILLS AND CIVIC EDUCATION: AN EMPIRICAL ASSESSMENT Melissa Kovacs Comber, Doctor of Philosophy, 2005 Dissertation directed by: Professor Christopher Foreman, Jr. School of Public Policy Participation in public life requires sufficient civic skills. Civic skills include the abilities to communicate with elected officials, organize to influence policy, understand and participate in one’s polity, and think critically about civic and political life. One source of civic skill development is civic education coursework, often provided in high school or college. This dissertation tests for a correlation between civic skills (political interpretation skills, news monitoring skills, group discussion skills, communication skills, and English language skills) and civics coursework among fourteen to thirty-yearolds using probit models and propensity score matching methods. Data sources include the International Association for the Evaluation of Educational Achievement Civic Education survey (1999), the Civic and Political Health of the Nation: A Generational Portrait survey (2001), the American Citizen Participation Study (1990); and the National Household Education Survey’s Civic Involvement study (2001). Political interpretation skills are almost always correlated with the presence of civic education. According to the IEA/CivEd study, studying the Constitution and the Presidency almost always influences civic skill levels, while other civic education topics sometimes influence civic skill levels. Civic education is not always correlated with news monitoring skills. Civic education is sometimes correlated with the presence of group discussion skills and communication skills. No evidence was found of a correlation between civic education and English language skills. Among minorities, females, low-income respondents, non-college respondents, and non-Hispanic whites, differences exist in civic skill levels and the effect of civic education on civic skill presence. This dissertation recommends that all American high school students take at least one semester of civics. This dissertation also recommends schools and communities seek to prioritize teaching civic skills in schools, so as to equalize abilities of political participation. Further research is needed to fully understand the relationship between the content of civics courses, the classroom climate of civics courses, and civic skill presence. CIVIC SKILLS AND CIVIC EDUCATION: AN EMPIRICAL ASSESSMENT
Dynamic Multi-Server Searchable Encryption Scheme Based on Concept Hierarchy

Due to the prevalent trend of cloud storage, data owners are motivated to outsource their private data to the cloud server. However, to ensure the security and privacy of their data, data owners might expect to store them in multiple cloud servers with the form of ciphertext. In previous studies, keywords are frequently used to retrieve the encrypted target data, which ignores the conceptual relationship between keywords and cannot return exact query results especially when users enter imprecise keywords. In this paper, it presents a Dynamic Multi-server Searchable Encryption Scheme Based on Concept Hierarchy to solve the problem. It designs the respective index of sub-servers and documents using the concept hierarchy tree, which can locate the most relevant cloud servers in order to improve the retrieval efficiency. And meanwhile it introduces TF*IDF method to further enhance the accuracy of retrieval. Simulation experiments demonstrate the advantages of our scheme when compared with other typical schemes.
Probabilistic High-Level Estimation of Vulnerability and Fault Mitigation of Critical Systems Using Fault-Mitigation Trees (FMTs)

The development of safety-critical systems is a rather challenging task, especially due to the cost and complexity associated with this endeavor. For this reason, early fault assessment is a key element towards minimizing vulnerability at the design stage of development. Existing early analysis techniques are often unable to conduct a comprehensive and exhaustive analysis on complex redundant architectures, which may lead to less than optimal risk evaluation. This paper seeks to address some of these issues by proposing a high-level analysis methodology based on probabilistic model checking. This analysis is done by introducing new probabilistic models for repairable fault trees described in the Continuous-Time Markov Decision Process formalism. The models include repairable components and redundancy partitioning to evaluate fault vulnerability across different implementations of the system. The presented approach is very scalable and results demonstrate that the proposed analysis is as reliable as physical FPGA testing, in some scenarios.
Remnant Preservation is Helpful to Obtain Good Clinical Results in Posterior Cruciate Ligament Reconstruction: Comparison of Clinical Results of Three Techniques

