Miracle-dz/UniFAR-FTUs huggingface dataset:
Instructional Planning: A Guide for Teachers

Preface. 1. Introduction. A Systematic Approach to Instructional Planning: An Introduction. Effective Instruction: The End Result of Systematic Planning. Key Principles Underlying Systematic Instructional Planning. Does This Process Limit the Way You Go about Teaching? Are You Really Likely to Use This Planning Process? 2. Identifying Instructional Goals. Characteristics of Instructional Goal Statements. How Instructional Goals Are Established. Uses of Instructional Goals. Sources to Help You Identify Instructional Goals. Student Characteristics and Instructional Goals. 3. Identifying Objectives. Components of Objectives. Sample oOjectives. Domains of Learning. Deriving Objectives from Goals. Limiting the Number of Objectives. Uses of Objectives. Short Forms of Objectives. Student Characteristics and Objectives. Prerequisite Skills. Identifying Students' Prerequisite Skills. 4. Planning Instructional Activities. Motivating Students. Informing Students of Objectives. Helping Students Recall Prerequisites. Presenting Information and Examples. Providing Practice and Feedback. Summarizing the Lesson. Instructional Activities and the Domains of Learning. Sequencing Instructional Activities. Other Types of Instructional Activities. Instructional Activities for Different Types of Students. Strategies for Planning Instructional Activities. Describing Your Activities in Writing: The Lesson Plan. Examples of Lesson Plans. 5. Choosing Instructional Media. Practicality of Media. Student Characteristics. Media and Instructional Activities. Choosing Media. Computers in Instruction. 6. Developing Assessment Tools. Who Develops Assessment Tools? Matching Assessment Items to Objectives. Assessment Tools for the Various Domains of Learning. Guidelines for Developing Assessment Tools. Pencil and Paper Assessment Tools. Alternative Assessment Tools. How Is an Assessment Tool Developed? 7. Implementing Instruction. Mastery Learning: One Approach to Implementation. Planning for Mastery Learning. Results of Using Mastery Learning. Alternative Teaching Approaches. 8. Revising Instruction. Data that Should be Gathered. Steps in Analyzing Data. Revising Your Instructional Plans. Revising Instruction: An Example. 9. Summary and Concluding Thoughts. Key Principles underlying the Systematic Planning Process. The Adaptability of the Process. Glossary. Bibliography.
APPLICABILITY OF METAHEURISTICS FOR ENERGY MINIMIZING VEHICLE ROUTING PROBLEM ( EMVRP )

In the field of combinatorial optimization, the Vehicle Routing Problem (VRP) is considered to be one of the most challenging and most widely researched problems. It has been formulated more than 40 years ago, the problem involves assigning the optimal set of routes for fleets of vehicles while minimizing the distance travelled serving a given set of Abstract: Energy Minimizing Vehicle Routing Problem (EMVRP) has become extremely important as well as complex in recent years due to the high logistic needs arising throughout the world. Strategic transport practices have become essential to serve the dynamic demanding customers located in various locations. Due to the Np-hard nature of the EMVRPs, these cannot be optimally solved within a feasible time due to large and complex nature of problems. The modern researchers have used heuristics to solve these problems in a reasonably feasible time. Metaheuristics such as Genetic Algorithm (GA) and Tabu Search (TS) are widely used in solving VRPs, where sequence of solutions are represented for each vehicle for each customer to ultimately minimize the total distance traveled by the fleet. The authors critically reviewed the literature of EMVRP and analyzed comparatively each of the metaheuristics utilized in the literature to solve variants of EMVRP. Furthermore, the scrutiny identifies the knowledge gaps in the current published literature and proposes future research directions in the EMVRP as well as methodological advancements in the broad metaheuristics domains.
Verification of the Chord protocol in TLA

