Reviewerly/exHarmony huggingface dataset:
Division And Replication Of Data In Cloud For Data Security And Increase Performance Using Fragment Placement Algorithm

The issues identified with security and activity defeats by the Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS). In distributed computing, the data is put away on outsider space which brings about a security concerns. The client and lymph organ inside cloud may bargain the information. Along these lines, to ensure information inside the cloud senior secondary school safety efforts are required. Partition an information document into pieces, and repeat the divided information over the cloud lymph organ is done in DROPS methodological investigation. Just a section of a specific information document can be put away by every one of the hub that guarantees that no significant data is uncovered to the attacker even if there should be an occurrence of a fruitful assault.
Summary of: Dynamic Structural Operational Semantics

This short paper summarises the contributions published in the authors’ journal article [2]. The journal paper develops the theory of Dynamic Structural Operational Semantics (DSOS or Dynamic SOS) as a framework for describing semantics of programming languages that include dynamic software upgrades. DSOS is built on top of the Modular SOS since it allows a sharp separation of the program execution code from the additional structures needed at run-time. DSOS follows the same modularity and decoupling that MSOS advocates, partly motivated by the long term goal of having machine-checkable proofs for general results like type safety. Dynamic SOS has been applied on two languages supporting dynamic software upgrades, namely the low-level Proteus, which supports updating of variables, functions, records, or types at specific program points, and Creol, which supports dynamic class upgrades in the setting of concurrent objects. Existing type analyses for software upgrades can be done on top of DSOS too, as we illustrate for Proteus.
Learning Neural Search Policies for Classical Planning

Heuristic forward search is currently the dominant paradigm in classical planning. Forward search algorithms typically rely on a single, relatively simple variation of best-first search and remain fixed throughout the process of solving a planning problem. Existing work combining multiple search techniques usually aims at supporting best-first search with an additional exploratory mechanism, triggered using a handcrafted criterion. A notable exception is very recent work which combines various search techniques using a trainable policy. It is, however, confined to a discrete action space comprising several fixed subroutines. In this paper, we introduce a parametrized search algorithm template which combines various search techniques within a single routine. The template's parameter space defines an infinite space of search algorithms, including, among others, BFS, local and random search. We further introduce a neural architecture for designating the values of the search parameters given the state of the search. This enables expressing neural search policies that change the values of the parameters as the search progresses. The policies can be learned automatically, with the objective of maximizing the planner's performance on a given distribution of planning problems. We consider a training setting based on a stochastic optimization algorithm known as the cross-entropy method (CEM). Experimental evaluation of our approach shows that it is capable of finding effective distribution-specific search policies, outperforming the relevant baselines.
La Musica fra dimensione sociale e dimensione vitale

Research about music has been traditionally associated with language studies and music has been usually considered as its derivation or precursor. Instead the perspective here adopted is that of Steven Mithen: music and language have an only precursor. This perspective allows to look at music and language as two features of the human being, as two unaltered biological traits that find their actualization within specific sociocultural contexts. Nevertheless, the substantial difference between music and language strictly emerges in the dialectics between biology and culture. While the language is always inextricably connected to a specific social reality, the music seems to be more independent. This is confirmed by the fact that if we want to understand a different language, we need to be taught to do it, while we may often be capable of understanding a music, that doesn't belong to us, perfectly.  Music more than language is connected to our “deep vital side” and, better than any other symbolic instrument, is able to express it. The best example of this ability is, paradoxically, given by language. There is a type of language called maternese, the principal purpose of which is not to transmit information to a newborn baby, or to educate it but it serves to comfort it and to make it feel protected and loved, it serves to affectively tune in with it. Thus, perhaps music is an instrument that allows us to manage our constitutive ambivalence, making the loneliness possible in company.
A STUDY ON ENGLISH LANGUAGE TEACHING AT AL-IMTIYAAZ SEMI-HOME BASED SCHOOL BANDA ACEH

