Miracle-dz/UniFAR-FTUs huggingface dataset:
Leakage Resilient Leveled <inline-formula><tex-math notation="LaTeX">$\mathsf {FHE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">FHE</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-2726554.gif"/></alternatives></inline-formula> on Multiple Bits Message

Fully Homomorphic Encryption (<inline-formula><tex-math notation="LaTeX">$\mathsf {FHE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">FHE</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-2726554.gif"/></alternatives></inline-formula>) allows computing over encrypted data without decrypting the corresponding ciphertexts, and it constitutes a promising cryptographic primitive to preserve data privacy in the big data computing environments. In general, <inline-formula><tex-math notation="LaTeX">$\mathsf {FHE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">FHE</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq3-2726554.gif"/></alternatives></inline-formula> schemes can be constructed by using the standard Learning with Errors (<inline-formula><tex-math notation="LaTeX">$\mathsf {LWE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">LWE</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq4-2726554.gif"/></alternatives></inline-formula>) assumption, and the current crux lies in how to achieve efficient multi-bit <inline-formula><tex-math notation="LaTeX">$\mathsf {FHE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">FHE</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq5-2726554.gif"/></alternatives></inline-formula> encryption while being leakage-resistent against attackers who may capture the information of cryptographic secret keys via side channel attacks. Based on Berkoff-Liu’s work at TCC’14, we aim to address this issue by giving a new structure of public key matrix with any number of <inline-formula><tex-math notation="LaTeX">$\mathsf {LWE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">LWE</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq6-2726554.gif"/></alternatives></inline-formula> instances, thereby avoiding the use of a straightforward composition to achieve multi-bit <inline-formula><tex-math notation="LaTeX">$\mathsf {FHE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">FHE</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq7-2726554.gif"/></alternatives></inline-formula> encryption under standard <inline-formula><tex-math notation="LaTeX">$\mathsf {LWE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">LWE</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq8-2726554.gif"/></alternatives></inline-formula>. Particularly, our scheme attains provable security.
Information professionals and copyright literacy: a multinational study

Purpose The purpose of this paper is to present findings from a multinational survey on copyright literacy of specialists from libraries and other cultural institutions. Design/methodology/approach This paper is based on a multinational survey of copyright literacy competencies of Library and Information Science (LIS) professionals and those who work in the cultural heritage sector (archives and museums), conducted in 13 countries, namely Bulgaria (BG), Croatia (CR), Finland (FI), France (FR), Hungary (HU), Lithuania (LT), Mexico (MX), Norway (NO), Portugal (PT), Romania (RO), Turkey (TR), UK and USA in the period July 2013-March 2015. An online survey instrument was developed in order to collect data from professionals regarding their familiarity with, knowledge and awareness of, and opinions on copyright-related issues. Findings Findings of this study highlight gaps in existing knowledge of copyright, and information about the level of copyright literacy of LIS and cultural sector professionals. Also attitudes toward copyright learning content in academic education and continuing professional development training programs are investigated. Originality/value This study aimed to address a gap in the literature by encompassing specialists from the cultural institutions in an international comparative context. The paper offers guidance for further understanding of copyright in a wider framework of digital and information literacy; and for the implementation of copyright policy, and the establishment of copyright advisor positions in cultural institutions. The recommendations support a revision of academic and continuing education programs learning curriculum and methods.
Effective Feedback Design Using Free Technologies

Feedback plays a critical role in student learning and performance. However, providing students with effective feedback is challenging in online environments because of physical separation between students and instructors. Technologies can be used to enhance the effectiveness of feedback in online courses. In this article, we propose effective feedback design for online students using freely available technologies. We first review literature on the attributes of feedback that impact its effectiveness. Then, to address such attributes, we propose effective feedback design using three types of free technologies—media sharing, collaborative, and screencasting technologies. For each type of technology, we focus on one representative technology such as VoiceThread for a media sharing tool, a wiki for a collaborative tool, and Jing for a screencasting tool. Based on the analysis of empirical studies using such technologies, effective feedback design was constructed to maximize the affordances of each technology that foster feedback dialogues, help to bring feedback from multiple sources, and encourage students to follow up with feedback.
Computational Fairness: Preventing Machine-Learned Discrimination