Background The purpose of the present study was to compare the clinical results of 3 posterior cruciate ligament reconstruction techniques according to the time from injury to surgery and remnant PCL status and to evaluate the efficiency of each technique. Methods The records of 89 patients who underwent primary PCL reconstructions with a posterolateral corner sling were analyzed retrospectively. Thirty-four patients were treated by anterolateral bundle (ALB) reconstruction with preservation of the remnant PCL using a transtibial tunnel technique in the acute and subacute stages of injury (group 1). Forty patients were treated with remnant PCL tensioning and an ALB reconstruction using the modified inlay technique in the chronic stage (group 2), and fifteen patients were treated with double-bundle reconstruction using the modified inlay technique (group 3). The double-bundle reconstruction was performed if there was a very weak or no PCL remnant. Results The mean side-to-side differences in posterior tibial translation on the stress radiographs were reduced from 10.1 ± 2.5 mm in group 1, 10.6 ± 2.4 mm in group 2, and 12.8 ± 3.2 mm in group 3 preoperatively to 2.3 ± 1.4 mm in group 1, 2.3 ± 1.5 mm in group 2, and 4.0 ± 2.5 mm in group 3 at the last follow-up (p < 0.001, p < 0.001, and p < 0.001, respectively). Statistical analyses revealed that group 1 and group 2 were similar in terms of side-to-side difference changes in posterior tibial translation on the stress radiographs; however, group 3 was inferior to group 1 and group 2 at the last follow-up (p = 0.022). The clinical results were not significantly different among the three groups. Conclusions Excellent posterior stability and good clinical results were achieved with ALB reconstruction preserving the injured remnant PCL in the acute and subacute stages and remnant PCL tensioning with ALB reconstruction in the chronic stage. The PCL injuries could be surgically corrected with different techniques depending on both the remnant PCL status and the interval between the knee trauma and operation.
Human-Robot Collaborative Semi-Autonomous Teleoperation with Force Feedback

Teleoperation is the most important method to control robot in the distance, as complete autonomy still has a long way to go. To facilitate teleoperation efficiency, human-robot collaboration methods have been suggested, in which robot and human work together to accomplish a task. In this paper, we propose a semi-autonomous teleoperation method, with which operator can command the robot to reach the desired location via direct control at the initial phase, by virtue of the intent recognition algorithm, sub-task is recognized, and finally robot itself takes over the control to accomplish the task. A biological experiment is designed, and the direct control and semi-autonomous control strategies are compared through user studies. Execution time, task success rate and a subjective evaluation (NASA TLX) are used to quantify user performance. The trials show that our strategy can reduce workload and enhance operation accuracy significantly.
Sentiment Analysis Approach Based on N-gram and KNN Classifier

Sentiment analysis is the approach which is designed to analyze positive, negative and neutral aspects of any text unit. In the past years, many techniques were designed for the sentiment analysis of twitter data. Based on the previous study about sentiment analysis, a novel approach is presented in this research paper for the sentiment analysis of twitter data. The proposed approach is the combination of feature extraction and classification techniques. N-gram algorithm is applied for the feature extraction and KNN classifier is applied to classify input data into positive, negative and neutral classes. To validate the proposed system, performance is analyzed in terms of precision, recall and accuracy. The results of the experiment of proposed system show that it performs well as compared to the existing system which is based on SVM classifier.
Simulations of the Whirling Instability by the Immersed Boundary Method

When an elastic filament spins in a viscous incompressible fluid it may undergo a whirling instability, as studied asymptotically by Wolgemuth, Powers, and Goldstein [Phys. Rev. Lett. , 84 (2000), pp. 16--23]. We use the immersed boundary (IB) method to study the interaction between the elastic filament and the surrounding viscous fluid as governed by the incompressible Navier--Stokes equations. This allows the study of the whirling motion when the shape of the filament is very different from the unperturbed straight state.
A Novel Genetic Algorithm Based ECOC Algorithm

This paper proposes a genetic algorithm (GA) based error correcting output codes (ECOC) algorithms. In our algorithm, some randomly initialized coding matrices are generated as seeds firstly, and our algorithm produces optimal coding matrices based on them in the evolutionary process. In our GA, each gene stands for an action, indicating two selected columns and an operator. The operators are proposed to generate new columns by exchanging information between a pair of parent columns. In this way, each individual represents a new coding matrix. A legality checking function is embedded in the GA to keep the produced coding matrix both legal and effective. At the end of this evolutionary process, the best coding matrix is selected as final solution. The experimental results show that our algorithm can efficiently optimize the coding matrix compared with the seed matrices.
A Framework for Tunable Anomaly Detection

As software architecture practice relies more and more on runtime data to inform decisions in continuous experimentation and self-adaptation, it is increasingly important to consider the quality of the data used as input to the different decision-making and prediction algorithms. One issue in data-driven decisions is that real-life data coming from running systems can contain invalid or wrong values which can bias the result of data analysis. Data-driven decision-making should therefore comprise detection and handling of data anomalies as an integral part of the process. However, currently, anomaly detection is either absent in runtime decision-making approaches for continuous experimentation and self-adaptation or difficult to tailor to domain-specific needs. In this paper, we contribute by proposing a framework that simplifies the detection of data anomalies in timeseries-outputs of running systems. The framework is generic, since it can be employed in different domains, and tunable, since it uses expert user input in tailoring anomaly detection to the needs and assumptions of each domain. We evaluate the feasibility of the framework by successfully applying it to detecting anomalies in a real-life timeseries dataset from the traffic domain.
A query sampling method for estimating local cost parameters in a multidatabase system