Abstract Name:Zuhratul Hafni Student No.:1309200080037 Department:English Education/Graduate Title:A Study on English Language Teaching at Al-Imtiyaaz Semi-Home Based School Banda Aceh. __________________________________________________________________ Keywords :English Language Teaching, Elementary School. This study was intended to: (1) describe the teacher’s preparation before teaching to elementary students at Al-Imtiyaaz semi home – school based, (2) describe how English is taught at Al-Imtiyaaz semi home – school based, (3) describe the problem faced by the teacher in teaching English to elementary students at Al-Imtiyaaz semi home – school based. This study involved a teacher who taught English at Al-Imtiyaaz semi home-based school. The data were collected through interview and observation. The finding reveals that: (1) the teacher at Al-Imtiyaz Semi Home-School Based prepared syllabus, lesson plan, and teaching materials from many resources.(2) the teacher taught the students in three phases; first, pre-learning activity when teacher prepared and motivated the students before start learning,second is core activity, when the teacher taught and deliver the material, and is third is closing activity when the teacher evaluated learning, praised students and asked students to play games to close the class. (3) there are five problem faced by the teacher; 1. Students’ variety in English competency, 2.Limited time to create English environment, 3.the context of textbooks is higher than the local curriculum, 4. Students’ emotion were not always stable when learning, 5. Some special needs students had to be taught separately.
Foreign Direct Investment and Total Factor Productivity Growth Nexus: A Realistic Testimony from VECM Approach

The main focus of current paper is to investigate the effect of foreign direct investment (FDI) on growth of total factor productivity (TFP) of Sierra Leone using financial development (FD) and trade openness (TR) as intermittent variables for the period 1990-2016. Further, this paper intends to identify the direction of causal affiliations amid employed series. Base on the application of ADF and PP-Fisher unit root tests as well as JJ co-integration tests respectively, all variables used are stationary at the first difference and also co-integrated. Through the estimation a proposed TFP model using the VECM framework, FDI is evidenced to have a significant positive effect on TFP when all other factors are held constant. Findings from the Granger causality test further revealed unidirectional liaison from FDI to TFP in the long-run whilst bidirectional causality is evidenced in the short-run. We therefore conclude that in order to increase the positive externalities and spillover benefits of FDI at the corporate level, Sierra Leone could provide assistance to foreign investment companies in management and worker training.
Mindset and Meditative Inquiry

This paper discusses Carol Dweck’s work on growth and fixed mindset and offers a conceptual critique of both mindsets’ limitedness from a holistic perspective. Toward this critique, the author draws on Ashwani Kumar’s writing on meditative inquiry. Kumar’s writing is based on the premise that our external conflicts result from our fractured, internal psychological states. Whereas growth mindset posits that we should constantly be striving toward something, Kumar’s writing asserts that striving toward a goal will only result in more internal conflict. Growth, for Kumar, is only desirable in as much as it does not interfere with one’s self-understanding. Through holding Dweck’s work in conversation with Kumar’s, new insights into both perspectives emerge. This paper concludes with a discussion of the implications of meditative inquiry and growth mindset for teachers and students, ultimately encouraging both groups to ask serious questions and give themselves time to answer them.
Efficient Motion Planning for Mobile Robots Dealing with Changes in Rough Terrain

This paper proposes a novel motion planning method that can deal with changes in rough terrain efficiently. Previous studies on motion planning in rough terrain did not consider significant changes in the environment. To solve this problem, we propose an efficient re-planning method. The re-planning method is characterized by using search tree repeatedly as long as it is still valid. Reusing search tree enables limiting the re-planning region, which leads to the shorter planning time. After re-planning, the re-planned path is deformed to shorten the length of the path. Experimental results show that the proposed method can generate a collision-free path against a significant environmental change and perform the re-planning within a short time.
Exponential Family Graph Embeddings.

Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional \textit{Skip-Gram} model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic \textit{exponential family graph embedding} model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks.
Proposal for teaching reform of ship fluid mechanics

《Ship Fluid Mechanics》 is an important professional foundation course for ship and ocean engineering. Almost all subsequent courses related to ship principles and dynamic performance of offshore structures are based on it. It is a course of difficult and comprehensive knowledge according to students’ reflection. Therefore, this paper aims to carry out reforms and attempts from the teaching methods to improve students' devotion and enthusiasm and make it easier for students to understand and master.
Don't Risk your Postal Vote with Missing Paperwork Warning

Postal voters in Ribble Valley are being warned against returning their ballot paper without the necessary paperwork.
ASSESSMENT OF STUDENTS DURING THE PERFORMANCE OF LABORATORY WORK IN PHYSICS

