Miracle-dz/UniFAR-FTUs huggingface dataset:
Full Flip, Half Flip and No Flip:Evaluation of Flipping an Introductory Programming Course

While some research has suggested that video lectures are just as effective as in-person lectures to convey basic information to students, not everyone agrees that the flipped classroom model is an effective way of educating students. This research explores traditional, semi-flipped and fully-flipped classroom models by comparing three sections of an Introduction to Programming (Java) course that were taught at the same institution in Spring 2015 by three different instructors using three different paradigms. The data and observations collected suggests that incorporating in-class activities improves student satisfaction but a semi-flipped classroom, including in-class activities, some outsideclass lecture videos, and some in-class lectures, may generally provide the best overall experience for the students. However, while students may be more satisfied and get more programming practice in a flipped paradigm, overall student performance did not appear to be greatly impacted.
Transfer learning for visual recognition and text categorization

In recent decades, transfer learning has attracted intensive attention from researchers and become a hot research direction in the field of machine learning. Different from traditional machine learning, transfer learning allows that the training and testing data can be from different domains (i.e., different data distributions and/or different feature spaces). This characteristic helps us to learn good classifiers for the domain of interest, where there have only a limited or even no labeled training data, by utilizing many existing data from other related sources. Because of this, transfer learning techniques have already been widely used in many areas such as machine learning, data mining, computer vision, etc. In this thesis, we propose several transfer learning frameworks, based on which a number of transfer learning are developed and applied for different real-world applications such as visual recognition and text categorization. Specifically, first we propose a domain transfer framework based on multiple kernel learning to minimize the data distribution mismatch between domains. Two methods are further developed under this framework to simultaneously learn a kernel function modeled by multiple kernel learning as well as a robust target classifier. We demonstrate the effectiveness of our proposed methods in the video concept detection and text classification tasks. Second, we present a visual event recognition framework for consumer videos by leveraging a large number of web videos, in which a pyramid matching method and a transfer learning method have been proposed to measure the distances between videos and cope with the data distribution mismatch between the consumer and web video domains, respectively. In the proposed transfer learning method, we define the target decision function as a linear combination of pre-learned classifiers and a perturbation function modeled by multiple kernel learning, such that we can better fuse the knowledge learned from multiple levels of a video and
ANALISIS KEPUASAN PELAYANAN PERGURUAN TINGGI (Kasus pada Prodi Magister Pendidikan Ekonomi Unnes)

Fokus penelitian ini adalah pengukuran kepuasan pelayanan terhadap mahasiswa, penelusuran lulusan, dan kepuasan pengguna lulusan. Populasi penelitian adalah mahasiswa Prodi Magister Pendidikan Ekonomi PPs Unnes yang aktif pada semester gasal 2015/2016, lulusan, dan pengguna lulusan. Alat yang digunakan adalah instrumen SERVQUAL yang dikembangkan dari Parasuraman et al. (1994) untuk mengukur kepuasan mahasiswa. Instrumen pengukuran kesesuaian lulusan dan kepuasan pengguna lulusan berdasarkan pedoman akreditasi BAN PT. Analisis data dilakukan dengan analisis statistik deskriptif dan importance-performance analysis (IPA). Hasil penelitian menunjukkan bahwa berdasarkan analisis kepuasan layanan terdapat dua dimensi layanan yang harus menjadi prioritas utama untuk segera diperbaiki, yaitu dimensi acces dan reliability. Hasil lainnya menunjukkan bidang pekerjaan alumni telah sesuai dengan visi misi prodi dan alumni berpendapat materi dan proses perkuliahan sangat relevan dengan bidang pekerjaan yang digeluti sekarang sehingga sangat membantu menyelesaikan pekerjaan. Terakhir, kepuasan pengguna terhadap kompetensi lulusan termasuk dalam kategori sangat bagus. Kata kunci : mahasiswa, lulusan, pengguna lulusan, kualitas layanan, Importance-Performance Analysis ANALYSIS TOWARDS UNIVERSITIES' SERVICES SATISFACTION Abstract:The study focuses on the measurement of satisfaction withstudent services, tracer study and stakeholder satisfaction. The study population is students of Economics Education Master Program of PPs Unnes in the odd semester of 2015/2016, graduates and stakeholders. SERVQUAL instrument developed by Parasuraman et al. (1994) is employed to measure student satisfaction. The compatibility between the graduate and stakeholder satisfaction is measured based on BAN PT accreditation guidelines. Data were analyzed using descriptive statistical analysis, and the Importance-Performance Analysis (IPA). The results show that, in terms of service satisfaction analysis, two dimensions of service should be improved immediately, i.e. access and reliability. It has also been revealed that the fields of alumni’s job have been in accordance with the vision and mission of the study program and that the material and lecture have been highly relevant to their field of work. Lastly, stakeholders are found to be highly satisfied with graduate competencies. Keywords: students, stakeholder, servicequality, Importance-Performance Analysis
Comparing Group Techniques: The Effectiveness of using GSS-Supported Focus Groups and Traditional Focus Groups

