Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Organizational Responses to Institutional Complexity: A Cross-Case Study of Three European Universities

European universities traditionally embedded in the institutional logics of academic professionalism as well as democratization and regulation face the emergence of a new managerial logic generating conflicting demands and triggering hybrid organizational responses. This article investigates hybridization processes by analyzing how multiple institutional logics play out among organizational attributes (strategic positioning, organizational design, governance systems and identity) over long periods. The longitudinal cross-case analysis reveals different hybrid responses made of various forms of segmentation and blending. Furthermore, it informs the debate on the stability of hybrid responses by tracking various hybrid response paths.
Design Criteria for Instructional Videos

The use of videos in teaching is becoming more and more widespread. For this purpose, not only existing videos are used, but also exclusive videos are created. There are different types of instructional videos used and created for teaching. It is useful to know which design principles these videos should follow in order to evaluate the quality of existing learning videos and to know the criteria one should consider when creating exclusive instructional videos. Therefore, questions arise as to how these videos should be designed to equally support theory and practice and what educational requirements they should meet for the use in teaching. These questions are answered with the framework of the study in hand that applied the method design-based research, incorporating qualitative and quantitative research methods. Survey instruments were guided interviews with experts, questionnaires for school students and a focus group. Participants in the study were teachers and students from the Vienna University of Teacher Education. The field of application was nutrition and consumer education for teaching students of the middle school. The results are presented in the form of design principles.
Energy-efficient protocols and hardware architectures for transport layer security

The Internet of Things (IoT) has introduced a vision of an Internet where computing and sensing devices are interconnected. Digitally connected devices are encroaching on every aspect of our lives, including our homes, cars, offices, and even our bodies. Researchers estimate that there will be over 50 billion wireless connected devices by 2020 [1]. On one hand, the IoT enables fundamentally new applications, but on the other, these devices are attractive targets for cyber attackers, thus making IoT security a major concern. Datagram Transport Layer Security (DTLS) is considered to be one of the most suited protocols for securing the IoT. However, computation and communication overheads make it very expensive to implement DTLS on resourceconstrained IoT sensor nodes. In this work, we profile the energy costs of DTLS version 1.3, using experimental models for cryptographic computations and radio-frequency (RF) communications. Based on this analysis, we propose protocol optimizations that can reduce the overall energy consumption of DTLS up to 45%, while still maintaining the same security strength of the standard DTLS. We discuss energy-efficient architectures for implementing the standard cryptographic primitives AES (Advanced Encryption Standard), SHA (Secure Hash Algorithm) and ECC (Elliptic Curve Cryptography) in hardware. Our hardware can provide more than 2,500 times reduction in energy consumption compared to traditional software implementations. These hardware primitives are integrated with dedicated control and memory to design a DTLS co-processor that can accelerate the complete DTLS state machine in hardware, thus minimizing the energy consumption due to DTLS computations. The proposed DTLS core is integrated with a RISC-V micro-processor to accurately profile these functions, as well as design custom protocols using standalone cryptographic instructions. Thesis Supervisor: Anantha P. Chandrakasan Title: Vannevar Bush Professor of Electrical Engineering and Computer Science
Generalized Quadratically Constrained Quadratic Programming and its Applications in Array Processing and Cooperative Communications