Machine learning algorithms called classifiers make discrete predictions about new data by training on old data. These predictions may be hiring or not hiring, good or bad credit, and so on. The training data may contain patterns such as a higher rate of good outcomes for members of certain groups (e.g. racial groups) and a lower rate of good outcomes for other groups. This is quantified by the “80% rule” of disparate impact, which is a legal measure and definition of bias. It is ethically and legally undesirable for a classifier to learn these biases from the data. We propose two methods of modifying data, called Combinatorial and Geometric repair. We test our repairs on three data sets. Experiments show that our repairs perform favorably in terms of training classifiers that are both accurate and unbiased.
The First Language - A Case for Python?

Abstract Python is a fully featured object-oriented programming language. Its commercial use is widespread and increasing, but its use in a teaching setting appears as yet to be limited (but is also increasing). This paper presents some features of Python that might be of interest to those who teach programming, and reports on the experience of using Python as a “pre-programming” language as part of a course aimed at a typical cohort of programming students. It thus considers the potential that Python may have to be used as a “first language”. The results of this work appear to be encouraging, and some initial evaluation has taken place. It is clear that there are many fewer cognitive obstacles to overcome before a novice can start writing programs with Python than with many other languages. It does indeed appear that Python has something to offer as an introductory programming language.
The Electronic Digital Computer in Aircraft Structural Analysis: The Programming of the Argyris Matrix Formulation of Structural Theory for an Electronic Digital Computer

This paper describes the use of preset and programme parameters with the matrix interpretive scheme (which was considered in Part I) for the preparation of general purpose programmes for an electronic digital computer. The implications of partitioning the matrices when using the force method of analysis are considered in detail and the above techniques are applied to develop general purpose programmes for the calculations encountered when using this method of structural analysis. The general case, when all matrices are partitioned; the displacement method of structural analysis, and the use of magnetic tape storage, are considered in Part III.
Recognizing Text Signatures Using Neural Machine Translation

Optical character recognition (OCR) is a technology used to convert scanned text into searchable data. OCR systems have achieved up to 99% recognition rates when working with clean and well-formatted documents under optimal conditions. However, the results are less promising under suboptimal conditions, for example when faced with damaged or obfuscated text. We propose a new method for recognizing words that are obfuscated in a particular way. This recognition is accomplished by utilizing their “signature,” a small portion of the original text. Our approach to this problem is to consider it as a translation problem, and we attempt to solve it by using state-of-the-art methods in the field of machine translation. Three models were developed as a result of the research conducted in this thesis. Two of these were based on the encoder-decoder framework for sequence-tosequence prediction. The best performing model had an accuracy of over 98% when recognizing text written in a single font and close to 90% when recognizing text written in five different fonts under 10% noise.
Studying the Effect of Bit-Plane in Super-Resolution Reconstruction of Text Image

Single-frame super-resolution image reconstruction is one of the issues highly considered and studied in recent research works. In the techniques of super-resolution, besides increasing the size of image, increasing the observable details is considered, too. In this research, a method of single-frame super resolution is presented. It has low calculation complexity and also good accuracy. In this method, by making a complete database and training a Multi Layer Perception (MLP) neural network, we have tried to reconstruct high- resolution text images from low-resolution ones. In this research, it has been shown that the basic information of text images is saved in two most significant bit-planes, so the reconstruction of the high frequency components of this two bit-plane, has been focused in this research. Then by using low-resolution image upscaled by nearest neighbor interpolation, separating six least significant bit-planes of it and finally with the combination of this six bit-planes and those two reconstructed bit-planes, the images with high-resolution are obtained. At the end, the results of our method are compared with the common techniques. To evaluate the proposed method and compare it with some other methods, Structural SIMilarity (SSIM) is used. This quality metric measures structural similarity of two images, that in comparison with PSNR and MSE measures, presents a better concept of similarity between two images.
Showing the equivalence of two training algorithms. I

Graph transformations offer a powerful way to formally specify neural networks and their corresponding training algorithms. This formalism can be used to prove properties of these algorithms. In this paper graph transformations are used to show the equivalence of two training algorithms for recurrent neural networks; backpropagation through time, and a variant of real-time backpropagation. In addition to this proof a whole class of related training algorithms emerges from the used formalism.
Teaching and Learning K-8 Mathematics and Science through Inquiry: Program Reviews and Recommendations.