The use of focus group interviews has helped researchers in many fields harvest rich data through the face-to-face interaction and communication between focus group participants. There are also several factors that hamper this method from collecting the best possible output such as production blocking, social loafing and evaluation apprehension. When the purpose of the research is to generate as many relevant and high quality ideas and insights as possible, focus groups may not be the most effective or practical method to use. The study reported here used computer-mediated communication (CMC) with the help of Group Support System (GSS) technologies to overcome the problems associated with traditional focus groups. The use of CMC over general face-to-face (FtF) methods has been consistently shown to increase the number of relevant ideas generated in a group. This research used the good-idea-count to determine ideation quality which assesses the degree to which the ideation activity produced ideas that are helpful in attaining a goal. Results showed no differences between the groups for relevant or high quality insights and ideas generated. Based on our findings we can conclude that CMC is as effective as FtF collaboration for the purpose of idea generation. Participants were also more satisfied with their performance in the traditional focus groups than the GSS sessions. The results suggest that the use of CMC was not sufficient to lead to more idea generation but the CMC transcripts are still automatically generated and more easily interpretable.
Efficient Feature Extraction for Shape Analysis, Object Detection and Tracking

During the course of this thesis, two scenarios are considered. In the first one, we contribute to feature extraction algorithms. In the second one, we use features to improve object detection solutions and localization. The two scenarios give rise to into four thesis sub-goals. First, we present a new shape skeleton pruning algorithm based on contour approximation and the integer medial axis. The algorithm effectively removes unwanted branches, conserves the connectivity of the skeleton and respects the topological properties of the shape. The algorithm is robust to significant boundary noise and to rigid shape transformations. It is fast and easy to implement. While shape-based solutions via boundary and skeleton analysis are viable solutions to object detection, keypoint features are important for textured object detection. Therefore, we present a keypoint featurebased planar object detection framework for vision-based localization. We demonstrate that our framework is robust against illumination changes, perspective distortion, motion blur, and occlusions. We increase robustness of the localization scheme in cluttered environments and decrease false detection of targets. We present an off-line target evaluation strategy and a scheme to improve pose. Third, we extend planar object detection to a real-time approach for 3D object detection using a mobile and uncalibrated camera. We develop our algorithm based on two novel naive Bayes classifiers for viewpoint and feature matching that improve performance and decrease memory usage. Our algorithm exploits the specific structure of various binary descriptors in order to boost feature matching by conserving descriptor properties. Our novel naive classifiers require a database with a small memory footprint because we only store efficiently encoded features. We improve the feature-indexing scheme to speed up the matching process creating a highly efficient database for objects. Finally, we present a model-free long-term tracking algorithm based on the Kernelized Correlation Filter. The proposed solution improves the correlation tracker based on precision, success, accuracy and robustness while increasing frame rates. We integrate adjustable Gaussian window and sparse features for robust scale estimation creating a better separation of the target and the background. Furthermore, we include fast descriptors and Fourier spectrum packed format to boost performance while decreasing the memory footprint. We compare our algorithm with state-of-the-art techniques to validate the results.
Synthesis of porous iron – zirconium mixed oxide fabricated ethylene diamine composite for removal of cationic dye

Synthesis of iron-zirconium binary oxide fabricated ethylene diamine (IZBO-en) composite involved two step synthesis including mixed binary oxide synthesis followed by synthesis of binary oxide-ethylene diamine composite. Mixed binary oxide was synthesized by co-precipitation method using ammonia as neutralizing agent. Binary oxide based ethylene diamine composite was synthesized using sodium dodecyl sulfate and potassium persulfate as reagents in reaction. Yield of product was 88%. Characterization of the material was done using Fourier-transform infrared spectroscopy, scanning electron microscopy, and Brunauer–Emmett–Teller analysis. Surface area was found to be 54.74 m2 g–1. The synthesized material was efficient for removal of cationic dye methylene blue which is known for its toxicological effects. Adsorption parameters like contact time, dose, interference and pH were studied. Maximum adsorption capacity was found to be 242.79 mg g–1. Kinetic data suggested the mechanism of adsorption follows pseudo-second-order rate (R2 = 0.998) with a rate constant of 2.788 mg g–1 min–1 and follows Langmuir adsorption isotherm model (R2 = 0.987). Thermodynamic study revealed the process to be spontaneous and feasible with large negative free energy (ΔG = –9.54 kJ mol–1). Column study shows a removal of 600 ml 2 mg L–1 with 1 g material. Desorption was achieved with 79% regeneration in acidic medium. Real wastewater from nearby textile industry was tested and found to get decolourized with the material. In conclusion, IZBO-en can be efficiently used in detoxification of cationic dyes.
Evaluating the impact of block scheduling

