Miracle-dz/UniFAR-FTUs huggingface dataset:
Workflow Based Information Flow Control(IFC) in Modern Operating Systems.

NADKARNI, ADWAIT PRAVIN. Workflow Based Information Flow Control(IFC) in Modern Operating Systems. (Under the direction of Dr. William Enck.) The security architecture of consumer operating systems is currently undergoing a fundamental change. In platforms such as Android, iOS, and Windows 8, each application is a separate security principal that can own data. While this distinction is a vast improvement over traditional user-focused security architectures, sharing data between applications results in an unexpected loss of control of that data, potentially exposing security and privacy sensitive information. This paper presents the Aquifer framework to address this data intermediary problem in modern operating systems. Aquifer builds from the notion that when apps share data, it is as part of a mashup to perform some task for the user. As such, Aquifer enforces host export restriction policies on data associated with the user interface workflow. These restrictions are specified by the applications on the UI workflow using available runtime context, as opposed to relying on users for manual specification. In doing so, Aquifer allows applications to retain control of data even after it is shared, thereby addressing a growing concern in modern operating systems. © Copyright 2012 by Adwait Pravin Nadkarni
MAGNETIC PAIR CREATION TRANSPARENCY IN GAMMA-RAY PULSARS

Magnetic pair creation, γ → e+e−, has been at the core of radio pulsar paradigms and central to polar cap models of gamma-ray pulsars for over three decades. The Fermi gamma-ray pulsar population now exceeds 140 sources and has defined an important part of Fermi's science legacy, providing rich information for the interpretation of young energetic pulsars and old millisecond pulsars. Among the population characteristics well established is the common occurrence of exponential turnovers in their spectra in the 1–10 GeV range. These turnovers are too gradual to arise from magnetic pair creation in the strong magnetic fields of pulsar inner magnetospheres. By demanding insignificant photon attenuation precipitated by such single-photon pair creation, the energies of these turnovers for Fermi pulsars can be used to compute lower bounds for the typical altitude of GeV band emission. This paper explores such pair transparency constraints below the turnover energy and updates earlier altitude bound determinations that have been deployed in various Fermi pulsar papers. For low altitude emission locales, general relativistic influences are found to be important, increasing cumulative opacity, shortening the photon attenuation lengths, and also reducing the maximum energy that permits escape of photons from a neutron star magnetosphere. Rotational aberration influences are also explored, and are found to be small at low altitudes, except near the magnetic pole. The analysis presented in this paper clearly demonstrates that including near-threshold physics in the pair creation rate is essential to deriving accurate attenuation lengths and escape energies. The altitude bounds are typically in the range of 2–7 stellar radii for the young Fermi pulsar population, and provide key information on the emission altitude in radio quiet pulsars that do not possess double-peaked pulse profiles. The bound for the Crab pulsar is at a much higher altitude, with the putative detection by MAGIC out to 350–400 GeV implying a lower bound of 310 km to the emission region, i.e., approximately 20% of the light cylinder radius. These results are also extended to the super-critical field domain, where it is found that emission in magnetars originating below around 10 stellar radii will not appear in the Fermi-LAT band.
The construct of cognitive load in interpreting and its measurement

ABSTRACT This article attempts to contribute to the study of cognitive load in interpreting by presenting an in-depth discussion of the construct and its measurement. Borrowing from mental workload and Cognitive Load Theory research, cognitive load in interpreting is defined as the portion of an interpreter’s limited cognitive capacity devoted to performing an interpreting task in a certain environment. The article then presents a methodological discussion on how to measure cognitive load, focusing on the major categories of cognitive load measures and a series of selection criteria. Considering that existing studies only focus on simultaneous interpreting, the article introduces some techniques that are potentially useful for measuring cognitive load in consecutive interpreting, including the NASA Task Load Index (NASA-TLX), pen recording, and eye tracking.
“So That Everybody Starts at the Same Line”: Exploring International English Language Learners’ Perceptions of Learning English and Studying in Canadian Universities

The myriad voices of international English language learners, who must enrol in an English language program before beginning their academic programs, are frequently left out of conversations about Canada's current efforts to internationalize higher education. The purpose of this study is to explore international English language learners' perceptions of learning English and studying in Canada as they transition from English language programs to post-secondary studies. Using an embedded qualitative case study design, the researcher interviewed 15 international English language learners studying engineering, business, and science in Ontario to explore how well they feel their particular English language program prepared them to study their academic disciplines. Additionally, this study extends scholarship on language learners' investments in English by exploring the personal values that international English language learners place on learning English as an additional language. Through a neocolonialist lens, the researcher examines how learning English impacts international students' identities and how they position themselves in relation to native-English speakers and non-English speakers. Findings from this study have the potential to help university and language program administrators better understand the diverse needs of international students undertaking post-secondary studies in Canada and implement both program and institutional reform. EXPLORING INTERNATIONAL ELLS' PERCEPTIONS v
Methods to improve creativity and innovation: the effectiveness of creative problem solving

