Reviewerly/exHarmony huggingface dataset:
An Analysis of Conversational Implicature in CNN World Exclusive Interview With Recep Tayyip Erdogan

This research focuses on (1) identifying the types of conversational implicature in CNN World Exclusive Interview with Recep Tayyip Erdogan and (2) analyzing the maxim flouted by Recep Tayyip Erdogan in CNN World Exclusive Interview. This research applies descriptive qualitative research. The data analyzed are utterances containing implicature in 7 segments of CNN World Exclusive Interview with Recep Tayyip Erdogan. The data were analyzed by using Grice theory (1975) and Yule (1996) and to analyze the flouting maxim using the theory of Grice (1975). The technique of collecting data is the observation and documentation method. Based on the analysis the writer found that: (1) there are two types of the types of conversational implicature are used, generalized conversational implicature 22% and particularized conversational implicature 78% (2) there are four types of flouting maxim are used, flouting maxim of quality 22%, flouting maxim of quantity 16%, flouting maxim of relevance 48% and flouting maxim of manner 14%. The result from this study is indeed different from the previous research because politician’s exclusive interview has been researched before. Therefore, this study can complete the research about conversational implicature.
Effect of Interest Rate Deregulation on Loans and Advances of Deposit Money Banks in Nigeria

The study examined the impact of the lending and deposit rates in the face of deregulation on the loans and advances of deposit money banks in the country covering the period of 1986 to 2019 using annual time series data.Using the Autoregressive Distributed Lag (ARDL) model, findings from the study revealed that the deregulation of interest rate in Nigeria encouraged the disbursement of loans and advances within the economy, but it was however not significant.In addition, the study found that the policy led to an inverse relationship between deposit rate and loans and advances in the country.Higher deposit rates significantly discouraged deposit money banks from granting loans and advances.To ensure that interest rate deregulation has a much significant effect on the loans and advances of deposit money bank, the deregulation of the sector must be full, as against the partial deregulation being presently practiced, to encourage the desired level of competition which would spur the growth of the sector, and ultimately expand credit facilities for the Nigerian economy.
Strengthening ICT competencies in Higher Secondary Education teachers to improve teaching practice in the health emergency of Covid 19

SARS-CoV-2 has brought enormous challenges and challenges to the global education system. The crisis has demonstrated the vulnerability of current educational models, their rapid and accelerated spread throughout the world has caused an unprecedented impact on the multidimensional development of the population. This brought as a possible solution the implementation and accelerated adaptation of working at a distance with approaches oriented to the use of Information and Communication Technologies (ICT), The project proposed as an objective to evaluate the knowledge and skills management of upper secondary teachers (northern part of the state of Nayarit), before and during the COVID-19 pandemic. The study is a non-experimental descriptive transectional study with a sample of teachers from the three Preparatory Academic Units of the UAN. A non-probability convenience sampling was used using two pre-designed questionnaires through the Google Forms platform, one at the beginning and the other at the end. With this project it was detected that no teacher uses digital tools at the beginning for online classes.
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning

In recent years, model-agnostic meta-learning (MAML) has become a popular research area. However, the stochastic optimization of MAML is still underdeveloped. Existing MAML algorithms rely on the ``episode'' idea by sampling a few tasks and data points to update the meta-model at each iteration. Nonetheless, these algorithms either fail to guarantee convergence with a constant mini-batch size or require processing a large number of tasks at every iteration, which is unsuitable for continual learning or cross-device federated learning where only a small number of tasks are available per iteration or per round. To address these issues, this paper proposes memory-based stochastic algorithms for MAML that converge with vanishing error. The proposed algorithms require sampling a constant number of tasks and data samples per iteration, making them suitable for the continual learning scenario. Moreover, we introduce a communication-efficient memory-based MAML algorithm for personalized federated learning in cross-device (with client sampling) and cross-silo (without client sampling) settings. Our theoretical analysis improves the optimization theory for MAML, and our empirical results corroborate our theoretical findings. Interested readers can access our code at \url{https://github.com/bokun-wang/moml}.
Learning from Multiple Noisy Partial Labelers

