EVALUATION OF THE ENGLISH LANGUAGE TEACHING PROGRAM OF THE FACULTY OF EDUCATION, UNIVERSITY OF BENGHAZI USING THE PEACOCK MODEL: TOWARDS QUALITY EDUCATION

The English Language Teaching (ELT) is a theory and practice of English teaching and learning for the welfare of the non-native students whose language is not English. It is an English language degree in exploring the application of the language development and current practice in teaching and testing. The study aims to identify the aspects of the English Language Teaching (ELT) and how do the aspects of the English Language Teaching (ELT) program should be maintained and improved based on the Peacock’s Model and Evaluation. The study employs the quantitative and qualitative descriptive research design and method. The study comprised seventeen (17) alumni of the Faculty of Education, English Department, University of Benghazi, Libya as these participants had experienced the evaluation of the English Language Teaching program to be maintained and to be improved in the Faculty of Education. Purposive sampling is utilized in the study because it is non-probability which is known as judgmental, subjective, and selective sampling. Results show that participants are encouraged to be a reflective teacher when they start teaching and taught to evaluate themselves as a teacher which is vital and important in ELT profession in the Faculty of Education and in English as a Foreign Language, show to promote flexibility in using different teaching practices in different situation and classroom management skills in terms of participation, show that there is adequate training and teaching skills and balances of teacher-centered and student-centered learning on its courses in ELT in terms of experimentation, show to avoid overlapping information between different courses and are ready to teach ELT of the program in the university in terms of application, and show that participants are encouraged to reflect on their past experiences as a language learners and are encouraged to be a reflective teacher in their teaching process in terms of cooperation.  Article visualizations

BREXIT: Psychometric Profiling the Political Salubrious Through Machine Learning: Predicting personality traits of Boris Johnson through Twitter political text

Whilst the CIA have been using psychometric profiling for decades, Cambridge Analytica showed that people\u27s psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook or Twitter accounts. To exploit this form of psychological assessment from digital footprints, we propose machine learning methods for assessing political personality from Twitter. We have extracted the tweet content of Prime Minster Boris Johnson’s Twitter account and built three predictive personality models based on his Twitter political content. We use a Multi-Layer Perceptron Neural network, a Naive Bayes multinomial model and a Support Machine Vector model to predict the OCEAN model which consists of the Big Five personality factors from a sample of 3355 political tweets. The approach vectorizes political tweets, then it learns word vector representations as embeddings from spaCy that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the quality of the predictions for each trait per model from a classification algorithm. Our findings show that all three models compute the personality trait “Openness” with the Support Machine Vector model achieving the highest accuracy. “Extraversion” achieved the second highest accuracy personality score by the Multi-Layer Perceptron neural network and Support Machine Vector model

Probing many-body dynamics on a 51-atom quantum simulator

Controllable, coherent many-body systems can provide insights into the fundamental properties of quantum matter, enable the realization of new quantum phases and could ultimately lead to computational systems that outperform existing computers based on classical approaches. Here we demonstrate a method for creating controlled many-body quantum matter that combines deterministically prepared, reconfigurable arrays of individually trapped cold atoms with strong, coherent interactions enabled by excitation to Rydberg states. We realize a programmable Ising-type quantum spin model with tunable interactions and system sizes of up to 51 qubits. Within this model, we observe phase transitions into spatially ordered states that break various discrete symmetries, verify the high-fidelity preparation of these states and investigate the dynamics across the phase transition in large arrays of atoms. In particular, we observe robust manybody dynamics corresponding to persistent oscillations of the order after a rapid quantum quench that results from a sudden transition across the phase boundary. Our method provides a way of exploring many-body phenomena on a programmable quantum simulator and could enable realizations of new quantum algorithms.Comment: 17 pages, 13 figure

The Minimum Shared Edges Problem on Grid-like Graphs

We study the NP-hard Minimum Shared Edges (MSE) problem on graphs: decide whether it is possible to route $p$ paths from a start vertex to a target vertex in a given graph while using at most $k$ edges more than once. We show that MSE can be decided on bounded (i.e. finite) grids in linear time when both dimensions are either small or large compared to the number $p$ of paths. On the contrary, we show that MSE remains NP-hard on subgraphs of bounded grids. Finally, we study MSE from a parametrised complexity point of view. It is known that MSE is fixed-parameter tractable with respect to the number $p$ of paths. We show that, under standard complexity-theoretical assumptions, the problem parametrised by the combined parameter $k$, $p$, maximum degree, diameter, and treewidth does not admit a polynomial-size problem kernel, even when restricted to planar graphs

Population genetic data for 17 Y STR markers from Benghazi (East Libya)

The seventeen Y-STR loci included in the AmpF‘STR1 YfilerTM PCR Amplification kit (DYS19, DYS389I,DYS389II, DYS390, DYS391, DYS392, DYS393, DYS385a/b, DYS438, DYS439, DYS437, DYS448, DYS458,DYS456, DYS635, and Y-GATA-H4) were used to type a sample population of 238 males from eastern Libya (Benghazi region). Of 238 observed haplotypes, 214 were unique (90%) and 24 (10%) were found more than once. The 17 loci gave a discriminating power of 0.999. DYS458 showed the highest diversity as a single-locus marker (0.73). Allelic frequencies and gene diversities for each Y-STR locus were determined. The high haplotype diversity and discrimination capacity (0.996) demonstrate the utility of these loci for human identification in forensic applications. Comparative analysis with Y-STR datasets of relevant populations and submission of the haplotypes to the Y-STR Haplotype Reference Database (YHRD) was undertaken

Integrating Neural Networks with a Quantum Simulator for State Reconstruction

We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator, by means of a neural network model incorporating known experimental errors. Specifically, we extract restricted Boltzmann machine (RBM) wavefunctions from data produced by a Rydberg quantum simulator with eight and nine atoms in a single measurement basis, and apply a novel regularization technique to mitigate the effects of measurement errors in the training data. Reconstructions of modest complexity are able to capture one- and two-body observables not accessible to experimentalists, as well as more sophisticated observables such as the R\'enyi mutual information. Our results open the door to integration of machine learning architectures with intermediate-scale quantum hardware.Comment: 15 pages, 13 figure

The Complexity of Routing with Few Collisions

We study the computational complexity of routing multiple objects through a network in such a way that only few collisions occur: Given a graph $G$ with two distinct terminal vertices and two positive integers $p$ and $k$, the question is whether one can connect the terminals by at least $p$ routes (e.g. paths) such that at most $k$ edges are time-wise shared among them. We study three types of routes: traverse each vertex at most once (paths), each edge at most once (trails), or no such restrictions (walks). We prove that for paths and trails the problem is NP-complete on undirected and directed graphs even if $k$ is constant or the maximum vertex degree in the input graph is constant. For walks, however, it is solvable in polynomial time on undirected graphs for arbitrary $k$ and on directed graphs if $k$ is constant. We additionally study for all route types a variant of the problem where the maximum length of a route is restricted by some given upper bound. We prove that this length-restricted variant has the same complexity classification with respect to paths and trails, but for walks it becomes NP-complete on undirected graphs