This report offers guidance for those shaping policy and designing elementary and middle school science and mathematics courses that prepare students to be lifelong users of scientific and mathematical ideas. We have reviewed programs designed to improve elementary and middle school students' understanding of science and mathematics by incorporating instructional technology effectively into the curriculum. Using criteria backed by research on learning and instruction, we have selected exemplary programs from among those reviewed. We emphasize the teaching of science and mathematics as a process of inquiry in which students learn to solve complex problems and critique scientific and mathematical arguments. We look for uses of computer and communications technology that help students become lifelong science and mathematics learners who are prepared to meet all sorts of challenges from computing their income taxes to interpreting data on global warming to assessing the risk of a medical procedure. We discuss: (1) Contemporary views of teaching and learning mathematics; (2) Promising uses of technology in learning and instruction; (3) Evolving uses of technology in science and mathematics; (4) Criteria for selecting exemplary instructional programs; (5) Reviews of programs that meet our mandatory requirements, organized by category; (6) Recommendations of exemplary programs; and (7) next steps. (Author) Reproductions supplied by EDRS are the best that can be made from the original document. 00 00 enGauge Resources North Central Re tonal Educational Laborato Page 1 of 25 lesources Success Stories aoctiepPause, 14cjitrolosyip,Itir, Ganten4A2eas, Classroom,; Resources, PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) U U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 4(. 1tridocument has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. Introduction
voltage quality improvement using dynamic votlage restorer(DVR)

Power quality is one of major problems in the presence era. It has become importance especially, with the introduction of sophisticated devices, whose performance is very sensitive to quality of power supply. power quality problem is an occurrence manifested as a nonstandard voltage,current,or frequency that results in a failure of end use euipement.One of the major problem deal here is the voltage sag. To solve this problem, custom power devices are used. One of those devices is the Dynamic Voltage Restorer (DVR),which is the most effective modern custom power device used in power distribution networks.Its appeal includes lower cost, smaller size, and its fast dynamic response to the disturbances. Keywords— DVR, Power Quality, Voltage Sag, Voltage Swell, Fuzzy Logic Controller, PI controller MATLAB/SIMULINK.
The Clinical Approach to Thalassaemia

Will reading habit influence your life? Many say yes. Reading the clinical approach to thalassaemia is a good habit; you can develop this habit to be such interesting way. Yeah, reading habit will not only make you have any favourite activity. It will be one of guidance of your life. When reading has become a habit, you will not make it as disturbing activities or as boring activity. You can gain many benefits and importances of reading.
Measurement of Visual Fatigue from Reading on Electronic Paper

Readers of e-Books read words on electronic paper. We conducted an experiment in which 13 college students read electronic paper and conventional paper for 90 minutes better understand the degree of visual fatigue from such reading. The degree of their visual fatigue was measured before, during, and after reading. Results showed no significant differences in the levels of visual fatigue between the electronic and conventional reading. We conclude that electronic paper is a good reading display that is as gentle to the eyes as conventional paper.
Large classes: Better results with blended learning

This study shows that adding blended learning elements in a large classroom course has a positive impact on students’ perception of the course, which leads to better course evaluations as well as better learning results. A study of 3 year bachelor students in a strategy course at a Norwegian business school in 2015 and 2016 showed that introducing new elements of blended learning had positive effects on perceived quality and student satisfaction. The additions were more videos and more direct communication online. The number of classroom lectures was reduced. Based on an electronic survey of the students (N=125 in 2015 and N=124 in 2016) we compared the students’ ratings on 10 specific elements of the course evaluation, and there was a significant improvement on seven of them and a significant reduction on one. In total, the course evaluations showed a significant improvement from 2015 to 2016 in students’ perception of learning quality. A separate measurement of a class comprehension test on strategy concepts showed a significant improvement in learning from 2015 to 2016.
Detecting Changes in Dynamic Functions with Tables and Graphs