This chapter focuses on the development of organizational creativity, using the CPS methodology, aiming at demonstrating its effectiveness in using the individual and team divergent thinking improvement in identifying organizational problems. A study was undertaken using problem solving teams in seven companies, in which each individual was submitted to a pre-post test in attitudes towards divergent thinking and asked to express the evaluation of the method. All the information reported in the sessions was recorded. The results indicate a change in attitude favourable to divergent thinking, the provision of a professional, efficient method of organizing knowledge in such a way that can help individuals to find original solutions to problems, and an important way to lead teams to creativity and innovation, according to companies’ different orientations. DOI: 10.4018/978-1-60960-519-3.ch007
Analysing heat exposure in two German cities by using meteorological data from both within and outside the urban area

As many cities are increasingly affected by heat waves, knowledge regarding those parts of cities most susceptible to heat exposure is essential for the implementation of directed adaptation measures. The frequency of heat waves is projected to increase in both German cities considered for this study: Karlsruhe and Berlin. By aggregating temperature data from meteorological stations within the two cities and their hinterlands, the local temperature distribution within the administrative city boundaries was assessed. A multiple regression approach was used to reveal the regional inter‐relationship between non‐meteorological factors such as altitude, population density and land use, on the one hand, and the heat distribution, on the other. This functional relationship was then applied at the city quarter level for the two cities. A model selection process was undertaken to find the most significant models describing the heat exposure of two heat indicators: heat wave days (HWDs) and tropical nights (TRNs). While altitude and population density were found to be the most significant explanatory variables for Karlsruhe, population density had a dominating influence on the distribution of heat at the city quarter level for Berlin. In Karlsruhe, models describing the daytime temperature performed best, whereas in Berlin those describing the night time temperature distribution had the highest statistical significance. This method could be used with relatively low financial and material expense to assess heat exposure in different city quarters even if there are insufficient meteorological stations within a city.
Achieving High Reliability and Efficiency in Maintaining Large-Scale Storage Systems through Optimal Resource Provisioning and Data Placement

With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues. First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience in maintaining large-scale storage systems. Second, in order to costeffectively integrate solid-state drives (SSD) into large-scale storage systems, I design a holistic algorithm which can adaptively predict the popularity of data objects by leveraging the temporal locality in their access patterns and adjust their placement among solid-state drives and regular hard disk drives so that the data access throughput as well as the storage space efficiency of the large-scale heterogeneous storage systems can be improved. Finally, I propose a new checkpoint placement optimization model which can maximize the computation efficiency of large-scale scientific applications while guarantee the endurance requirements of the SSD-based burst buffer in high performance hierarchical storage systems. All these models and algorithms are validated through extensive evaluation using data collected from deployed large-scale storage systems and the evaluation results demonstrate our
Machine Learning under the light of Phraseology expertise: use case of presidential speeches, De Gaulle - Hollande (1958-2016)

Author identification and text genesis have always been a hot topic for the statistical analysis of textual data community. Recent advances in machine learning have seen the emergence of machines competing state-of-the-art computational linguistic methods on specific natural language processing tasks (part-of-speech tagging, chunking and parsing, etc). In particular, Deep Linguistic Architectures are based on the knowledge of language speci-ficities such as grammar or semantic structure. These models are considered as the most competitive thanks to their assumed ability to capture syntax. However if those methods have proven their efficiency, their underlying mechanisms, both from a theoretical and an empirical analysis point of view, remains hard both to explicit and to maintain stable, which restricts their area of applications. Our work is enlightening mechanisms involved in deep architectures when applied to Natural Language Processing (NLP) tasks. The Query-By-Dropout-Committee (QBDC) algorithm is an active learning technique we have designed for deep architectures: it selects iteratively the most relevant samples to be added to the training set so that the model is improved the most when built from the new training set. However in this article, we do not go into details of the QBDC algorithm-as it has already been studied in the original QBDC article-but we rather confront the relevance of the sentences chosen by our active strategy to state of the art phraseology techniques. We have thus conducted experiments on the presidential discourses from presidents C. De Gaulle, N. Sarkozy and F. Hollande in order to exhibit the interest of our active deep learning method in terms of discourse author identification and to analyze the extracted linguistic patterns by our artificial approach compared to standard phraseology techniques.
Localist models are compatible with information measures, sparseness indices, and complementary-learning systems in the brain