In a multidatabase system (MDBS), some query optimization information related to local database systems may not be available at the global level because of local autonomy. To perform global query optimization, a method is required to derive the necessary local information. This paper presents a new method that employs a query sampling technique to estimate the cost parameters of an autonomous local database system. We introduce a classification for grouping local queries and suggest a cost estimation formula for the queries in each class. We present a procedure to draw a sample of queries from each class and use the observed costs of sample queries to determine the cost parameters by multiple regression. Experimental results indicate that the method is quite promising for estimating the cost of local queries in an MDBS. <<ETX>>
A Non-Linearity Compensation Technique for Charge-Redistribution SAR ADCs

The linearity of charge-redistribution successive approximation register (SAR) analog-to-digital converters (ADCs) is affected by systematic mismatch in its capacitor array. Fabrication phenomena, such as copper dishing, lead to radial gradients in the array, and the detrimental effect of these gradients cannot be mitigated by the use of common centroid layout techniques. In this work, a technique that uses the foreknowledge of radial gradients in order to shape the linearity of data converters is introduced. By choosing the order in which the capacitors within the physical array are connected during a conversion, it is possible to manipulate the converter linearity, and several integral nonlinearities (INLs) shapes can be obtained. This technique, also applicable for any ADC where the static linearity depends upon the mismatch within an array of regularly-placed circuit elements, has been successfully tested for a 10-bit SAR ADC implemented in a 180-nm CMOS process.
Estimation of the quality of refined protein crystal structures

Crystallographic Rwork and Rfree values, which are measures of the ability of the models of macromolecular structures to explain the crystallographic data on which they are based, are often used to assess structure quality. It is widely known, and confirmed here that both are sensitive to the methods used to compute them, and can be manipulated to improve the apparent quality of the model. As an alternative it is proposed here that the quality of crystallographic models should be assessed using a global goodness‐of‐fit metric RO2A/Rwork where RO2A is the number of reflections used for refinement divided by the number of nonhydrogen atoms in the structure, and Rwork is the working R‐factor of the refined structure. Also, analysis of structures in the Protein Data Bank suggests that many data sets have been truncated at high resolution, thereby improving the R‐factor statistics. To discourage this practice, it is proposed that the resolution of a dataset be defined as the resolution of the shell of data where falls to 1. The proposed goodness‐of‐fit metric encourages investigators to use all the data available rather than a truncated subset.
Bayesian analysis of Turkish Income and Living Conditions data, using clustered longitudinal ordinal modelling with Bridge distributed random effects

This article is motivated by the panel surveys, called Statistics on Income and Living Conditions (SILC), conducted annually on (randomly selected) country representative households to monitor EU 2020 aims on poverty reduction. We particularly consider the surveys conducted in Turkey within the scope of integration to the EU. Our main interests are on health aspects of economic and living conditions. The outcome is self-reported health that is clustered longitudinal ordinal, since repeated measures of it are nested within individuals and individuals are nested within families. Economic and living conditions have been measured through a number of individual- and family-level explanatory variables. The questions of interest are on the marginal relationships between the outcome and covariates that we address using a polytomous logistic regression with Bridge distributed random effects. This choice of distribution allows us to directly obtain marginal inferences in the presence of random effects. Widely used Normal distribution is also considered as the random effects distribution. Samples from the joint posterior densities of parameters and random effects are drawn using Markov Chain Monte Carlo. Interesting findings from the public health point of view are that differences were found between the subgroups of employment status, income level and panel year in terms of odds of reporting better health.
Towards Interactive and Realistic Rendering of 3D Fetal Ultrasound via Photon Mapping

Three-dimensional (3D) ultrasound is extensively used in obstetrics and gynecology, and realistic rendering results can both enhance the accuracy of diagnosis and facilitate communication between obstetricians and pregnant women. This paper proposes an interactive and realistic rendering method using global illumination effects for 3D ultrasound images with low signal-to-noise-ratio (SNR) values. The method is capable of generating both single and multiple scattering effects, and separately rendering these two illuminations using ray-casting. Volumetric photon mapping is used to simulate indirect illumination. In order to avoid complex storage structures and accelerate the computational speed, a new screen-space destiny estimation is proposed to calculate the radiance of each photon. In addition, to solve the issue of the foetal skin exceeding the dynamic range capability of the display device, high-dynamicrange (HDR) methods are further incorporated in our method. Experiments demonstrate that our method can produce realistic rendering results with enhanced depth information compared to traditional approaches.
The Cow, the Cook, and the Quaker: Fifty Years of Spokes-Character Advertising

