Reviewerly/exHarmony huggingface dataset:
Amelioration of Google Assistant – A Review of Artificial Intelligence Stimulated Second Language Learning and Teaching

Artificial Intelligence (AI) has become an undeniable technological innovation in our world. The usability of AI-powered applications has gradually increased in all fields. In need of change and adaptability in life, many AI tools have been developed to accomplish certain tasks faster. AI-featured Intelligent Personal Assistant (IPA) applications like Google Assistant (GA), Alexa, Siri, Cortona, and Bixby are involved in the process of helping humankind to achieve certain actions in a faster mode. The evolvement of Industry 4.0 and Education 4.0 triggers, as well as, challenges the language curriculum to adapt AI-based applications to engage in second language learning. Among all the above-mentioned AI-featured applications, Google Assistant predominantly involves in language learning and teaching. The main objective of this paper is to review the Al-powered Google Assistant for teaching and learning languages. It specifically reviews and examines the study on the use of Google Assistant in terms of teaching and learning a language. The approach used to evaluate the articles pulled from pertinent databases is the qualitative research method, especially content analysis. The findings of the study show that there are four distinct patterns in which AI-powered Google Assistant is used to teach and learn languages. The endorsement of AI-powered Google Assistant and pedagogy based on it proves that it is very helpful for second language acquisition.
IOT Based Health Monitoring System

Abstract: As we know that Health is a very important factor for humans. In order to maintain good health and to be able to overcome diseases it is essential to have a good healthcare system. Coronavirus outbreaks have put more emphasis on health care these days. A remote monitoring system based on IOT seems to be the better solution for pandemic such as Coronavirus. Taking advantage of the new revolution of the internet that is Internet of Things, which is an expanding research area, especially in the healthcare industry. The objective of this system is to monitor health care remotely from wherever you wish
Journey of Free and Compulsory Elementary Education in India

Education is a process of human enlightenment and empowerment for developing a better quality of life. The progress of any nation can be gauged from its achievement in education. Education is the tool that can play a vital role in improving the socio-economic conditions and prosperity of a nation. According to Indian Constitution, Article 21A states that it will be the responsibility of government to provide free and compulsory elementary education to all the children of age group 6 to 14 years. The right of free and compulsory elementary education in India has faced many ups and downs. After very long journey Indian Parliament passed RTE Act 2009 on August 4, 2009, which describes the modalities of the importance of free and Compulsory Education for children between 6 to 14 years in India under article 21A of the Indian constitution. This article tries step to step review of the efforts made by various stack holders for free and compulsory elementary education in India.
Language Learning, ESL with Literature is No more Drudgery

Effective learning needs the use of language since it is the means through which learners construct their ideas and interact with the ideaso of others.Literature has been a unique source for learning a language for many years.
Comparative analysis of mobile forensic proprietary tools: an application in forensic investigation

The utilization of the Internet and wireless communication reaches its pinnacle from one side of the planet to the other. Marking the rise of criminal activity in recent years sees enormous growth in security breaches and data theft-related cases in mobile phones. To mitigate them, the implementation of security patches, safety fixes, and updates in mobile devices is of high priority for the organization. The need to foster techniques and procedures in the field to be able to extract and precisely dissect digital crime cases, providing valuable tactical data about the investigation. Mobile forensics is a developing branch assisting the investigator in criminal trials and investigations. Acquisition, Collection, and Analysis of mobile phones settle the purpose of recovering cumulative and corroborative evidence. Upgradation and innovation of mobile devices with time imposed a challenge to mobile forensic technology to extract information from such devices. The study aims at extracting comparative and statistical approaches in the analysis of Physical data acquisition utilizing significant versatile mobile criminological proprietary tools. The proposed study also introduces newly developed utility tools along with their characteristic features which help in successful data extraction from mobile devices.
A Study of the Effectiveness of the Self-instructional Material of the Foundation Course in Mathematics