Programmatic weak supervision creates models without hand-labeled training data by combining the outputs of heuristic labelers. Existing frameworks make the restrictive assumption that labelers output a single class label. Enabling users to create partial labelers that output subsets of possible class labels would greatly expand the expressivity of programmatic weak supervision. We introduce this capability by defining a probabilistic generative model that can estimate the underlying accuracies of multiple noisy partial labelers without ground truth labels. We show how to scale up learning, for example learning on 100k examples in one minute, a 300x speed up compared to a naive implementation. We also prove that this class of models is generically identifiable up to label swapping under mild conditions. We evaluate our framework on three text classification and six object classification tasks. On text tasks, adding partial labels increases average accuracy by 8.6 percentage points. On image tasks, we show that partial labels allow us to approach some zero-shot object classification problems with programmatic weak supervision by using class attributes as partial labelers. On these tasks, our framework has accuracy comparable to recent embedding-based zero-shot learning methods, while using only pre-trained attribute detectors.
LibGuides: ACRL Academic Library Services for Graduate Students Interest Group: Notable Works 2021

Programming Information for ACRL's Academic Library Services for Graduate Students Interest Group.
Awareness of Security Threats in Social Media

Social media are sites on the Internet that allow users to interact and communicate with each other and since it is easy to use and produce a fast way to communicate. According to these features, it becomes very popular to everyone either the young generation or the old generation. But we need to be concerned about security and privacy. Is our information safe? Will we be safe while we using these sites?  This paper presents the reasons that make us unsafe in social media. We'll also show some of the ways that we should follow to protect ourselves. So, in short, we can say that this research aims to increase the awareness of the risks of social media threats. Also, suggest some solutions to reduce the damage that may happen
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models

Cross-lingual document representations enable language understanding in multilingual contexts and allow transfer learning from high-resource to low-resource languages at the document level. Recently large pre-trained language models such as BERT, XLM and XLM-RoBERTa have achieved great success when fine-tuned on sentence-level downstream tasks. It is tempting to apply these cross-lingual models to document representation learning. However, there are two challenges: (1) these models impose high costs on long document processing and thus many of them have strict length limit; (2) model fine-tuning requires extra data and computational resources, which is not practical in resource-limited settings. In this work, we address these challenges by proposing unsupervised Language-Agnostic Weighted Document Representations (LAWDR). We study the geometry of pre-trained sentence embeddings and leverage it to derive document representations without fine-tuning. Evaluated on cross-lingual document alignment, LAWDR demonstrates comparable performance to state-of-the-art models on benchmark datasets.
Research on the Construction of Comprehensive Training System for Business Administration Majors in Colleges Based on Computer

Abstract Advanced-math course has the typical characteristics of wide concept, deep thinking, complex calculation and proof, which makes it difficult to learn. Therefore, for a long time, there have been some practical problems, such as students’ low interest in learning, poor learning effect and so on. On account of this, this paper first analyses the utilization status of blended-mode of advanced-math, then studies the design of blended-mode of advanced-math on account of cyber teaching platform, and gives the design objectives and utilization strategies.
Distribution of Gaussian entanglement in linear-optical systems

Entanglement is an essential ingredient for building a quantum network that can have many applications. Understanding how entanglement is distributed in a network is a crucial step to move forward. Here we study the conservation and distribution of Gaussian entanglement in a linear network using a new quantifier for bipartite entanglement. We show that the entanglement can be distributed through a beam splitter in the same way as the transmittance and reflectance. The requirements on the entangled states and the type of networks to satisfy this relation are presented explicitly. Our results provide a useful quantification for quantum entanglement and further insights into the structure of entanglement in a network.
Novel View Video Prediction Using a Dual Representation

We address the problem of novel view video prediction; given a set of input video clips from a single/multiple views, our network is able to predict the video from a novel view. The proposed approach does not require any priors and is able to predict the video from wider angular distances, upto 45 degree, as compared to the recent studies predicting small variations in viewpoint. Moreover, our method relies only onRGB frames to learn a dual representation which is used to generate the video from a novel viewpoint. The dual representation encompasses a view-dependent and a global representation which incorporates complementary details to enable novel view video prediction. We demonstrate the effectiveness of our framework on two real world datasets: NTU-RGB+D and CMU Panoptic. A comparison with the State-of-the-art novel view video prediction methods shows an improvement of 26.1% in SSIM, 13.6% in PSNR, and 60% inFVD scores without using explicit priors from target views.
DOCTOR: A Simple Method for Detecting Misclassification Errors

Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations where DNN are implemented as "black boxes". A promising approach to secure their use is to accept decisions that are likely to be correct while discarding the others. In this work, we propose DOCTOR, a simple method that aims to identify whether the prediction of a DNN classifier should (or should not) be trusted so that, consequently, it would be possible to accept it or to reject it. Two scenarios are investigated: Totally Black Box (TBB) where only the soft-predictions are available and Partially Black Box (PBB) where gradient-propagation to perform input pre-processing is allowed. Empirically, we show that DOCTOR outperforms all state-of-the-art methods on various well-known images and sentiment analysis datasets. In particular, we observe a reduction of up to $4\%$ of the false rejection rate (FRR) in the PBB scenario. DOCTOR can be applied to any pre-trained model, it does not require prior information about the underlying dataset and is as simple as the simplest available methods in the literature.
دور القواعد الإرجونوميکية الصحيحة في تحسين العزف على الآلات الموسيقية التربوية لدى تلاميذ المرحلة الابتدائية