This study explores how spokes-characters in print ads have changed over time from the 1950s to the present. A content analysis of spokes-character ads in one men's and one women's magazine shows that spokes-character advertising is an important communication tool directed at both male and female audiences. Contrary to previous speculation, the use of spokes-characters in magazine ads does not appear to be increasing. Spokes-character ad styles have changed over time but no increase in female or non-white characters seems to have occurred since the 1950s. Differences between the types of spokes-characters used to appeal to male and female audiences are noted.
Multiuser MIMO-MAC capacity in the low SNR regime: Channel knowledge and double-scattering

We investigate the sum capacity of the uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) in the low signal-to-noise ratio (SNR) regime. For each user, the MIMO channel is modeled according to a general class of stochastic channel matrices, known as double-scattering. Assuming that each user knows only their own spatial correlation matrices and employs optimal statistical beamforming transmission, we present new analytical approximations for the sum capacity of the MIMO-MAC for low SNR values. Our approximations are accurate, and lead to key insights into the effect of correlation.
Churn Resilience of Distributed Content Service System on Smart Terminals Network

Recently, the content service system is shifting from a centralized model to distributed model with the capabilities of terminal devices increasing. In addition to providing service to local users, the smart terminals utilize unused bandwidth and storage resources to provide proximity services to neighbor terminals in the overlay network. How to ensure the performance of the distributed system with the continuous process of terminals joining or leaving the overlay network (churn) is a crucial issue. Existing strategies maintain the system’s service under churn through the central server scheduling or constructing complex topology. In this paper, we propose a hierarchical topology for terminals network. The inter-cluster and intra-cluster overlay use the unstructured network. The connectivity of topology is easy to be maintained under the churn. Further, we offer a decentralized replications diffusion and maintenance mechanism on the topology. Under the churn of terminals, the number of replications can be maintained autonomously to a reasonable amount. In the ordinary churn, the success rate of services in our network only drops by about 9% and is well kept. The mechanism can also quickly restore the performance of the network under the extremely churn.
Log Layering Based on Natural Language Processing

With the increasing number and variety of logs, the requirement of storage space is growing rapidly. Meantime, the speed and accuracy of querying in massive logs are becoming increasingly important. Although the well-built distributed storage technique solves the problem of mass storage and fast query, the cost is too high. As logs are created as the method to trace the historical operation, the requirement for query rate is not high. To balance the storage cost and query rate, this paper proposes a real-time log layering storage technique based on natural language processing. According to the characteristics of the log data, this technique is combined with the text language processing technique. It compresses the real-time log data effectively while considering the query efficiency. Firstly, the method extracts the feature of each log that flows in, which will be the type name of the log. Then, the method performs word segmentation on the log and encodes each word to store the key value pairs. Finally, the key value pairs of the log are stored in the memory, and the code of each log is stored in the database. Experiments show that this method can ensure the integrity of the data effectively, decompression time dropped to 40%, compression rate down to 35%.
Dependency Parsing for Identifying Hungarian Light Verb Constructions

Light verb constructions (LVCs) are verb and noun combinations in which the verb has lost its meaning to some degree and the noun is used in one of its original senses. They often share their syntactic pattern with other constructions (e.g. verbobject pairs) thus LVC detection can be viewed as classifying certain syntactic patterns as light verb constructions or not. In this paper, we explore a novel way to detect LVCs in texts: we apply a dependency parser to carry out the task. We present our experiments on a Hungarian treebank, which has been manually annotated for dependency relations and light verb constructions. Our results outperformed those achieved by state-of-the-art techniques for Hungarian LVC detection, especially due to the high precision and the treatment of long-distance dependencies.
A Lunchtime Walk in Nature Enhances Restoration of Autonomic Control during Night-Time Sleep: Results from a Preliminary Study

Walking within nature (Green Exercise) has been shown to immediately enhance mental well-being but less is known about the impact on physiology and longer lasting effects. Heart rate variability (HRV) gives an indication of autonomic control of the heart, in particular vagal activity, with reduced HRV identified as a risk factor for cardiovascular disease. Night-time HRV allows vagal activity to be assessed whilst minimizing confounding influences of physical and mental activity. The aim of this study was to investigate whether a lunchtime walk in nature increases night-time HRV. Participants (n = 13) attended on two occasions to walk a 1.8 km route through a built or a natural environment. Pace was similar between the two walks. HRV was measured during sleep using a RR interval sensor (eMotion sensor) and was assessed at 1–2 h after participants noted that they had fallen asleep. Markers for vagal activity were significantly greater after the walk in nature compared to the built walk. Lunchtime walks in nature-based environments may provide a greater restorative effect as shown by vagal activity than equivalent built walks. Nature walks may improve essential recovery during night-time sleep, potentially enhancing physiological health.