The present study results reveal that, the developed course content reached the target learners those who do not have mathematics at their formal education and those admissions made through Eligibility Test. Over all out of ﬁve criterion tests, the reference group showed better performance on three tests, and the experimental group showed better performance on two tests. It reveals that, the developed course content has the internal validity. Thus it shows that the self-Instructional Course Material in Mathematics is equally eﬀective with that of the Course Material studied by Intermediate students with Mathematics. The performance of the experimental group [post-test scores] in the achievement test in Mathematics was better when compared with pre-test scores of the distance learners taught through Self- Instructional Course Material. Further, the results show that the self-Instructional Course Material in Mathematics is equally eﬀective when the performance of experimental group was compared with that of the reference group i.e. Intermediate students with Mathematics. The study revealed that, the analysis of the data during the course development reveal that, there is no signiﬁcant diﬀerence on the scores of comprehensive and that of combined criterion tests. It shows that the course content as a whole is eﬀective and students’ comprehensive level.
VENTEL: An Attempt of Transforming Rural India

Education plays a constructive role in the development of society and the nation. India where two-third of its total population resides in rural areas needs a comprehensive planning for the development of their educational status that guarantees their economic development by providing employment, may be self-employment. Basic Education as suggested by Mahatma Gandhi in 1937 was an attempt to promote work centric education with the aim to achieve the educational, economic and overall development mainly in rural India. It was reconstructed as Nai Talim in 1944-45, but the basic idea of craft centeredness was unchanged. Vocational Education Nai Talim Experiential Learning (VENTAL) can be referred as a subsequent attempt of Nai Talim adopted by Mahatma Gandhi National Council of Rural Education under the Ministry of Education, Government of India in the present time to promote work centric education with economic value. This paper will be an attempt to study different aspects of VENTAL as a whole.
Identification of Problems Associated with Listening Skill of Bangladeshi Students: A Literature Review

This paper is an endeavor to focus on some problems of listening skill of learners at tertiary level in private universities in Bangladesh. In communicative language teaching, four language skills are equally important, but in Bangladesh listening is neglected from secondary to tertiary level of education. This essay tries to pinpoint the problems associated with poor listening skill of Bangladeshi students and proposes some practical approaches to solve these problems. Arguably, such an effort may not only help in shedding light on key listening problems but also greatly help in understanding the pedagogical implications of listening for classroom practice, in general, and in the Bangladeshi ELT context, in particular.
Movie Reveiw Generator

This Android app will help the people to know about Semantics of the movies. In this project, we propose a Movie Recommendation System by combining Naive Bayes Algorithm with Collaborative filtering. The recommendation system is able to predict the everchanging area of interest or tastes of users about the movies. In this project, we propose a Movie Recommendation System by combining Naive Bayes Algorithm with Collaborative filtering. The recommendation system is able to predict the everchanging area of interest or tastes of users about the movies.
Pengembangan Aplikasi Berbasis Android Untuk Sistem Kesejahteraan Sosial Terpadu Kesejahteraan Sosial Kabupaten Jember

Validation of the Integrated Social Welfare Data (DTKS) needs to be carried out because the government uses this data as a basis for providing various forms of social assistance. Valid data is one of the efforts so that the social assistance provided is right on target. Jember Regency is one of the districts instructed to validate the data. The University of Jember through the Institute for Research and Community Service (LP2M) is working with the Regional Government of Jember Regency to carry out DTKS validation efforts. This activity then developed an Android-based application that enumerators used to validate in the field. The data used in the application is based on data already owned by the Jember District Social Service
Pelatihan Media Pembelajaran Berbasis Komik bagi Guru SD di Wilayah Kabupaten Tasikmalaya