In traditional software engineering methodologies, software correctness is established through testing and progressive fault mitigation. Safety properties are established by demonstrating that a sufficiently large number of test cases fail to violate them. In contrast, formal verification methods permit a systems design process where desired safety properties are stated outright in the system specification, and enforced by automated analysis tools. This is of particular interest in designing distributed systems, where safety properties may be easy to formally define and specify, yet hard to implement in practice. Despite this promise, the use of formal methods has largely been confined to academia and certain classes of safety-critical systems. Recently, however, companies like Amazon and Microsoft have adopted formal verification tools to verify distributed system designs. In this thesis, we present a formal specification of the Chord distributed hash table protocol, using the TLA specification language. We specify the protocol at a coarse level with a relaxed failure model, and then increase the granularity and introduce fail-stop failures, yielding a formal specification of Chord with asynchronous messaging and fault-tolerance mechanisms. We first model-check the specification under the constraint that no failures occur, and show that it satisfies critical safety properties. We then show that the introduction of failures leads the specification to admit several behaviors which break the safety properties Chord promises, potentially leading to permanent partitions in the network and performance degradation. As part of this work, we provide an overview of formal verification methods; we discuss certain formalisms and logics involved in modelling and proving algorithms, show potential advantages of applying formal methods to distributed systems design, and identify barriers keeping formal methods from widespread use.
Systematic Review of Existing IoT Architectures Security and Privacy Issues and Concerns

Internet of things (IoT) has become one of the most prominent technologies that the world has been witnessing nowadays. It provides great solutions to humanity in many significant fields of life. IoT refers to a collection of sensors or object in the universe with the capability of communicating with each other through the internet without human intervention. Currently, there is no standard IoT architecture. As it is in its infancy, IoT is surrounded by numerous security and privacy concerns. Thus, to avoid such concerns that may hinder its deployment, an IoT architecture has to be carefully designed to incorporate security and privacy solutions. In this paper, a systematic literature review was conducted to trace the evolvement of IoT architectures from its initial development in 2008 until 2018. The Comparison among these architectures is based on terms of the architectural stack, covered issues, the technology used and considerations of security and privacy aspects. The findings of the review show that the initial IoT architectures did not provide a comprehensive meaning for IoT that describe its nature, whereas the recent IoT architectures convey a comprehensive meaning of IoT, starting from data collection, followed by data transmission and processing, and ending with data dissemination. Moreover, the findings reveal that IoT architecture has evolved gradually across the years, through improving architecture stack with new solutions to mitigate IoT challenges such as scalability, interoperability, extensibility, management, etc. with lack consideration of security solutions. The findings disclose that none of the discussed IoT architectures considers privacy concerns, which indeed considered as a critical factor of IoT sustainability and success. Therefore, there is an inevitable need to consider security and privacy solutions when designing IoT architecture.
INADEQUACY OF SELECTIVE PLATING MEDIA IN FIELD DETECTION OF

The laboratory isolation of Salmonella began approxhately a century ago. Since that time there have been numerous reports in the scientific literature comparing the efficacy of various media and incubation conditions. An important part of the Salmonella laboratory procedure is the selective plating agar. Many of the currently used media were developed for the clinical microbiologist, and clinical samples have been used for the initial evaluations of these media [l]. Although these plating media were not initially developed for isolating Salmonella from environmental sources (and more specifically, poultry sources), several published reports have evaluated selective plating media efficacy with poultry samples [2, 3, 4, 51. However, the conflicting results of these past studies render them of limited value to theSalmonella researcher. It is rare to find any two papers that recommend the same plating medium on the basis of efficacy. One report may indicate that a certain plating medium is best, and another will report that the same plating medium was the poorest performer of those tested. Therefore, the objective of this paper is not to compare media, but rather to demonstrate the inability of many commonly
Novel rogue optical network unit detection algorithm for gigabit passive optical networks

