Miracle-dz/UniFAR-FTUs huggingface dataset:
MicroNF: An Efficient Framework for Enabling Modularized Service Chains in NFV

The modularization of service function chains (SFCs) in network function virtualization (NFV) could introduce significant performance overhead and resource efficiency degradation due to introducing frequent packet transfer and consuming much more hardware resources. In response, we exploit the <italic>reusability</italic>, <italic>lightweightness</italic>, and <italic>individual scalability</italic> features of <italic>elements</italic> in modularized SFCs (MSFCs) and propose <italic>MicroNF</italic>, an efficient framework for MSFC in NFV. <italic>MicroNF</italic> addresses the performance overhead and resource efficiency problems in three ways. First, <italic>MicroNF</italic> graph constructor reuses the processing results of elements from different NFs and reconstructs the MSFC after modularization to shorten the chain latency. Second, optimized placer pays attention to the problem of which elements to consolidate and provides a performance-aware placement algorithm to place MSFCs compactly and optimize the global packet transfer cost. Third, <italic>MicroNF</italic> individual scaler innovatively introduces a <italic>push-aside scaling up</italic> strategy to avoid degrading performance and taking up new CPU cores. To support MSFC reusing and consolidation, <italic>MicroNF</italic> also designs a high-performance infrastructure to efficiently forwarding packets with consistency ensured and to automatically scheduling elements with fairness ensured when the elements are consolidated on the CPU core. Our evaluation results show that <italic>MicroNF</italic> achieves significant performance improvement and efficient resource utilization on several metrics.
Cooperative learning versus the lecture method of instruction in an introductory statistics course

Traditionally, the teaching of introductory statistics course in many colleges around the world is through the lecture method where the emphasis is on giving students the rules and techniques to be memorized and drill set for practicing algorithms.Active learning in learning statistical activities, is rarely, if ever, being encouraged.The work done in an introductory statistics classroom can therefore become dry and technique-oriented and consequently fail to show the power and nature of statistics.This paper describes an experimental study where the relative effects of cooperative learning versus lecture method of instruction in an introductory statistics course for student teachers were examined. The experimental group was given learning materials or units using the cooperative learning method while the control group was given none of the units but received the same cognitive input through the lecture method. The study was carried out over a period of five weeks with an hour-long session per week with each group. Student teachers in both groups were given questionnaires before the study and an assessment test after the study. In both groups, neither their readiness to study statistics nor their attitudes to learning statistics differ significantly. However, in the assessment test, student teachers in the experimental group achieved higher test scores. The implications of these findings on the teaching and learning of the introductory statistics course are discussed briefly.
Stochastic games and their complexities

We study a class of games introduced by Mio to capture the probabilistic µ -calculi called branching games . They are a subclass of stochastic two-player zero-sum turn-based inﬁ-nite-time games of imperfect information. Branching games extend Gale-Stewart games by allowing players to split the execution of a play into new concurrent sub-games that continue their execution independently. In consequence, the play of a branching game has a tree-like structure, as opposed to linearly structured plays of Gale-Stewart games. In this thesis, we focus our attention on regular branching games . Those are the branching games whose pay-oﬀ functions are the indicator functions of regular sets of inﬁnite trees, i.e. the sets recognisable by ﬁnite tree automata . We study the problems of determinacy , game value computability , and the related problem of computing a measure of a regular set of inﬁnite trees. Determinacy is a property of a game that guarantees that none of the players gains or loses an advantage by revealing their strategy at the start of the game.
Bayesian Structured Representation Learning

Author(s): Vikram, Sharad Mandyam | Advisor(s): Dasgupta, Sanjoy | Abstract: Bayesian methods offer the flexibility to both model uncertainty and incorporate domain knowledge into the modeling process. Deep generative modeling and Bayesian deep learning methods, such as the variational autoencoder (VAE), have expanded the scope of Bayesian methods, enabling them to scale to large, high-dimensional datasets. Incorporating prior knowledge or domain expertise into deep generative modeling is still a challenge, often resulting in models where Bayesian inference is prohibitively slow or even intractable. In this thesis, I first motivate using structured priors, presenting a contribution in the space of interactive structure learning. I then define Bayesian structured representation learning (BSRL) models, which combine structured priors with the VAE, and present foundational work along with applications of BSRL models.
Communicating with Data: Telling the Extension Story in Credible and Actionable Ways