هدفت الدراسة الحالية إلى التعرف على دور القواعد الإرجونوميکية الصحيحة في تحسين العزف على الآلات  الموسيقية التربوية لدى تلاميذ المرحلة الابتدائية . ولتحقيق أهداف الدراسة،  تبنت الباحثة المنهج الوصفي و تناولت نشأة الارجونوميکا وأهميتها في مجال الموسيقى والمبادئ الأساسية لها والتدخل الارجونوميکي لتحسين بيئة العزف وظروفها وأداء العازفين، ثم تناول البحث عرضا للأهداف العامة والخاصة لتعليم الموسيقى للنشئ، وکيفية تحقيقها عمليا باتباع القواعد الارجونوميکية بشکل تفصيلي على آلة الاکسيليفون والريکوردر والأوکورديون. The Role of the Proper Ergonomic Rules in Improving Primary School Pupils’ Playing Classroom Musical Instruments Dr. Doaaalfagr Mohamed Sami Abbas Subhi Assistant Professor of Curriculum and Music Education Faculty of Specific Education – Zagazig University Abstract This current study aimed at identifying the role of the proper ergonomic rules in improving primary school pupils’ playing classroom musical instruments. For fulfilling the study objectives, the researcher adopted the qualitative approach and tackled the evolution of ergonomics, its significance in the field of music, its basic principles, and the ergonomic intervention for improving music playing environment, its conditions and the players’ performance. Then, the researcher provided a presentation of the general aims and objectives for teaching music to the grown-ups, and how to apply them practically in detail to the xylophone, the recorder and the accordion.
CHARACTER EDUCATION THROUGH LITERATURE LEARNING IN WOMEN'S MORRORS OF NH DINI

One of the most important parts in adequate literary learning is character education. Language is a literary characteristic as a work of art. This is one reason why character education needs to be taught in literary learning. In teaching literary works, there are 3 (three) methods of teaching literature that can be applied, namely (a) comparative method is the method used in learning that provides opportunities for students to compare two or more literary works that have the same topic, (b) the method of fostering creativity is to train students' imagination by as if sending a letter to an author whose content responds to or appreciates literary works written by the author, and (c) an Impressive method is a method that is done by listening to poetry and drama performances.
SPLIE: Optimal Illumination Estimation for Structure Preserving Low-light Image Enhancement

The images taken in low-light conditions often have many flaws such as, color vividness and low visibility which negatively affects the performance of many vision-based systems. Many of the existing Retinex-based enhancement algorithms improve the visibility of low-light images via estimating the illumination map and use it to obtain the corresponding reflectance. However, the improper estimation of the initial illumination map may produce unsatisfactory illuminated enhanced images with weak color constancy. To address this problem, this paper proposes an efficient algorithm for the enhancement of low-light images. In this algorithm, the initial illumination map is obtained by the fusion between the maximum color channel and bright channel prior. The estimated initial illumination map is then refined using a multi-objective problem that contains the illumination regularization terms specifically, the structural and textural details of the illumination. The optimization problem is solved using the alternative direction minimization (ADM) technique with the augmented Lagrangian multiplier to produce structure-aware smoothness of the initial illumination map. Finally, the contrast of the refined illumination map is adjusted using the gamma correction method. Experimental results on several benchmark datasets reveal the superiority of the proposed algorithm on the state-of-the-art algorithms in terms of qualitative and quantitative analysis. Furthermore, the proposed algorithm produces enhanced images with reducing the artifacts and preserving the naturalness and structural details.
An Analytical Study on the Use of Electronic Resources by the Students and Research Scholars of Annamalai University, Tamil Nadu

This paper deals with E-resources such as E-book, E-journals, E-papers. Most of the students and scholars using the E-resources. E-resources plays an important role in the library and information science. Opinions regarding Frequency of library visit of the users, E-resources usage were gathered from 250 users out of which 200 valid questionnaire received from the scholars.
LEARNING SYSTEM DESIGN FOR GAME APPLICATIONS