Passive optical networks are widely used as a promising solution for future access networks. Currently, the bandwidth is still increasing which means the current copper networks are not able to transfer new services such as 4K video, live streaming, etc. In other words, they reached their capacity limit. The passive optical networks rely on point-to-multipoint technology. That means each customer uses a share medium by time slots. Each time slot exactly specifies who and when is able to transfer data. In general, this control mechanism is implemented in the optical network unit by worst transmission convergence layer. On the other hand, there are cases when the optical network unit (it is called rogue optical network unit) does not follow instructions provided by the optical line termination, for example, if an attacker modifies a firmware of the end unit and/or when the control protocol is not loaded properly inside optical network unit. In worst case, the optical network unit transmits data in a continual mode (other optical network units cannot send data). The standard defines finding of the rogue optical network unit but it does not specify how the rogue optical network unit should be allocated because the frames of the rogue optical network unit do not contain the proper parameters. We realized a measurement in a real network with the rogue optical network unit and then we analyzed the captured data. A new algorithm for the rogue optical network unit allocation is presented. We do not consider any modification of the transmission convergence layer in gigabit passive optical networks.
Dysregulated PKA activity leads to defective neural crest differentiation and Schwann cell tumorigenesis.

ii DEDICATION . v ACKNOWLEDGEMENTS . vi VITA . ix LIST OF TABLES . xiii LIST OF FIGURES . xiv LIST OF ABBREVIATIONS . xvii CHAPTER 1: INTRODUCTION . 1 1.1 THE cAMP-DEPENDENT PROTEIN KINASE (PKA) . 1 The PKA Signaling Pathway . 1 The PKA Holoenzyme . 3 PKA Knock-Out Mouse Models . 7 1.2 THE CARNEY COMPLEX . 13 General Pathology . 13 Genetics of Carney Complex . 16 Mouse Models of Carney Complex . 17 1.3 THE NEURAL CREST . 21 Cranial Neural Crest . 21 Neurocristopathies . 24 PKA in Palate Development . 26 1.4 THE NEUROFIBROMATOSIS (NF) SYNDROMES . 28 General Pathology . 28 Genetics of NF . 35 Mouse Models of NF . 38 PKA Interactions with the NF Proteins . 43
CLINICAL PATTERN AND CAUSATIVE AGENTS IN CUTANEOUS ADVERSE DRUG REACTIONS: AN EVIDENCE BASED APPROACH

Aim: Cutaneous adverse drug reactions reactions. The present study was conducted to study the clinical pattern, causative agents and magnitude of CADRs. Materials and Methods: pharmacology and dermatology in SMHS hospital. The study was cond 2016 on admitted patients of CADRs. After obtaining an informed consent, these reactions were reported on a structured questionnaire based on ADR monitoring form provided by the Central Drug Standard control Organization (CDSCO India. The CADRs were analyzed for their pattern, causative agents, severity and prognosis. Causality assessment was done by using a validated ADR probability scale of Naranjo as well as WHO Monitoring Centre (WHO protocols were analyzed for their clinical outcome through a proper follow up period. Results: Age group ranged from 2 noted, most common being maculopapular TEN(14%), FDE (14%), anticonvulsant hypersensitivit TEN was seen in all females (100%) and in no male. Drugs implicated in causing these cutaneous reactions were identified as Phenytoin (24.6%), Fluoroquinolones (20%), Carbamazepine piroxicam i.m(15. cefixime, cefpodoxime, amoxcicillin, ayurvedic medicine and capecitabine all (1.5%). Despite higher reported mortality rates in SJS and TEN all patients survived with 2 patients from long term ophthalmological sequelae of the disease. Conclusion: therapies ranging from simple nuisance rashes to rare life threatening diseases like North Indian ethnic population has great predisposition of CADRs due to aromatic antiepileptic drugs, fluroquinolones, oxicam NSAIDs, lamotrigine and other antibiotics. To ensure safe use of pharmaceutical agents and newer molecules/ biologic rare and unusual reactions remains inevitable.
A Saucy Alternative to Spaghetti Plots