ABSTRACT In this paper, I express continued support for localist modelling in psychology and critically evaluate previous studies that have sought to weaken the localist case in favour of models with thoroughgoing distributed representation. I question claims that information measures and sparseness indices derived from single-cell recording data are supportive of distributed representation and show that the patterns observed in those data can be reproduced from simulations of a model that is known to be localist. I also set out some logical objections to the complementary-learning hypothesis, particularly in as much as it is used to justify thoroughgoing distributed models of the cortex.
A Quantitative Linguistic Study on the Relationship between Word Length and Word Frequency

The present paper reports on a corpus-based quantitative linguistic study on the relationship between word length and word frequency in Chinese within the theoretical framework of Synergetic Linguistics.Results show a high dependency of word frequency on word length.The longer a word is,the less frequently it is used in discourse,which reflects an inverse relation between these two properties.The power model y = axb is proved to fit best the data and capture this regularity.The results further indicate that the parameter of this model,a,is powerful in distinguishing the texts of different styles.The results of the study not only complement the current theories on the relationship between word length and word frequency,providing new evidence for the relationship as a linguistic universal,but also offer a new paradigm for style identification and text classification.
The Influence of the Soil Water Content in the Soil-Tool DEM Model

The Discrete Element Method (DEM) for describing the action mechanism between soil and sweep tool can be used to perform a detailed analysis of draft force, soil cutting, clod-crushing and loosening by taking into account the tillage speed and the three soil phases. This study describes the simulation of the 3D DEM soil model and a cultivator sweep digitized with a 3D scanner, showing the soil—sweep interaction as a function of implement draft force and implement operating speed. The suitability of the model is validated by comparing the results of laboratory and simulated shear tests (static validation) with the results of soil bin tests (dynamic validation). The mechanical parameters of the sandy soil used for the soil bin tests were measured using the direct shear box test. Cohesion for the soil model used during simulations was set using the parallel bond contact model, where the determining factors were the Young modulus for particle contact (Ec) and bonding (Ēc), the Poisson’s ratio (nu), the normal.
Efficient Algorithms for Stochastic Decoding of LDPC Codes

Efficient Algorithms for Stochastic Decoding of LDPC Codes by Kuo-Lun Huang Doctor of Philosophy in Electrical and Computer Engineering Northeastern University, May 2016 Professor Masoud Salehi, Advisor The expanding demand for high-speed communications has resulted in development of high-throughput error-correcting techniques required by emerging communication standards. Low-Density Parity-Check (LDPC) codes are a class of linear block codes that achieve near-capacity performance and have been selected as part of many digital communication standards. Stochastic computation has been proposed as a hardware efficient approach for decoding LDPC codes. Using stochastic computation, all messages in the iterative decoding process are represented by Bernoulli sequences. Computations on these sequences are performed bit-by-bit using simple logic operations. Furthermore, serial messages used in stochastic decoders help alleviate routing congestion in hardware implementation of decoder. These factors make stochastic decoding a low complexity alternative to implement LDPC decoders. In this dissertation, we analyze the characteristics of stochastic decoding and propose reduced-latency designs for stochastic LDPC decoders to achieve improved performance on various channel models. We statistically analyze the behavior of stochastic LDPC decoding, including randomization in the stochastic streams and convergence of transition probabilities in iterative decoding process. We also present a space and time-efficient code bit determination method for stochastic LDPC decoders. In addition, we investigate and characterize the decoding errors of stochastic LDPC decoders and as an example, study the stochastic-decoding-specific trapping sets in the (1056,528) LDPC code used in the WiMAX standard. This study helps to develop methods to lower the error floor of stochastic decoding.
Fifth Brainstorming Week on Membrane Computing