The relative efficiency of graphic and tabular displays for detecting changes in the amplitude of periodic sine functions, simulating a dynamic process, was assessed. Line graphs had an advantage over tables for the detection of changes and for the correct identification of the changed function. However, the advantage depended on the type of change that could occur. A large difference between the displays was evident when both increases and decreases in amplitude were possible, and differences were much smaller when amplitudes could only increase. These results indicate that participants adapted their detection methods to the types of possible changes. The findings demonstrate the utility of graphic displays for process control and substantiate the claim that graphic displays have an advantage when the displayed information has inherent structure and when the task requires the use of this structure. In addition, task performance was shown to be the subject of adaptive changes, which depend on the context in which the task is performed.
Web Site Demise and Graduate Research

Abstract Do Web sites have enduring value for graduate student research? The present study attempts to find Internet sites cited by graduate social work students some time after student access of the site. Web site lifespan was tracked by domain and by time lapsed between first and second access of the site. Web site migration, disappearance, inaccessibility and citation problems are reported.
Reduplicated Words Are Easier to Learn

ABSTRACT Infants’ disposition to learn repetitions in the input structure has been demonstrated in pattern generalization (e.g., learning the pattern ABB from the token ledidi). This study tested whether a repetition advantage can also be found in lexical learning (i.e., learning the word lele vs. ledi). Twenty-four English-learning infants (mean age: 18.5 months) were exposed to novel word-object mappings involving either a reduplicated CVCV word (e.g., neenee) or a nonreduplicated CVCV word (e.g, bolay). Infants were more adept at learning word-object mappings with a reduplicated word than with a nonreduplicated word. A follow-up corpus analysis of infant-directed speech showed that this preference could not be attributed to the frequency patterns of reduplicated words or syllables in the linguistic input. These findings indicate that an experience-independent bias toward repeated elements plays an important role not only in pattern generalization but also in word learning.
Multivariate Statistical Analysis of Functional Neuroimaging Data

Multivariate Statistical Analysis of Functional Neuroimaging Data by Takeshi Yokoo Adviser: Lawrence Sirovich This dissertation is intended to provide a comprehensive theoretical review and some new developments in the multivariate statistical analysis of functional neuroimaging data. As a stereotypical example of functional imaging techniques, we consider intrinsic optical imaging of the central visual system of mammals, where the spatial pattern of the cortical reflectance is imaged in response to various visual stimulations. We appeal to a general analysis strategy called “pseudo-univariate reduction”, with which the multivariate data distribution is marginalized to a univariate distribution through projection onto some vector. For each choice of projection vector, the resulting one-dimensional distribution can be characterized by some statistic, such as the mean, variance, etc. For a given statistic, one can attempt to optimize its value as a function of the projection vector, and compute an unique set of projection vectors whose univariate distributions have “best” statistical characteristics. We give three particular examples of such a procedure, namely, principal component analysis, canonical variate analysis, and generalized indicator function analysis. First two methods are considered standard in multivariate statistics, and they have been successfully applied to the analysis of functional imaging data. The third method is our original contribution, which we present in the framework of the classical theory of multivariate analysis. We discuss some theoretical and practical advantages of our new method over the existing ones, and demonstrate its efficacy in the analysis of actual functional imaging data of mammalian primary visual cortex.
EST for EMI: A Problem-Based Learning Approach to Domain-Specific Fluency

An English for Science and Technology (EST) course is offered as a potential bridge to Eng-lish as a Medium of Instruction (EMI) in the sciences. Consisting of four modules, each or-ganized around a “big problem” in science or technology, the course challenges students to collectively arrive at solutions through critical and creative thinking that ultimately nds ex -pression in three modalities: verbal (e.g., expert panel discussions, debates) graphic (e.g., prob-lem statements, action plans), and visual-spatial (e.g., graphs, models). It is suggested that Problem-Based Learning (PBL) approaches to language learning – especially ones propelled by critical thinking frameworks (e.g., SPRE) – not only ease the transition to science courses where English is the medium of instruction but promote the acquisition of general competen-cies thought vital to 21st century success.
Computational Intelligence Applied to Financial Price Prediction: A State of the Art Review

The following work aims to review the most important research from computational intelligence applied to the financial price prediction problem. The article is organized as follows: The first section summarizes the role of predictability in the Neoclassical financial world. This section also criticizes the zero predictability framework. The second section presents the main computational intelligence techniques applied to financial price prediction. The third section depicts common features of revised works.