Computer Science

Media Centers offer an opportunity to diversify optional delivery methods. Coupled with the idea of multiple intelligences – different ways of learning, I was able to put into practice an old strategy into play – offer within the same classroom different ways to learn the same lesson. The Media Center offers the opportunity to use different learning stations with three different smart boards, and a fourth station to generate and deliver lessons using technology as well as some students want – a low technology approach. Innovative ideas such as flipping – changing homework into class time activities and classroom activities into homework provide a great opportunity to enhance student success. Optional grading opportunities present themselves such as using the textbook and the test as learning tools and strategy as a by-product of this learning approach. A demonstration of this model and the student success results will be presented.

by Bermawi Priyatna Iskandar
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Part 1: Orientation to Small Group Systems Chapter 1: Small Groups as the Heart of Society Chapter 2: Groups as Structured Open Systems Part 2: Foundations of Small Group Communication Chapter 3: Communication Principles for Group Members Part 3: From Individuals to Group Chapter 4: Becoming a Group Chapter 5: Working with Diversity in a Small Group Part 4: Understanding and Improving Group Throughput Processes Chapter 6: Creative and Critical Thinking in the Small Group Chapter 7: Group Problem Solving Procedures Chapter 8: Managing Conflicts Productively Chapter 9: Applying Leadership Principles. Part 5: Small Group Public Presentations Chapter 10: Planning, Organizing and Presenting Small Group Oral Presentations Appendix: Techniques for Observing Small Groups

Artificial Intelligence (AI) can roughly be categorized into two streams, knowledge driven and data driven both of which have their own advantages. Incorporating knowledge into Deep Neural Networks (DNN), that are purely data driven, can potentially improve the overall performance of the system. This paper presents such a fusion scheme, DeepEX, that combines these seemingly parallel streams of AI, for multi-step time-series forecasting problems. DeepEX achieves this in a way that merges best of both worlds along with a reduction in the amount of data required to train these models. This direction has been explored in the past for single step forecasting by opting for a residual learning scheme. We analyze the shortcomings of this simple residual learning scheme and enable DeepEX to not only avoid these shortcomings but also scale to multi-step prediction problems. DeepEX is tested on two commonly used time series forecasting datasets, CIF2016 and NN5, where it achieves competitive r.

K-systems analysis is a generalization of reconstructability analysis (RA), where any general, complete multivariate system (g-system) can be transformed into an isomorphic, dimensionless system (a K-system) that has sufficient properties to be analyzed using probabilistic RA algorithms. In particular, a g-system consists o f a set of states formed from a complete combination of the variables assigned specific values from a finite set of possible values and an associated system function value. The gsystem must be complete in that all possible states must have an associated system function value. K-systems analysis has been applied to a variety of systems, but many real-world systems consist o f data that is incomplete. Impediments in real-world systems have been previously identified as state contradictions, data scattering and missing data [JONE 85d]. The problem o f state contradictions has been adequately addressed, but while techniques for the resolution of data scattering and m.

Combining easy-to-use parallelism, portability and ef-ciency is a very hard task when traditional programming models are applied to networks of workstations. Even if standard message-passing libraries allow to write code that runs on various machines (code portabil-ity), the parallel application risks to be ineecient on any other platform for which a diierent domain decomposition would be the best. If we want a parallel application to be portable with eeciency (performance portability) on heterogeneous and nondedicated distributed platforms, the best domain decomposition and communication pattern cannot be chosen during implementation. In this paper, we describe the new architecture of the Dame system that hides to the programmer many details of the actual computing platform, and makes programs self-adaptable to the platform without additional eeorts to the parallelization itself.

In response to recent directives to promote quality energy efficient buildings throughout Europe, the EU funded Build UP Skills Ireland (BUSI) project launched a national skills gap analysis of the construction sector in 2011. Generally, the gap that was identified was one of knowledge rather than skills. However, this knowledge is fundamental for the successful implementation of low energy buildings. The BUSI analysis also found that the majority of trainers of construction related crafts lacked the experience and knowledge on the implementation of energy efficient buildings. Consequently, the follow on Build UP Skills QualiBuild project focussed on the development and delivery of a Train the Trainer programme which would address this. The QualiBuild Train the Trainer pilot was designed with a focus on active learning, incorporating a flipped learning model for the delivery of a blended learning programme. This was facilitated by the development of learner manuals for each of the p.