In the article on the basis of the analysis of the worked out sources it is determined the necessity and expediency of carrying out cross-cutting assessment of students of physical-mathematical and technical profiles of training during the performance of laboratory works. It is conducted with the aim of timely establishment of the level of professional competence formation during training. This is an indicator of the individual's ability to succeed and confidently continue his professional development.Assessment at various stages of preparation and conduct of experimental activities of students gives the opportunity to establish the level of its correspondence to professional activity by different indicators characteristic of specialists of these profiles. In this way, students are able to gradually form and systematically support students with stable motivational potential to study, to realize their practical importance of the material and to ensure confidence in their own knowledge and skills in their practical use in further professional activity.Key words: understanding, awareness, training, teacher, questions, experiment, activity, practical importance, professional training.
Emotion Prediction in Movies Using Visual Features & Genre Information

There are many application fields to predict the emotion in multimedia content automatically such as offering personalized media options to the users, indexing media. With the developments in deep learning, the issues have become more popular. In this study, it is aimed to predict the emotion elicited from movies by using convolutional neural network approaches from the visual based features. In addition, the effect of movie genre in emotion prediction in terms of valence and arousal score is analyzed separately by using the LIRIS-ACCEDE movie dataset. As the main contribution, the dataset is deeply analyzed according to film genres. After that, it is categorized into the training groups in a way that the same genre movies are proportionally distributed, and well-known CNN networks are utilized for regression training.
간호대학생의 모유수유 간호활동에 영향을 미치는 요인

Purpose: This study was conducted to examine the relationships among knowledge, attitudes and nursing activities while breastfeeding among nursing students, and to identify factors influencing nursing activities in breastfeeding. Methods: The research participants were 172 nursing students in the nursing departments of 3 universities located in G city and J provinces. The students had successfully completed their obstetric and pediatric clinical practice courses. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation coefficient and multiple regression. Results: Participants’ scores for knowledge, attitudes and nursing activities while breastfeeding were 16.24±2.73, 3.74±0.43 and 3.49±0.43, respectively. Knowledge and attitudes toward breastfeeding had positive correlations with nursing activities while breastfeeding. Factors influencing nursing students’ nursing activities while breastfeeding included knowledge, attitudes toward breastfeeding, grade, breastfeeding educational experience, and breastfeeding intentions. These variables explained 18% of the variance in nursing activities while breastfeeding. Conclusion: Based on the results of this study, it would be beneficial to enhance knowledge and attitudes toward breastfeeding by developing education programs for breastfeeding.
An improved data structure for left-right maximal generic words problem

For a set D of documents and a positive integer d, a string w is said to be d-left-right maximal, if (1) w occurs in at least d documents in D, and (2) any proper superstring of w occurs in less than d documents. The left-right-maximal generic words problem is, given a set D of documents, to preprocess D so that for any string p and for any positive integer d, all the superstrings of p that are d-left-right maximal can be answered quickly. In this paper, we present an O(n log m) space data structure (in words) which answers queries in O(|p| + o log log m) time, where n is the total length of documents in D, m is the number of documents in D and o is the number of outputs. Our solution improves the previous one by Nishimoto et al. (PSC 2015), which uses an O(n log n) space data structure answering queries in O(|p|+ r * log n + o * log^2 n) time, where r is the number of right-extensions q of p occurring in at least d documents such that any proper right extension of q occurs in less than d documents.
ROLES OF ICT IN TECHING AND LEARNING PHYSICS: FOCUS ON SENIOR SECONDARY SCHOOLS IN AZARE

Information and communication technology (ICT) is defined by UNESCO as forms of technology used for creating, displaying, storing, manipulating, and exchanging information. Effective teaching and learning of science including that of physics can easily be carried out in today’s modern world using information and communication technology (ICT) facilities. To become a great among the committee of nations, Nigeria must change her method of teaching and learning of science education from the traditional way of talk and chalk method. The paper examines on the extent of ICT integration in teaching and learning of physics; attitude of physics teachers and students towards the use of ICT as well as the extent of management and technical support given to physics teachers in the use of ICT. The paper is of the view and concluded that the students perform better when taught with ICT. Keywords: Teachers, ICT, physics, students, teaching
Digital model for convenience of education of students

Almost every activity in the world today is controlled by computer driven software programs. This trend was first accommodated by engineering applications in the past. However, s the life style became more and more complex, every area of human interactions was invaded by various Software systems, such as real time, business, simulation, embedded, web based, personal and more recently, artificial intelligence software etc. According to the above facts, managing and maintaining a book store could also be controlled by efficient software. This project focuses attention on designing efficient and reliable software which controls the transactions of a bookstore. In real world, it tends to associate with automated systems as they provide many benefits than doing the same thing in manually. As mentioned, here we have introduced a system which can be used to maintain an online bookstore. When we are concerning the manual process of a bookshop, the major problem is the waste of time. A customer has to waste his/her valuable time when he needs to buy a book as all the events such as searching, purchasing are done by members of the staff. In briefly, the manual process is very slow. But automation will reduce the time taken in the whole process.
Handling complex systems: systems design for AD vehicles