Effective communication requires a good message delivered through an effective channel and received by a receptive individual. When that communication is successful, the result is enhanced credibility and trust between the sender and the receiver. Telling the Extension story effectively requires both relevant, credible data to compose a clear message and appropriate communication channels to deliver the message to various audiences. This article describes the approach taken by Florida Extension to gather better statewide data to improve communication about the impact of its Extension work, primarily through the use of infographics. With credible data, and working together, Extension data analysts and communicators can enhance Extension’s reputation, trust, and support with key stakeholders.
Different Topic, Different Traffic: How Search and Navigation Interplay on Wikipedia

As one of the richest sources of encyclopedic information on the Web, Wikipedia offers large-scale article access data that allows us to compare articles with respect to the two main paradigms of information seeking, i.e., search by formulating a query, and navigation by following hyperlinks. Using such data from the English Wikipedia, we study access behavior by employing two main metrics, namely (i) searchshare -- the relative amount of views an article received by search --, and (ii) resistance -- the ability of an article to relay traffic to other Wikipedia articles -- to characterize articles. We demonstrate how articles in distinct topical categories differ substantially in terms of these properties. For example, architecture-related articles are often accessed through search and are simultaneously a ``dead end'' for traffic, whereas historical articles about military events are mainly navigated. We further link traffic differences to varying network, content, and editing activity features. Lastly, we measure the impact of the article properties by modeling access behavior on articles with a gradient boosting approach and explore explicit importance of individual features. Our results constitute a step towards understanding human information seeking behavior, and may contribute to identify focal points for future improvements of Wikipedia and similar systems.
Salicylic Acid Phytohormone Decreased the Toxicity Damage of Exogenous Lead Absorption in Seedlings of an Oilseed Plant, Brassica napus L. Grown in Hydroponic Conditions

Oilseed plant, Brassica napus L. seedlings grown in hydroponic condition with different concentrations of Pb were treated with salicylic acid (SA) to investigate the role of exogenous salicylic acid in alleviating lead toxicity on biochemical and physiological activities of the plant. The results showed that application of different concentrations of Pb increased soluble sugars and reduced carbohydrate levels significantly in roots and shoots of the plants. The stress induced by application of Pb triggered significant inhibitory effects on growth and chlorophyll synthesis induced on the production of protein and proline and enhanced the levels of antioxidant activity. Salicylic acid (SA) treated plants showed alleviation increasing total dry mass, leaf area, shoot and root length as well as leaf total chlorophyll content in responses to Pb stress. Results revealed the importance of salicylic acid (SA) activity in enabling plants to reduce the soluble sugars and increase of insoluble sugar in heavy -metal-stressed plants. The content of proline and proteins were also reduced in plants were treated with salicylic acid. Our data provide evidence that salicylic acid treatment decreased the activity of antioxidant enzymes in plants were exposed to different levels of Pb.
Investigating Polynomial Fitting Schemes for Image Compression

Image compression is a means to perform transmission or storage of visual data in the most economical way. Though many algorithms have been reported, research is still needed to cope with the continuous demand for more efficient transmission or storage. This research work explores and implements polynomial fitting techniques as means to perform block-based lossy image compression. In an attempt to investigate nonpolynomial models, a region-based scheme is implemented to fit the whole image using bell-shaped functions. The idea is simply to view an image as a 3D geographical map consisting of hills and valleys. However, the scheme suffers from high computational demands and inferiority to many available image compression schemes. Hence, only polynomial models get further considerations. A first order polynomial (plane) model is designed to work in a multiplicationand division-free (MDF) environment. The intensity values of each image block are fitted to a plane and the parameters are then quantized and coded. Blocking artefacts, a common drawback of block-based image compression techniques, are reduced using an MDF line-fitting scheme at blocks’ boundaries. It is shown that a compression ratio of 62:1 at 28.8dB is attainable for the standard image PEPPER, outperforming JPEG, both objectively and subjectively for this part of the rate-distortion characteristics. Inter-block prediction can substantially improve the compression performance of the plane model to reach a compression ratio of 112:1 at 27.9dB. This improvement,
Surveying the Level of Perceived Freedom of University Students in their Leisure Time

The purpose of this study is to investigate the level of perceived freedom of university students in their leisure time with respect to various parameters. The sample group of the study has been selected with purposive sampling method among Dumlupinar University PE and Sports College students with an average age of 22.49±2.76, of whom 192 are male and 98 are female, with a total of 290 students. In the study, “Freedom Perceived in Leisure Time Scale” which has been developed by Witt and Ellis (1985) and adapted to Turkish by Lapa and Tercan (2017), has been used. To determine the personal information of participants percentage and frequency methods, and to determine whether the data has normal distribution or not Shapiro Wilks normality test has been conducted and once it has been concluded, that the data was suitable for parametric test conditions, independent T Test and ANOVA Test has been used to analyse the data. Analysis shows, that with respect to gender, age, department, grade, and weekly leisure time sufficiency parameters there is no significant difference in perceived level of freedom, but with respect to welfare level and sports branch parameters there is a statistically meaningful difference. Consequently, it has been concluded, that in the perceived level of freedom of participants there are differences with respect to various parameters.
SORPTION PROPERTIES OF SELECTED DIETARY FIBERS