John Joseph Hourani Evaluating the Impact of Block Scheduling 1999 Dr. Ronald L. Capasso School Administration The purpose of this study was to evaluate and describe the effectiveness of a 4x4 semester block schedule with respect to academic achievement, discipline, attendance and the school climate of Penns Grove High School. A longitudinal trend study design was utilized to compare statistics from the school's first two years under the block schedule to the school's final two years of the traditional schedule. Such statistics include the frequency of students obtaining A's, F's, and honor roll status, as well as monthly attendance rates, and discipline referral counts. Additionally, a modified action research design is used to examine the new schedule's effects on the school climate. This study found the block schedule to have no effect on the number of students receiving A's, honor roll lists, or attendance rates. However, the number of students receiving F's and discipline referral counts revealed noteworthy reductions which positively correlate to the implementation of the block schedule. Additionally, morale, motivation, and teaching styles were found to improve due to the implementation of the block schedule.
Protonation state of radiation-produced cytosine anions and cations in the solid state: EPR/ENDOR of cytosine monohydrate single crystals x-irradiated at 10 K

Single crystals of cytosine monohydrate were X-irradiated at 10 K and examined using K-band EPR, ENDOR, and FSE spectroscopy at temperatures between 10 K and room temperature. Three radicals dominated the low-temperature spectra. Radical RI is the cytosine anion protonated at the N3-position. A large coupling to HC6 and smaller couplings to HN3 and to the two amino protons were detected and their characteristics unambiguously established the structure of this radical. Radical RII exhibits major couplings to HC5, N1, and to the nitrogen and the protons of the amino group
Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications

Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties create bias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a wellknown data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics.
Oxidation of Sulfides to Sulfoxides and Sulfones with 30% Hydrogen Peroxide under Organic Solvent- and Halogen-Free Conditions.

Abstract Aromatic and aliphatic sulfides are oxidized to sulfoxides or sulfones in high yield with 30% hydrogen peroxide under organic solvent- and halogen-free conditions. Dialkyl and alkyl aryl sulfides are cleanly oxidized to sulfoxides using aqueous hydrogen peroxide without catalysts. The best catalyst for the sulfone synthesis consists of sodium tungstate, phenylphosphonic acid, and methyltrioctylammonium hydrogensulfate. Co-existing primary or secondary alcohol or olefinic moieties are unaffected under such conditions.
Progress in the Development of a Superconductive High-Resolution ADC

This paper is a progress report on the development of a high-resolution analog-to-digital converter (ADC) which uses a phase modulation/demodulation architecture. Presented are an analysis of the performance limitations, proposed design improvements, and recent test results. Test results for new versions of room temperature VXIbased interface and processing modules are also described.
Boards That Make A Difference

Thank you very much for reading boards that make a difference. Maybe you have knowledge that, people have look numerous times for their chosen readings like this boards that make a difference, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their laptop. boards that make a difference is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the boards that make a difference is universally compatible with any devices to read.
Linking Dutch Wikipedia Categories to EuroWordNet : SA-OT accounts for pronoun resolution in child language

Wikipedia provides category information for a large number of named entities but the category structure of Wikipedia is associative, and not always suitable for linguistic applications. For this reason, a merger of Wikipedia andWordNet has been proposed. In this paper, we address the word sense disambiguation problem that needs to be solved when linking Dutch Wikipedia categories to polysemous Dutch EuroWordNet literals. We show that a method based on automatically acquired predominant word senses outperforms a method based on word overlap between Wikipedia supercategories and WordNet hypernyms. We compare the coverage of the resulting categorization with that of a corpus-based system that uses automatically acquired category labels.
Is Compensated by Cyclin D2 in Cyclin of Normal B-1a Cells, but Loss of Cyclin D3 Disruption of Cyclin D3 Blocks Proliferation

ChilesSean M. Gurdak, Thomas L. Rothstein and Thomas C. Jennifer M. Mataraza, Joseph R. Tumang, Maria R. Gumina,http://www.jimmunol.org/content/177/2/787J Immunol€2006; 177:787-795; ;Referenceshttp://www.jimmunol.org/content/177/2/787.full#ref-list-1This article cites 51 articles, 24 of which you can access for free at: Subscriptionshttp://jimmunol.org/subscriptionsInformation about subscribing to The Journal of Immunology is online at: Permissionshttp://www.aai. org/ji/copyright.htmlSubmit copyright permission requests at: Email Alertshttp://jimmunol.org/cgi/alerts/etocReceive free email-alerts when new articles cite this article. Sign up at:
A High-productivity Framework for Multi-GPU computation of Mesh-based applications