Architecture Trend: Architectures move from Distributed to Centralized to Service-Oriented Architectures Complexity: Adoption of new automotive trends (e.g. zones & servers) helps to cope with complexity Process: Champion best practices in Systems Engineering Development enhanced by dedicated system architecture views.
Analysis of Generalized Expectation-Maximization for Gaussian Mixture Models: A Control Systems Perspective

The Expectation-Maximization (EM) algorithm is one of the most popular methods used to solve the problem of parametric distribution-based clustering in unsupervised learning. In this paper, we propose to analyze a subclass of generalized EM (GEM) algorithms in the context of Gaussian mixture models, where the maximization step in the EM is replaced by an increasing step. We show that this subclass of GEM algorithms can be understood as a linear time-invariant (LTI) system with a feedback nonlinearity. Therefore, we explore some of its convergence properties by leveraging tools from robust control theory. Lastly, we explain how the proposed GEM can be designed, and present a pedagogical example to understand the advantages of the proposed approach.
Fact-Finding or Exploration: Identifying Latent Behavior Clusters in User’s Search Activities

Understanding the search behavior of users based on their current interaction with an information retrieval system, such as a search engine, can be quite beneficial to develop advanced support mechanisms. Statistical models comprise a rich framework for characterizing user’s search behavior for known search activity classes but also for identifying unknown/latent search behavior clusters. To gather deeper insights of search activities, we apply Hidden Markov Models to classify search sessions of known search activities with an accuracy of 88.58% and derive behavioral properties of users from these models. Afterwards, we extend these models to Mixture Models and cluster the users’ search behavior to reveal strong associations to the known search activities with an agreement of 79.1%, but also to identify new search behavior. That is, instead of just classifying already known search activities, our methodology also allows to identify new search activities solely on behavior parameters independent of the known search activities. Hence, in addition to qualitatively determined and evaluated search patterns, this statistical approach can be used to reveal new search activities from the data and therefore to further understand the human information seeking process. The method is suitable to detect new search behaviors as generic patterns in a latent parameter space. This enables the identification of user’s search activities that might be triggered contradictory to the originally expected search behavior and/or can reveal behavioral sub patterns.
Fast MCMC algorithms, Stability and DeepTune

Author(s): Chen, Yuansi | Advisor(s): Yu, Bin | Abstract: Drawing samples from a known distribution is a core computational challenge common to many disciplines, with applications in statistics, probability, operations research, and other areas involving stochastic models. In statistics, sampling methods are useful for both estimation and inference, including problems such as estimating expectations of desired quantities, computing probabilities of rare events, gauging volumes of particular sets, exploring posterior distributions and obtaining credible intervals etc.Facing massive high dimensional data, both computational efficiency and good statistical guarantees are more and more important in modern statistical and machine learning applications. In this thesis, centered around sampling algorithms, we consider the fundamental questions on their computational and statistical guarantees: How to design a fast sampling algorithm and how long should it be run? What are the statistical learning guarantee of these algorithms? Are there any trade-offs between computation and learning?To answer these questions, first we start with establishing non-asymptotic convergence guarantees for popular MCMC sampling algorithms in Bayesian literature: Metropolized Random Walk, Metropolis-adjusted Langevin algorithm and Hamiltonian Monte Carlo. To address a number of technical challenges arise enroute, we develop results based on the conductance profile in order to prove quantitative convergence guarantees general continuous state space Markov chains. Second, to confront a large class of constrained sampling problems, we introduce two new algorithms, Vaidya and John walks, to sample from polytope-constrained distributions with convergence guarantees. Third, we prove fundamental trade-off results between statistical learning performance and convergence rate of any iterative learning algorithm, including sample algorithms. The trade-off results allow us to show that a too stable algorithm can not converge too fast, and vice-versa. Finally, to help neuroscientists analyze their massive amount of brain data, we develop DeepTune, a stability-driven visualization and interpretation framework via optimization and sampling for the neural-network-based models of neurons in visual cortex.