holding capacities. This was proven on the basis of the evaluation of the adsorption properties and the differences in the surface microstructure of the preparations under examination. Not only the components present in the complex fraction of fibres, but also their physical state are of the utmost importance. K e y w o r d s: fibre preparations, water-binding, water-holding capacities, adsorption properties
Miscue Analysis Applications To Reading Instruction

Thank you very much for downloading miscue analysis applications to reading instruction. Maybe you have knowledge that, people have look numerous times for their chosen novels like this miscue analysis applications to reading instruction, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their computer.
An Ontology Framework for Addressing Cost Overrun through Risk Modeling: A Risk Path Approach

ii List of Figures . vii List of Tables . viii List of Equations . ix Chapter 1: Introduction . 1 1.1. Background . 1 1.2. Problem Statement . 2 1.3. Objectives . 3 1.4. Scope of Work . 4 1.5. Research Methodology . 5 1.6. Research Organization . 6 Chapter 2: Literature Review . 8 2.
Graduate attributes in higher education: Examining academics' perception in the Middle East

ABSTRACT Higher education institutes are facing increasing pressure to ensure that graduates are equipped with the right set of attributes that will make them desirable to potential employers. To deal with this pressure, universities have for some time been embedding employability attributes into their curriculum through classroom-based teaching and external programs. Yet, little research exists into examining academics' perception of graduate attributes and the potential challenges they face in delivering these attributes. The authors examine graduate attributes that are perceived as most important by academics. The findings suggest that communication, teamwork, problem solving, creativity, critical thinking, time management, and research skills were all seen as important for the employability.
Chemical Reactivity Properties, Drug-Likeness Features and Bioactivity Scores of the Cholecystokinin Peptide Hormone

Five density functionals, CAM-B3LYP, LC-ωPBE, MN12SX, N12SX and ωB97XD, in connection with the Def2TZVP basis set were assessed together with the SMD solvation model for the calculation of the molecular and chemical reactivity properties of the Cholecystokinin peptide hormone (CCK-8) in the presence of water. All the chemical reactivity descriptors for the systems were calculated via Conceptual Density Functional Theory (CDFT). The potential bioavailability and druggability as well as the bioactivity scoresfor CCK-8 were predicted through different methodologies already reported in the literature which have been previously validated during the study of different peptidic systems. The conclusion was that the CCK-8 peptide will be moderately bioactive regarding all the interactions.
An Evaluation of Targeted Security Awareness for End Users

The content of Chapter 4 including the experimental work and the results have been published. Moreover, most parts of Chapter 5 and some content of Chapter 6 have also been published. It should be mentioned, there are some contents of the thesis which are not published yet and are intended to be published either within conferences of academic journals.
MALPITY: Automatic Identification and Exploitation of Tarpit Vulnerabilities in Malware

Law enforcement agencies regularly take down botnets as the ultimate defense against global malware operations. By arresting malware authors, and simultaneously infiltrating or shutting down a botnet's network infrastructures (such as C2 servers), defenders stop global threats and mitigate pending infections. In this paper, we propose malware tarpits, an orthogonal defense that does not require seizing botnet infrastructures, and at the same time can also be used to slow down malware spreading and infiltrate its monetization techniques. A tarpit is a network service that causes a client to stay busy with a network operation. Our work aims to automatically identify network operations used by malware that will block the malware either forever or for a significant amount of time. We describe how to non-intrusively exploit such tarpit vulnerabilities in malware to slow down or, ideally, even stop malware. Using dynamic malware analysis, we monitor how malware interacts with the POSIX and Winsock socket APIs. From this, we infer network operations that would have blocked when provided certain network inputs. We augment this vulnerability search with an automated generation of tarpits that exploit the identified vulnerabilities. We apply our prototype MALPITY on six popular malware families and discover 12 previously-unknown tarpit vulnerabilities, revealing that all families are susceptible to our defense. We demonstrate how to, e.g., halt Pushdo's DGA-based C2 communication, hinder SalityP2P peers from receiving commands or updates, and stop Bashlite's spreading engine.
Summarize to learn: summarization and visualization of text for ubiquitous learning