Longitudinal data are key to causal inference in epidemiologic studies. As the number of subjects, frequency of measurements, and period of active data collection grow, so does the size of the dataset. As the dataset grows, so do the burden and complexity of graphically exploring and summarizing the data without obscuring salient features. The gold standard for graphically displaying longitudinal data is the classic spaghetti plot, which plots a subject’s values for the repeated outcome measure (vertical axis) versus time (horizontal axis) and connects the dots chronologically, using lines of uniform color and intensity for each subject. However, the classic spaghetti plot presents obstacles to the display of longitudinal data. Although useful for fewer subjects, trends and patterns are obscured with the larger numbers of subjects typical of modern epidemiologic studies. For example, trajectories commonly overlap in a classic spaghetti plot, as both subjects and the magnitude of the outcome measure are displayed on the vertical axis. With large datasets, the figure often succumbs to “over-plotting,” in which the multiple intersecting lines have no discernible patterns. One solution has been to plot a subset of the data based on medians or deciles, but this approach fails to use the whole dataset. Furthermore, repeated-measures data containing different enrollment times, missing data, or loss to follow-up (censoring), typically are difficult to display in the classic spaghetti plot. Exploratory plots generally are used to reveal various structures in the data: trends (how most people respond over time), outliers (whether some subjects differ from most), clusters (groups of patients responding the same, maybe due to a covariate such as treatment assignment), as well as data-quality checks of reasonable values and sensible collection patterns of multicenter repeated-measures data. Classic spaghetti plots suffering from over-plotting reduce the chances of seeing such patterns. Spaghetti plots can be improved. Color saturation, a method of handling overplotting, has been implemented in parallel coordinate plots, of which the spaghetti plot is a special case. In many situations, a classic spaghetti plot or the more modern parallel co-ordinates plot can visualize data sufficiently. However, 2 common data features limit the usefulness of parallel coordinate/spaghetti plots: categorical outcomes and missing outcome values (eFigure 16 in eAppendix, http://links.lww.com/EDE/A401). We propose a procedure for visualization and exploration that is particularly useful for categorical outcomes and missingness.
Identifying time dependence in network growth

Identifying power-law scaling in real networks - indicative of preferential attachment - has proved controversial. Critics argue that measuring the temporal evolution of a network directly is better than measuring the degree distribution when looking for preferential attachment. However, many of the established methods do not account for any potential time-dependence in the attachment kernels of growing networks, or methods assume that node degree is the key observable determining network evolution. In this paper, we argue that these assumptions may lead to misleading conclusions about the evolution of growing networks. We illustrate this by introducing a simple adaptation of the Barab{\'a}si-Albert model, the "k2 model", where new nodes attach to nodes in the existing network in proportion to the number of nodes one or two steps from the target node. The k2 model results in time dependent degree distributions and attachment kernels, despite initially appearing to grow as linear preferential attachment, and without the need to include explicit time dependence in key network parameters (such as the average out-degree). We show that similar effects are seen in several real world networks where constant network growth rules do not describe their evolution. This implies that measurements of specific degree distributions in real networks are also likely to change over time.
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation

Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data. In many applications of machine learning such as learning-to-rank, decision forests deliver remarkable performance. They also possess other coveted characteristics such as interpretability. Despite their widespread use and rich history, decision forests to date fail to consume raw structured data such as text, or learn effective representations for them, a factor behind the success of deep neural networks in recent years. While there exist methods that construct smoothed decision forests to achieve representation learning, the resulting models are decision forests in name only: They are no longer axis-aligned, use stochastic decisions, or are not interpretable. Furthermore, none of the existing methods are appropriate for problems that require a Transfer Learning treatment. In this work, we present a novel but intuitive proposal to achieve representation learning for decision forests without imposing new restrictions or necessitating structural changes. Our model is simply a decision forest, possibly trained using any forest learning algorithm, atop a deep neural network. By approximating the gradients of the decision forest through input perturbation, a purely analytical procedure, the decision forest directs the neural network to learn or fine-tune representations. Our framework has the advantage that it is applicable to any arbitrary decision forest and that it allows the use of arbitrary deep neural networks for representation learning. We demonstrate the feasibility and effectiveness of our proposal through experiments on synthetic and benchmark classification datasets.
Soil Population of Helicotylenchus multicinctus under Pueraria phaseoloides, Flemingia macrophylla and Natural Bush Fallows and their Effect on Plantain Yield in the Humid Forest Zone of Southern Cameroon