In this thesis, we introduce and solve a particular generalization of the quadratically constrained quadratic programming (QCQP) problem which is frequently encountered in the fields of communications and signal processing. Specifically, we consider such generalization of the QCQP problem which can be precisely or approximately recast as the difference-of-convex functions (DC) programming problem. Although the DC programming problem can be solved through the branch-and-bound methods, these methods do not have any worst-case polynomial time complexity guarantees. Therefore, we develop a new approach with worst-case polynomial time complexity that can solve the corresponding DC problem of a generalized QCQP problem. It is analytically guaranteed that the point obtained by this method satisfies the Karsuh-Kuhn-Tucker (KKT) optimality conditions. Furthermore, there is a great evidence of global optimality in polynomial time for the proposed method. In some cases the global optimality is proved analytically as well. In terms of applications, we focus on four different problems from array processing and cooperative communications. These problems boil down to QCQP or its generalization. Specifically, we address the problem of transmit beamspace design for multiple-input multiple-output (MIMO) radar in the application to the direction-of-arrival estimation when certain considerations such as enforcement of the rotational invariance property or energy focusing are taken into account. We also study the robust adaptive beamforming (RAB) problem from a new perspective that allows to develop a new RAB method for the rank-one signal model which uses as little as possible and easy to obtain prior information. We also develop a new general-rank RAB method which outperforms other existing state-of-the-art methods. Finally, we concentrate on the mathematical issues of the relay amplification matrix design problem in a two-way amplify-and-forward (AF) MIMO relaying system when the sum-rate, the max-min rate, and the proportional fairness are used as the design criteria.
The Influence of Science Teachers on High School Students’ Science Motivation: An Analysis Using a Nationally Representative Large-Scale Data Set

: This study examined the degree to which science teacher qualifications, characteristics, and practices influence high school students’ motivational beliefs about science. Marrying a social cognitive career theoretical framework with a teacher quality theoretical framework, this study explored teacher-related variables as a contextual variable shaping students’ motivation towards science. A nationally representative, large-scale data set—the High School Longitudinal Study 2009 (HSLS:09) was used to conduct hierarchical regression analyses. After controlling for student demographics, results indicated that teacher’s self-efficacy beliefs, a college or graduate science degree, and science instructional practices were found to be positively associated with students’ motivation in science
Quality Assessment of High-Resolution Images with Small Distortions after Compression

Image quality assessment still remains a highly relevant problem, and objective quality assessment methods are being actively developed. The methods, based on the Structural Similarity index method, have become very popular during the last decade. However, their sensitivity has been investigated using only small images and only in the cases of obvious image distortions. In this paper, we have investigated a quality assessment of high-resolution images with low distortions after compression using the Structural Similarity index method. The specific cases, related to the usage of this method for high-resolution images, are analyzed, and the problems that occur when using the method are identified. Experimental investigations have shown that image downsampling is necessary when the image quality is assessed by the Structural Similarity index method. Moreover, a sensitive algorithm suitable for the comparison of the quality of high-resolution images with small distortions is proposed and investigated in the paper.
An Adaptive Approach to Identify Genre in Music Videos Using Word2Vec Model

The vector representations of words presented by Word2Vec model have been shown to be very useful in many application developments due to the semantic information they convey. This paper proposes a similar form, the MusicGenre2Vec. MusicGenre2Vec represents the numerical genre features of music segments inside the vector with the intention to describe the phonetic systems of the tune segments in an excellent way. We are hoping the vector representations obtained in this way can describe more precisely the phonetic structures of the Music indicators, so the Music segments that sound alike would have vector representations close by within the space. This form of depiction is called MusicGenre2Vec in this paper. The proposed system gives 80% of accuracy in finding the Genre of a video song.
FPGA Realisation of n-QAM Digital Modulators

ABSTRACT This article introduces a novel modulators’ design approach by applying direct digital generation capabilities that improve the development, synthesis, and verification of wireless transmitter systems. In this manner, digital modulators’ that are able to process 8-, 16-, and 64-quadrature amplitude, are realised herein by using embedded systems like the well-known field-programmable gate array (FPGA). It is worth mentioning that using FPGAs allows us to perform a reduction of the hardware resources. This issue also allows relaxing the operating conditions of the systems in the digital domain. Several experiments are shown to demonstrate the usefulness of an FPGA, like using Stratix III DSP Development Kit, and to confirm the good agreement of the experimental results with theoretical ones.
prediction Modeling for Design Space Exploration in Optical Network on Chip