The paper proposes a high-productivity framework for multiGPU computation of mesh-based applications. In order to achieve high performance on these applications, we have to introduce complicated optimized techniques for GPU computing, which requires relatively-high cost of implementation. Our framework automatically translates user-written functions that update a grid point and generates both GPU and CPU code. In order to execute user code on multiple GPUs, the framework parallelizes this code by using MPI and OpenMP. The framework also provides C++ classes to write GPU-GPU communication effectively. The programmers write user code just in the C++ language and can develop program code optimized for GPU supercomputers without introducing complicated optimizations for GPU computation and GPU-GPU communication. As an experiment evaluation, we have implemented multi-GPU computation of a diffusion equation by using this framework and achieved good weak scaling results. By using peer-to-peer access between GPUs in this framework, the frameworkbased diffusion computation using two NVIDIA Tesla K20X GPUs is 1.4 times faster than manual implementation code. We also show computational results of the Rayleigh-Taylor instability obtained by 3D compressible flow computation written by this framework.
Adaptive Connectivity for Vehicular Cyber-Physical Systems

Vehicular connectivity is regarded as a backbone for communications in intelligent vehicular CPS to provide timely information to drivers or to provide feedback to the vehicles on the road to enhance road safety and overall traffic efficiency. Note that about “60% of roadway collisions could be avoided if the operator of the vehicle was provided warning at least one-half second prior to a collision” [1, 2]. When vehicles use fixed transmit range/power for communications, they may not be able to connect with their neighboring vehicles using single hop or multiple hops in case of sparse vehicle density. At the same time, when high and fix transmission range/power is used, there could be broadcast storm problem because of rebroadcast of the messages from several vehicles within the given transmission range. Thus, individual vehicles should be able to adapt their communication parameters including transmission power/range based on their corresponding local observations without any intervention from driver/users [3]. Furthermore, quick data transmission is essential for emergency related messages in vehicular networks to disseminate them in a timelymanner [4–9] using vehicle-to-vehicle and/or a vehicle-to-roadside-to-vehicle communications. When all messages in vehicular CPS are treated equally, time sensitive emergency messages could face higher delay in case of saturated network and drivers/vehicles could not be informed in a timely manner. Thus message differentiation becomes a vital factor for providing means to disseminate time sensitive emergency messages in the network rather quickly. Note that when there is no priority for emergency messages, they could suffer from delays and they would have no purpose after certain time. This chapter investigates an analytical approach for enhancing network performance through dynamic adaptation of transmit power and contention window in vehicular CPS. Transmission range/power is adapted based on both local traffic density and data collision rate in the network. Furthermore, contention window sizes of differentiated messages is adapted based on the data collision rate in the
The effects of increased acetate turnover on glucose-induced insulin secretion in lean and obese humans

Abstract Introduction: Increased endogenous acetate production (Ra) in rodents has been shown to activate the parasympathetic nervous system and thereby promote increased glucose-stimulated insulin secretion (GSIS), increased ghrelin secretion, hyperphagia and obesity. Aim: To examine whether rates of acetate turnover are different in lean versus obese humans and whether increased acetate turnover promotes increased GSIS and increased ghrelin secretion in humans. Methods: Basal acetate Ra was measured following an overnight fast in lean (BMI: 21.3 ± 1.1 Kg/m2) and obese (30.2 ± 0.9 Kg/m2, P = 0.00001) individuals. The subjects underwent two hyperglycemic (10 mmol/L) clamp studies to measure GSIS during a basal acetate infusion and during a high-dose acetate infusion increasing plasma acetate concentrations ∼5-fold. Results: Basal acetate Ra was 30% higher in the lean compared to the obese subjects (257 ± 27 vs. 173 ± 18 μmol/min; P = 0.025). Basal plasma insulin concentrations were 4-fold higher in the obese than the lean subjects (P = 0.008) and increased 5-fold during hyperglycemia in both groups, independent of changes in plasma acetate concentrations. Fasting plasma ghrelin concentrations were 35% lower in the obese compared to the lean subjects (P = 0.015). During the hyperglycemic clamp, plasma ghrelin decreased by 42% in the lean group (P < 0.022 vs. basal) and did not change in the obese group. Conclusion: Rates of endogenous acetate turnover are ∼30% higher in the lean subjects compared to the obese subjects, and increasing plasma acetate turnover does not promote increased GSIS or ghrelin secretion in either group.