ABSTRACT Soil population densities of Helicotylenchus multicinctus (Cobb) Golden were assessed under Flemingia macrophylla [(Willd.) Merrill] and Pueraria phaseoloides (Roxb.) Benth, and natural bush fallows over 24 months in three villages in southern Cameroon. Also assessed was the effect of H. multicinctus on yield of plantain. Trials were set up in four to five year-old Chromolaena odorata (L.) R. M. King & H. Rob. dominated natural bush fallow. Initial H. multicinctus population density at Ngoumou (512 100 cm−3 soil) was (p < 0.05) higher than at Mfou (241 100 cm−3 soil) and Nkometou (70 100 cm−3 soil). H. multicinctus population was correlated with (i) total annual rainfall (r = 0.79; p = 0.01) and (ii) clay content (r = 0.95; p < 0.001). At 12 months after planting (MAP), P. phaseoloides and F. macrophylla supported (p < 0.01) lower population densities of H. multicinctus (88 and 152 100 cm−3 soil, respectively) compared with the natural regrowth (287 100 cm−3 soil). Comparing the initial population with that at 12 MAP for each of the three fallow systems, both P. phaseoloides and F. macrophylla reduced density of H. multicinctus (p < 0.05). However, when the initial population density was compared with that at 24 MAP only under P. phaseoloides was the reduction maintained. Contrarily, population density under F. macrophylla was comparable between the initial and 24 MAP. This is the first report of the nematode reducing effect of P. phaseoloides. Plantain flowering and subsequent harvesting was earliest under P. phaseoloides, different from that under F. macrophylla (p < 0.05). Plant bunch weight was positively correlated with the length of the fruit filling period (r = 0.67) which in turn was positively correlated with the non damaged root index (NDRI) (r = 0.58). Plantain under P. phaseoloides had the highest NDRI and consequently recorded the greatest yield (p < 0.05). There was no difference in plantain yield between the natural regrowth and F. macrophylla. Pueraria phaseoloides could be used as one component for the biological control of H. multicinctus.
Small RNA Regulation of the Innate Immune Response: A Role for Dicer in the Control of Viral Production and Sensing of Nucleic Acids: A Dissertation

All organisms exist in some sort of symbiosis with their environment. The food we eat, air we breathe, and things we touch all have their own microbiota and we interact with these microbiota on a daily basis. As such, we employ a method of compartmentalization in order to keep foreign entities outside of the protected internal environments of the body. However, as other organisms seek to replicate themselves, they may invade our sterile compartments in order to do so. To protect ourselves from unfettered replication of pathogens or from cellular damage, we have developed a series of receptors and signaling pathways that detect foreign bodies as well as abnormal signals from our own perturbed cells. The downstream effector molecules that these signaling pathways initiate can be toxic and damaging to both pathogen and host, so special care is given to the regulation of these systems. One method of regulation is the production of endogenous small ribonucleic acids that can regulate the expression of various receptors and adaptors in the immune signaling pathways. In this dissertation, I present work that establishes an important protein in small ribonucleic acid regulation, Dicer, as an essential protein for regulating the innate immune response to immuno-stimulatory nucleic acids as well as regulating the productive infection of encephalomyocarditis virus. Depleting Dicer from murine embryonic fibroblasts renders a disparate type I interferon response where nucleic acid stimulation in the Dicer null cells fails to produce an appreciable interferon response while infection with the paramyxovirus, Sendai, induces a