In at least a decade chip multiprocessors (CMP) have been dominating new commercial releases due to computational advantages of parallel computing cores on a single chip. Network on Chip (NoC) has emerged as an interconnection network of CMPs. But significant bandwidth that is required for multicore chips is becoming a bottleneck in the traditional (electrical) network on chip, due to delays caused by long wires in the electric NoC. Integration of photonic links with traditional electronic interconnects proposes a promising solution for this challenge. Since there are numerous design parameters for opto-electrical network architectures, an accurate evaluation is needed to study the impact of each design parameter on network performance, and to provide the most suitable network for a given set of applications, a power or a performance goal. In this thesis, we present a prediction modeling technique for design space exploration of an opto-electrical network on chip. Our proposed model accurately predicts delay (includes network packet latency and network contention delay) and energy (includes static and dynamic energy consumption) of the network. Specifically, this work addresses the fundamental challenge of accurate estimation of desired metrics without having to incur high simulation cost of numerous configurations of the optical network on chip architecture. We reduce the number of required simulations by accurately selecting the parameters that have the most impact on the network. Furthermore, we sparsely and randomly sample the designs build using these parameters from an Optical Network on Chip (ONoC) design space, and simulate only the sampled designs. We validate our model with three different applications executing on a large set of network configurations in a large optical network on chip design space. We achieve average error rates (root relative squared error) as low as 5.5% for the delay and 3.05% for the energy consumption.
Umbilical Cord: A system for ubiquitous computing

Computers are still too difficult to maintain and use. Many people are dependent on knowledgeable friends to assist them in administering their computer. Even professional users still have trouble getting their computers to do what they want. An alternative kind of computing, called Ubiquitous Computing, has been proposed. Ubiquitous Computing'aims to make computing widely available, easy to use, and perhaps even completely transparent to the user. Umbilical Cord is•intended to be the first step in researching Ubiquitous Computing at CSUSB. It implements a model scalable network with a client-server architecture that features consistent user interaction and global access to user data. It is based on the Linux operating system which can be leveraged for future research due to the open nature of its source code. It also features a scalable network swap. Umbilical Cord employs diskless clients that download their OS and applications from servers. These diskless clients prototype inexpensive machines that could be placed
EFFECTIVE ANALYSIS OF BRAIN TUMOR USING HYBRID DATA MINING TECHNIQUES

In the recent era there has been a rapid growth in the availability of medical databases and medical imagery in the past few years, and the uncertainty involved in the effective prediction of diseases from these databases made the research community to take up the challenges in this domain.The Central Nervous System of mankind being is mainly composed of Spinal Cord, Brain and Neurons. Largest part of brain is Cerebrum. The human brain throws good number of challenges to the research community. The Brain Tumour or the Intra Cranial Neoplasm is formed due to the irregular growth of cells in the brain. This sort of irregular growth of cells in brain damages frontal, temporal and parietal lobes thereby results in abnormal behaviour. Machine learning algorithms provide computers with the ability to learn without being explicitly programmed. The clinical brain data set was analysed effectively using machine learning algorithms and made conclusions on the results. In this paper we applied Hybrid Data Mining methods which in turn consists of Clustering, Classification andAssociation techniques and further analysed the results using some statistical techniques.
LandRate toolbox: An adaptable tool for eye movement analysis and landscape rating

The paper presents a new software tool called LandRate toolbox. The toolbox constitutes an extension of EyeMMV toolbox and supports the generation of a full analysis report based on experimental data collected through eye tracking methods. Additionally, a new aggregated index (LRI), appropriate for the performance of landscape rating procedures, is introduced. The index combines both quantitative eye tracking metrics and experts’ opinions while it can be easily adapted in similar fields.
Vehicle Routing Problem with Time Windows Study