The presented paper investigates the problem of designing a learning system for agents in intelligent game applications based on Unity Game Engine and reinforcement machine learning algorithms. Modern trends in the game applications development are characterized by the active using of the concept of an intelligent agent as a behavior model of an active element in various situations with applying various strategies for interactions with other active elements and the environment. In recent years, there have been a significant number of advances in this area, such as DeepMind and the Deep Q learning architecture, the winning of the Go Game Champion with AlphaGo, OpenAI and PPO. Unity developers have implemented a support for machine learning and, in particular, for deep reinforcement learning in order to create a deep reinforcement learning the SDK (Software Development Kit) for game and simulation developers. With Unity and ML-Agents toolkits we can create physically, visually, and cognitively rich environments, including ones for evaluating new algorithms and strategies. However, learning system design for agents in Unity ML-Agents is possible only by using the Python API. The possibility of a learning system design for agents in the Flappy Bird game application based on the Unity Game Engine with using its own environment is discussed in this paper. Separately, the paper highlights typical features of the Flappy Bird gaming application environment. The environment can be implemented as a fully observable environment or a partially observable environment. The fully observable environment is suggested to be used due to all environment states in this case are seen in the playfield. Thus, the problem of strategy formation is considered as a Markov decision-making process and the agent directly observes the current state of the environment. Temporal Difference Learning is used as a learning method; it involves the assessment of a reward at each stage. Two separate environments, deterministic and stochastic, have been implemented, that allows to conduct further research and evaluation of strategy formation algorithms.
Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training

In this paper, we propose Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. In contrast to standard adversarial training algorithms, IAT encourages the model to be sensitive to the perturbation in the dialogue history and therefore learning from perturbations. By giving higher rewards for responses whose output probability reduces more significantly when dialogue history is perturbed, the model is encouraged to generate more diverse and consistent responses. By penalizing the model when generating the same response given perturbed dialogue history, the model is forced to better capture dialogue history and generate more informative responses. Experimental results on two benchmark datasets show that our approach can better model dialogue history and generate more diverse and consistent responses. In addition, we point out a problem of the widely used maximum mutual information (MMI) based methods for improving the diversity of dialogue response generation models and demonstrate it empirically.
THE ANALYSIS OF MODULE-COMPETENCE APPROACH IN EDUCATIONAL PROCESS’DESIGNOF KAZAKHSTAN AND LATVIA UNIVERSITIES

The article presents a theoretical and methodological comparative analysis of the module-competencybased approach for the educational process’designin universities in Latvia and Kazakhstan. The article is intended for a survey and analytical study of the diversification of methods, technologies in two post-Soviet countries with different educational paths. The study contains an analysis of the legal and regulatory documents for the specialties “Vocational Training”, “Design”, “Scenography”, as well as a comparative analysis of the pedagogical process organizationof the professional competence formationin Kazakhstan and Latvia. The purpose of the study is to analyze two types of educational process in universities that carry out activities within the framework of the Bologna system. The objective of the study is to identify the differences and similarities in the organization of the pedagogical process in both countries, and to determine their systems, structure and forms. The results of the study are shown in the development of conclusions for the educational process in universities of Kazakhstan.
Object Based Attention Through Internal Gating

Object-based attention is a key component of the visual system, relevant for perception, learning, and memory. Neurons tuned to features of attended objects tend to be more active than those associated with non-attended objects. There is a rich set of models of this phenomenon in computational neuroscience. However, there is currently a divide between models that successfully match physiological data but can only deal with extremely simple problems and models of attention used in computer vision. For example, attention in the brain is known to depend on top-down processing, whereas self-attention in deep learning does not. Here, we propose an artificial neural network model of object-based attention that captures the way in which attention is both top-down and recurrent. Our attention model works well both on simple test stimuli, such as those using images of handwritten digits, and on more complex stimuli, such as natural images drawn from the COCO dataset. We find that our model replicates a range of findings from neuroscience, including attention-invariant tuning, inhibition of return, and attention-mediated scaling of activity. Understanding object based attention is both computationally interesting and a key problem for computational neuroscience.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues

Anaphora and ellipses are two common phenomena in dialogues. Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses. Traditionally, anaphora is resolved by coreference resolution and ellipses by query rewrite. In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding. Given an ongoing dialogue between a user and a dialogue assistant, for the user query, our joint learning model first predicts coreference links between the query and the dialogue context, and then generates a self-contained rewritten user query. To evaluate our model, we annotate a dialogue based coreference resolution dataset, MuDoCo, with rewritten queries. Results show that the performance of query rewrite can be substantially boosted (+2.3% F1) with the aid of coreference modeling. Furthermore, our joint model outperforms the state-of-the-art coreference resolution model (+2% F1) on this dataset.