Visualizations can stand in many relations to texts – and, as research into learning with pictures has shown, they can become particularly valuable when they transform the contents of the text (rather than just duplicate its message or structure it). But what kinds of transformations can be particularly helpful in the learning process? In this paper, we argue that interacting with, and creating, summaries of texts is a key transformation technique, and we investigate how textual and graphical summarization approaches, as well as automatic and manual summarization, can complement one another to support effective learning.
Long non-coding RNAs and latent HIV – A search for novel targets for latency reversal

The latent cellular reservoir of HIV is recognized as the major barrier to cure from HIV infection. Long non-coding RNAs (lncRNAs) are more tissue and cell type-specific than protein coding genes, and may represent targets of choice for HIV latency reversal. Using two in vitro primary T-cell models, we identified lncRNAs dysregulated in latency. PVT1 and RP11-347C18.3 were up-regulated in common between the two models, and RP11-539L10.2 was down-regulated. The major component of the latent HIV reservoir, memory CD4+ T-cells, had higher expression of these lncRNAs, compared to naïve T-cells. Guilt-by-association analysis demonstrated that lncRNAs dysregulated in latency were associated with several cellular pathways implicated in HIV latency establishment and maintenance: proteasome, spliceosome, p53 signaling, and mammalian target of rapamycin (MTOR). PVT1, RP11-347C18.3, and RP11-539L10.2 were down-regulated by latency reversing agents, suberoylanilide hydroxamic acid and Romidepsin, suggesting that modulation of lncRNAs is a possible secondary mechanism of action of these compounds. These results will facilitate prioritization of lncRNAs for evaluation as targets for HIV latency reversal. Importantly, our study provides insights into regulatory function of lncRNA during latent HIV infection.
Procedural Policy Tools and the Temporal Dimensions of Policy Design

In recent years work on policy design and instrument choice has advanced towards a better understanding of the nature of policy mixes, their dimensions, and the trade-offs between choices of tools, as well as the identification of basic design criteria such as coherence, consistency and congruence among policy elements. However, most of this work has ignored the temporal dimension of mixes or has studied this only as an important contextual variable affecting instrument choices, for example, highlighting the manner in which tools and mixes often evolve in unexpected or unintended ways as they age. This ignores the important issue of the intentional sequencing of tools as part of a mix design, either in terms of controlling spillovers which emerge as implementation proceeds, ratcheting up (or down) specific tool effects like stringency of implementation and public consultation as time passes. This article reviews existing work on the unintentional sequencing of policy activity as well as the lessons which can be derived from the few works existing on the subject of intentional sequencing. In so doing, it helps define a research agenda on the subject with the expectation that this research can improve the resilience and robustness of policies over time.
Optimized Energy Aware Resource Allocation Algorithm Using Software Deﬁned Network Technology

The number of data centers (DCs) used for storing and processing data has evolved rapidly in recent years. However, the operations held by DCs may relate to a number of disadvantages, primarily presuming in excessive energy and power consumption due to the poor management standards applied. This may lead to a situation in which many devices within the DC operate at full capacity without any tasks assigned for actual execution. A Software Deﬁned Network (SDN) is a network architecture where the control plane is an independent entity from the data plane, yielding to a higher controllability and ﬂexibility over the network. Through the utilization of SDN architecture, a highly functional energy aware network may be established. In this paper, we propose a heuristic algorithm that monitors the current status of an SDN network (in addition to all ingoing and outgoing trafﬁc), in order to dynamically and eﬃciently allocate network resources by ensuring that only the necessary network devices are active and by turning the idle ones oﬀ. The results show that the proposed algorithm reduces energy consumption of the network compared to existing solutions.
Generative computing: African-American cosmetology as a link between computing education and community wealth

ABSTRACT Recent scholarship in computer science (CS) education shifts from a focus on the technical-cognitive skills of computational thinking to the socio-cultural goal of computational participation, often illustrated as remixing popular media (e.g. music, photos, etc.) in online communities. These activities do enhance the participatory dimensions of CS, but whether they also support broadening the participation of underrepresented youth remains unclear. While online communities that are dedicated to computational participation have existed in the U.S. for over a decade, many communities of color remain underrepresented in CS disciplines. How might CS educators, researchers, and technologists promote culturally responsive forms of computational participation? To answer this question, we propose a culturally responsive framework for computational participation called generative computing. Generative computing approaches CS as a means for strengthening relationships between learning environments and local communities, leveraging culturally relevant sources of wealth generation in technology design and implementation. To explore this concept, we conducted a mixed-methods study with a cosmetology high school program that predominantly serves young African-American women. Through a series of computationally and culturally rich cosmetology projects, we tested our hypothesis that generative computing can enhance connections between Black heritage, CS, and cosmetology while supporting students’ academic interests and knowledge.