The aim of this work is to develop an application for autonomous landing. We exploit the properties of Deep Reinforcement Learning and Transfer Learning, in order to tackle the problem of planetary landing on unknown or barely-known extra-terrestrial environments by learning good-performing policies, which are transferable from the training environment to other, new environments, without losing optimality. To this end, we model a real-physics simulator, by means of the Bullet/PyBullet library, composed by a lander, defined through the standard ROS/URDF framework and realistic 3D terrain models, for which we adapt official NASA 3D meshes, reconstructed from the data retrieved during missions. Where such model were not available, we reconstruct the terrain from mission imagery - generally SAR imagery. In this setup, we train a Deep Reinforcement Learning model - using DDPG - to autonomous land on the lunar environment. Moreover, we perform transfer learning on the Mars and Titan envir.

. complex, dynamic stimulus presentations and the capacity to record and measure all responses precisely within the virtual environ-ment. . presented, followed by a description of our ongoing work developing a VR mental rotation and. more

This article is a resource for educators looking to offer personal instruction and literacy opportunities to secondary students. It provides a thoughtful and in-depth look at the workshop model in a high school setting. It offers methods and suggestions for setting up the workshop model to methods and strategies for diverse learners. The information will provide teachers with approaches to authentic and differentiated learning opportunities for all students in any secondary classroom. AUTHOR BIOGRAPHY Valerie Brunow is a high school English teacher at Millbrook High School in Millbrook, New York. She is a graduate of Manhattanville College and holds certifications and degrees in Secondary English Education and Secondary Literacy. She currently teaches ninth grade and electives for grades nine through twelve. In her career she has taught students in grades six through twelve, including; Regents, Honors, electives and small Literacy instruction groups. She has worked as a professional.

Scalar replacement of array references Data-cache optimizations r 1 Procedure integration Tail-call optimization, including tail-recursion elimination Scalar replacement of aggregates Sparse conditional constant propagation. more

by Vicente Rodríguez Montequín
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A local area network (LAN) is a computer network within a small geographical area such as a home, school, computer laboratory, office building or group of buildings. A LAN is composed of interconnected workstations and personal computers which are each capable of accessing and sharing data and devices, such as printers, scanners and data storage devices, anywhere on the LAN. LANs are characterized by higher communication and data transfer rates and the lack of any need for leased communication lines. Communication between remote parties can be achieved through a process called Networking, involving the connection of computers, media and networking devices. When we talk about networks, we need to keep in mind three concepts, distributed processing, network criteria and network structure. The purpose of this Network is to design a Local Area Network (LAN) for a BAEC (Bangladesh Atomic Energy Commission) Head Quarter and implement security measures to protect network resources and syst.

The cleft lip and palate (CLP) speech intelligibility is distorted due to the deformation in their articulatory system. For addressing the same, a few previous works perform phoneme specific modification in CLP speech. In CLP speech, both the articulation error and the nasalization distorts the intelligibility of a word. Consequently, modification of a specific phoneme may not always yield in enhanced entire word-level intelligibility. For such cases, it is important to identify and isolate the phoneme specific error based on the knowledge of acoustic events. Accordingly, the phoneme specific error modification algorithms can be exploited for transforming the specified errors and enhance the wordlevel intelligibility. Motivated by that, in this work, we combine some of salient phoneme specific enhancement approaches and demonstrate their effectiveness in improving the word-level intelligibility of CLP speech. The enhanced speech samples are evaluated using subjective and objective e.

Resumen es: El objetivo general de esta investigacion es determinar la sensibilidad o resistencia de las bacterias Escherichia coli, Pseudomona aeruginosa, Staphyloc.