Neural Nets: Tissue P Systems. Lecture Notes in Computer Science 2387, (2002), 290–299. 6. L. Pan, T. -O. Ishdorj. P systems with active membranes and separation rules. Journal of Universal Computer Science, 10, 5, (2004), 630–649. 7. L. Pan, M.J. Pérez-Jiménez. Computational complexity of tissue–like P systems. Journal of Complexity, 26, 3 (2010), 296–315. 8. Gh. Păun. Attacking NP-complete problems. In Unconventional Models of Computation, UMC’2K (I. Antoniou, C. Calude, M. J. Dinneen, eds.), Springer-Verlag, 2000, pp. 94-115. 9. Gh. Păun. Membrane Computing. An Introduction. Springer–Verlag, Berlin, (2002). 10. A. Păun, Gh. Păun. The power of communication: P systems with symport/antiport. New Generation Computing, 20, 3, (2002), 295–305. 11. Gh. Păun, M.J. Pérez-Jiménez, A. Riscos-Núñez. Tissue P System with cell division. In. J. of Computers, Communications and Control, 3, 3, (2008), 295–303. 12. M.J. Pérez-Jiménez, A. Romero-Jiménez, Sancho-Caparrini. Complexity classes in models of cellular computing with membranes. Natural Computing, 2, 3 (2003), 265– 285. 13. M.J. Pérez-Jiménez, A. Romero-Jiménez, F. Sancho-Caparrini. A polynomial complexity class in P systems using membrane division. Journal of Automata, Languages and Combinatorics, 11, 4, (2006), 423-434. 14. M.J. Pérez-Jiménez, P. Sośık. Improving the efficiency of tissue P systems with cell separation. Submitted, 2012. 15. H. Vollmer. Introduction to Circuit Complexity: A Uniform Approach. Springer– Verlag, New York, (1999). Cell Complexes and Membrane Computing for Thinning 2D and 3D Images Raúl Reina-Molina, Daniel Dı́az-Pernil, Miguel A. Gutiérrez-Naranjo Research Group on Computational Topology and Applied Mathematics Department of Applied Mathematics University of Sevilla raureimol@alum.us.es, sbdani@us.es Research Group on Natural Computing Department of Computer Science and Artificial Intelligence University of Sevilla
The Definition of Blended Learning in Higher Education

Through the beginning of the millennium, the education environments have witnessed the introduction of information technologies and new pedagogies. Especially, the extensive use of Internet technologies as well as the networked learning made it possible to design and utilize new generation learning environments that are realistic, authentic, and engaging. By means of educational developments, alternative content delivery techniques or technologies have been implemented into the teaching environments throughout the years. In an effort to capitalize on the advantages of instructional delivery modalities and minimize the disadvantages, scholars started to combine the most functional elements of the instruction in these learning environments and that is universally called as ‘Blended Learning’. Although the blended learning as an instruction model has an increasing interest in the field of higher education, it is still in its infancy. The definitions of blended learning in the literature needs to be clarified or collocated for the readers, who would like to deal with blended learning in any level of instruction. Therefore, this chapter reviews the recent literature on blended and online learning and juxtaposes the definitions of the blended learning as well as the types of blended learning instruction that took place in the higher education environments.
How to Add Transparency to Artificial Neural Networks

The main issue about"black box"inherent in artificial neural networks(ANN's)is discussed. Adding transparency is well recognized to be an effective solution to dealing with this problem. Significant benefits are obtained through using this approach,such as providing a certain degree of comprehensive power,decreasing model size,speeding learning process and improving generalization capability.A hierarchical classification is applied to the existing approaches for better understanding of their intrinsic features and limitations.The first level of classification is made by two strategies. building prior knowledge into neural networks;extracting rules embedded within networks. Most of important approaches are introduced and compared in detail with further classifications within each strategy.Finally,the personal perspectives to the studies of machine learning are presented.Other objective functions are suggested for the extension of studies,such as performance- to-cost ratio and transparency.The study of increasing transparency to ANN's is considered as the most fundamental and direct solution to the other existing issues.A new machine learning approach called Knowledge Increasing via Feedback is proposed.
Model validation of mixed-signal systems

Today it is difficult to validate a mixed-signal System-on-Chip, i.e., one which contains analog and digital components. The problem is that the analog and digital subsystems are usually strongly intertwined so they must be validated together as a system, but the validation approach for analog and digital blocks are completely different. We address this problem by creating high-level functional models of the analog components that are compatible with top-level digital system validation, and then providing a method of formal checking to ensure that these functional models match the operation of the transistor level implementations of these blocks. The formal checking of the functional analog models is enabled by observing that the result surface of an analog block is a smooth function of its analog inputs – that is what makes it an analog block. This smooth result surface means it is not difficult to "explore" the design space of an analog block. We use this insight to create an equivalence checker between two analog descriptions: a SPICE netlist and its Verilog model. This checker exploits the fact that most analog circuits have a linear intent. Lastly we use this same insight to simplify statistical analysis of large mixed-signal systems to ensure that the system is robust to process variations. We describe a way to characterize the statistical behavior of circuits and to map the results to the functional model so one can estimate the parametric yield of the system by running system-level Monte Carlo simulations with analog models instead of circuit netlists.