Customer demands of logistics timeliness is getting higher and higher. The research on vehicle routing problem with time windows, can improve the level of logistics services. This paper studies the optimization of vehicle routing problem with time window based on the above background. Thesis assumes that customers tend to be in a specific time period to accept delivery services, beyond the delivery service time will give punishment cost of time, in order to reflect a decrease in the level of service. The paper takes the minimum of total cost as the target, establishes the nonlinear distribution path model, and in the model, corrects the errors of the time constraint in the literature. Finally, the practical case is introduced, the model is established, the genetic algorithm is used to solve the problem, and the optimal distribution path is obtained. Introduction In most of the previous vehicle routing problem studies, people often pay attention to the total mileage of the least, so that the total cost of the corresponding minimum. But with the customer's personalized demand is getting higher and higher, we need to consider the customer to receive goods time. The customer does not meet the delivery, but wants to deliver the goods on time. Different customer time requirements are different, personalized service more and more competitive advantage. Especially with the online shopping, the rapid development of e-commerce, almost everyone online shopping, so the number of express parcels increasing dramatically. At the same time, the demand for delivery has gone up, but the customer demand for distribution is also getting higher and higher. Therefore, the solution of vehicle routing problem with time window [1] (VRPTW) is proposed. Through the solution of adaptive genetic algorithm, the minimum distribution cost and optimal distribution route can be obtained, which can meet the realistic requirements of logistics company and customer time, improve vehicle utilization, reduce delivery costs. Therefore, for logistics and related enterprises, the study of this problem can improve the economic efficiency of enterprises, reduce energy consumption, improve customer service satisfaction, thereby enhancing the core competitiveness of enterprises, but also for energy conservation and emission reduction provides an effective reference program. The Research Status at Home and Abroad In the case of vehicle routing problems with time windows, domestic and foreign scholars have done a lot of research work. Liu Cheng and others in 2005 for the genetic algorithm in the solution VRPTW initial population of the unity, proposed a parallel genetic algorithm, the algorithm for different populations with different initialization methods, the effect is ideal. Yang Yudong [2] and so on in 2006 to introduce the direct arrangement of the solution, improve the simulated annealing algorithm, improve the efficiency of solving VRPTW. Zhang Liyan [3] is equal to 2006 using the
Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.

BACKGROUND Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. OBJECTIVES The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. METHODS The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. RESULTS Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. CONCLUSION Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.
Changeful Tales: Design-Driven Approaches Toward More Expressive Storygames

Author(s): Reed, Aaron | Advisor(s): Wardrip-Fruin, Noah; Mateas, Michael | Abstract: Stories in released games are still based largely on static and predetermined structures, despite decades of academic work to make them more dynamic. Making game narratives more playable is an important step in the evolution of games and playable media as culturally relevant art forms. In the same way interactive systems help students learn about complicated subjects like physics in a more intuitive and immediate way than static texts, more dynamic interactive stories open up new ways of understanding people and situations. Such dreams remain mostly unrealized in released and playable games.In this dissertation I will describe a number of design and technical solutions to the problem of creating more expressive and dynamic storygames, informed by a practice-based approach to game production. I will first define a framework for the analysis of games, including especially the terms storygame (a playable system with units of narrative where the understanding of the interconnectedness between story and system is crucial) and the notion of narrative logics (the set of processes that define how player input affects the next unit of story presented by the system). I will exercise this framework on an existing and well-known storygame genre, the adventure game, and use it to make a number of claims about the mechanics and dynamics of narratives in this genre that are borne out by an analysis of how contemporary games adopting some of its aesthetics succeed and fail. I will then describe three emerging storygame modes that are still in the process of being defined, developing a critical framework for each informed by close readings and historical analysis, and considering what design and technical innovations are required to fully realize the new mode's potential. These three modes I discuss are sculptural fiction (which shifts the focus from navigating to building a structure of narrative nodes), social simulation (games that explore the possibility space created by a set of simulated characters and rules for social interaction), and collaborative storygames (in which the lexia are generated at least in part by the participants during play). Each theoretical chapter is paired with a case study of one or several fully completed and released games I have created or co-created in that mode, to see how these design ideas were realized and technical advancements implemented in practice. I will conclude each section with applied advice for game makers hoping to work in these new spaces, and new technological developments that will help storygames continue to evolve and prosper.