Internet of things what is iot and iot security kaspersky. report most iot transactions are not secure network world. internet of things iot solutions ssl. internet of things iot solutions amp technologies samsung. secure and smart internet of things iot using. secure and smart internet of things iot using. internet of things brochure shaping europe s digital. putting the s in iot how to make internet of things. iot security 42 top internet of things security solutions. securing the internet of things a proposed framework. the teamviewer iot solution secure hyperconnectivity. internet of things iot iot security web secure media. internet of things iot security what are the issues. what is iot security internet of things security. mozilla iot. iot for smart things stmicroelectronics. about smart cards applications internet of things iot. plan to secure internet of things with new law bbc news. internet of things. securing the internet of things. internet of things iot of smart home p.

O artigo a seguir busca propor um software baseado em um sistema embarcado conectado a um banco de dados que realiza o registro em tempo real de presença de usuários por meio da leitura de cartões emissores de radiofrequência. O hardware pode ser acoplado a qualquer porta, oferecendo processamento suficiente para a leitura e armazenamento dinâmicos. A análise de dados deste sistema é realizada por uma página web construída em Javascript que realiza múltiplas conexões de registro e possui segurança de login, sendo altamente escalável a padrões empresariais. O programa tem como sua vertente principal eliminar os gastos com papel no registro de presença de instituições educacionais e empresariais, oferecendo um caráter mais sustentável a este ramo e eliminando possíveis fraudes de assinatura.

In the last twenty years, many different approaches to deal with Computer-Interpretable clinical Guidelines (CIGs) have been developed, each one proposing its own representation formalism (mostly based on the Task-Network Model) execution engine. We propose META-GLARE a shell for easily defining new CIG systems. Using META-GLARE, CIG system designers can easily define their own systems (basically by defining their representation language), with a minimal programming effort. META-GLARE is thus a flexible and powerful vehicle for research about CIGs, since it supports easy and fast prototyping of new CIG systems.

In the article the use of information technologies in an electoral process as an element of electronic democracy is investigated. Considerable attention is spared to realization of agitation over the Internet or mobile communication.

Anterior cruciate ligament (ACL) deficient and reconstructed knees display altered biomechanics during gait. Identifying significant gait changes is important for understanding normal and ACL function and is typically performed by statistical approaches. This paper focuses on the development of an explainable machine learning (ML) empowered methodology to: (i) identify important gait kinematic, kinetic parameters and quantify their contribution in the diagnosis of ACL injury and (ii) investigate the differences in sagittal plane kinematics and kinetics of the gait cycle between ACL deficient, ACL reconstructed and healthy individuals. For this aim, an extensive experimental setup was designed in which three-dimensional ground reaction forces and sagittal plane kinematic as well as kinetic parameters were collected from 151 subjects. The effectiveness of the proposed methodology was evaluated using a comparative analysis with eight well-known classifiers. Support Vector Machines were.

In this paper I measure first year student Facebook usage as part of a broader PhD study into the influence of social media usage on the success of students in higher education. A total of 906 students were asked to complete 3 surveys on Facebook usage with their peers, for two consecutive years (2011-2012 and 2012-2013). The different purposes for Facebook usage, in addition to whether or not students used (self-created) Facebook-groups, were measured and the relationship between the use of pages compared to the purpose of Facebook usage. This resulted in significant correlations between the purpose of Facebook usage and the use of different pages, as well as correlations between the purpose and use of different pages. This study hereby explores the variances in student Facebook usage and provides valuable insight into the potential value of Facebook for students in an educational setting, without the interference of teachers. It is also the next logical step in revising existing i.

https://journalistethics.com/ Free book available in PDF at this link This book lifts the veil to identify the puppet masters behind the MK Ultra Mind Control shills that front the New World Disorder Dystopia. The usual suspects are. more

https://journalistethics.com/
Free book available in PDF at this link

This book lifts the veil to identify the puppet masters behind the MK Ultra Mind Control shills that front the New World Disorder Dystopia. The usual suspects are named: Jeff Bezos, Mark Zuckerberg, Benjamin Netanyahu, Fake religious Patriarchs and a list of celebrities from Hollywood and other MK Ultra mind control realms.

Human excellences such as intelligence, morality, and consciousness are investigated by philosophers as well as artificial intelligence researchers. One excellence that has not been widely discussed by AI researchers is practical wisdom, the highest human excellence, or the highest, seventh, stage in Dreyfus’s model of skill acquisition. In this paper, I explain why artificial wisdom matters and how artificial wisdom is possible (in principle and in practice) by responding to two philosophical challenges for building artificial wisdom systems. The result is a conceptual framework that guides future research on creating artificial wisdom.

From Text to Political Positions addresses cross-disciplinary innovation in political text analysis for party positioning. Drawing on political science, computational methods and discourse analysis, it presents a diverse collection of analytical models including pure quantitative and qualitative approaches. By bringing together the prevailing text-analysis methods from each discipline the volume aims to alert researchers to new and exciting possibilities of text analyses across their own disciplinary boundary.

The volume builds on the fact that each of the disciplines has a common interest in extracting information from political texts. The focus on political texts thus facilitates interdisciplinary cross-overs. The volume also includes chapters combining methods as examples of cross-disciplinary endeavors. These chapters present an open discussion of the constraints and (dis)advantages of quantitative and qualitative methods and the affordances of combining them.

When a computer-based tool or application is used to carry out a specific task in a learning situation—that is, it is used for learning—more effectively or efficiently one speaks of learning with the tool or application. When, possibly, that same tool or application is used to enhance the way a learner works and thinks, and as such has effects that reach further than the learning situation in which it is used, then one speaks of learning from the tool or application. This article concentrates on the latter. It zooms in on the use of mindtools in education—computer programs and applications that facilitate meaningful professional thinking and working—because this is the epitome of learning from ICT. Mindtools and cognitive tools help users represent what they know as they transform information into knowledge and are used to engage in, and facilitate, critical thinking and higher order learning. These tools can be as simple as email and or discussion lists and as complicated as argume.

High prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression; and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. Aiming to guide future research and identify new directions for advancing development in this important domain, this article presents an introduction to, and a systematic review of, current ML work regarding psycho-socially .

Objective: The objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has gained much popularity and interest over the last few years. Central to this methodology is the notion of computational docking which is the process of predicting the best pose (orientation + conformation) of a small molecule (drug candidate) when bound to a target larger receptor molecule (protein) in order to form a stable complex molecule. In computational docking, a large number of binding poses are evaluated and ranked using a scoring function. The scoring function is a mathematical predictive model that produces a score that represents the binding free energy, and hence the stability, of the resulting complex molecule. Generally, such a function should produce a set of plausible ligands ranked according to their binding stability along with their binding poses. In more practical terms, an effective scoring function should produce promising drug candidates which can then be synthesized and physically screened using high throughput screening process. Therefore, the key to computer-aided drug design is the design of an efficient highly accurate scoring function (using ML techniques). Methods: The methods presented in this paper is specifically based on ML techniques. Despite many traditional techniques have been proposed, the performance was generally poor. Only in the last few years started the application of the ML technology in the design of scoring functions; and the results have been very promising.
Material: The ML-based techniques are based on various molecular features extracted from the abundance of protein-ligand information in the public molecular databases, e.g., protein data bank bind (PDBbind).
Results: In this paper, we present this paradigm shift elaborating on the main constituent elements of the ML approach to molecular docking along with the state-of-the-art research in this area. For instance, the best random forest (RF)-based scoring function (Li, 2014) on PDBbind v2007 achieves a Pear- son correlation coefficient between the predicted and experimentally deter- mined binding affinities of 0.803 while the best conventional scoring function achieves 0.644 (Cheng, 2009). The best RF-based ranking power (Ashtawy, 2012) ranks the ligands correctly based on their experimentally determined binding affinities with accuracy 62.5% and identifies the top binding ligand with accuracy 78.1%.
Conclusions: We conclude with open questions and potential future research directions that can be pursued in smart computational docking; using molecular features of different nature (geometrical, energy terms, pharmacophore), advanced ML techniques (e.g., deep learning), combining more than one ML models.
Keywords:
machine learning, random forest, support vector machine, drug discovery, computational docking, scoring function, virtual screening, complex binding affinity, ligands ranking accuracy, force field interaction, pharmacophore fingerprint.