With the government program related to the provision of IT facilities and infrastructure in schools in the Tasikmalaya Regency area, it is challenging for teachers to be able to use them optimally. However, their knowledge and skills regarding the use of interactive learning media are still limited. Thus, the service regarding training and mentoring of learning media is deemed necessary to improve the competence of elementary school teachers in the Tasikmalaya Regency area. The description of the implementation of training and assistance in the use of comic-based interactive learning media consists of: (1) training on the use of comics as a learning medium for elementary school-aged children that focuses on numeracy literacy and reading literacy; (2) assistance in designing comic-based learning media for elementary school-aged children using storyboardthat.com. (3) improving teacher skills in implementing learning for elementary school-aged children through comic-based learning. The output targets that will result from the implementation of this regional scheme service (PbM-KW), are publications in national journals, and online media/local newspapers.
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

One-shot neural architecture search (NAS) substantially improves the search efficiency by training one supernet to estimate the performance of every possible child architecture (i.e., subnet). However, the inconsistency of characteristics among subnets incurs serious interference in the optimization, resulting in poor performance ranking correlation of subnets. Subsequent explorations decompose supernet weights via a particular criterion, e.g., gradient matching, to reduce the interference; yet they suffer from huge computational cost and low space separability. In this work, we propose a lightweight and effective local intrinsic dimension (LID)-based method NAS-LID. NAS-LID evaluates the geometrical properties of architectures by calculating the low-cost LID features layer-by-layer, and the similarity characterized by LID enjoys better separability compared with gradients, which thus effectively reduces the interference among subnets. Extensive experiments on NASBench-201 indicate that NAS-LID achieves superior performance with better efficiency. Specifically, compared to the gradient-driven method, NAS-LID can save up to 86% of GPU memory overhead when searching on NASBench-201. We also demonstrate the effectiveness of NAS-LID on ProxylessNAS and OFA spaces. Source code: https://github.com/marsggbo/NAS-LID.
EM's Convergence in Gaussian Latent Tree Models

We study the optimization landscape of the log-likelihood function and the convergence of the Expectation-Maximization (EM) algorithm in latent Gaussian tree models, i.e. tree-structured Gaussian graphical models whose leaf nodes are observable and non-leaf nodes are unobservable. We show that the unique non-trivial stationary point of the population log-likelihood is its global maximum, and establish that the expectation-maximization algorithm is guaranteed to converge to it in the single latent variable case. Our results for the landscape of the log-likelihood function in general latent tree models provide support for the extensive practical use of maximum likelihood based-methods in this setting. Our results for the EM algorithm extend an emerging line of work on obtaining global convergence guarantees for this celebrated algorithm. We show our results for the non-trivial stationary points of the log-likelihood by arguing that a certain system of polynomial equations obtained from the EM updates has a unique non-trivial solution. The global convergence of the EM algorithm follows by arguing that all trivial fixed points are higher-order saddle points.
Project-based Learning in the University Course and its Effectiveness

The effusively theoretical education belongs to the highly important problems within the area of education. Achievement of practical experience within the studied field improves students’ knowledge and skills. This is similarly valid in the field of business informatics, where students can deepen their knowledge by developing a practical project, which is a form of project-based learning. The article aims to present how methods of project-based learning can be implemented in the business informatics university course. The educational process’ effectiveness in the university course is measured using learning gains measures in the available periods. Comparison of the learning gains with project-based learning and without them shows improvement in the effectiveness of the educational process.
Information- and Coding-Theoretic Analysis of the RLWE Channel

Several cryptosystems based on the \emph{Ring Learning with Errors} (RLWE) problem have been proposed within the NIST post-quantum cryptography standardization process, e.g., NewHope. Furthermore, there are systems like Kyber which are based on the closely related MLWE assumption. Both previously mentioned schemes result in a non-zero decryption failure rate (DFR). The combination of encryption and decryption for these kinds of algorithms can be interpreted as data transmission over a noisy channel. To the best of our knowledge this paper is the first work that analyzes the capacity of this channel. We show how to modify the encryption schemes such that the input alphabets of the corresponding channels are increased. In particular, we present lower bounds on their capacities which show that the transmission rate can be significantly increased compared to standard proposals in the literature. Furthermore, under the common assumption of stochastically independent coefficient failures, we give lower bounds on achievable rates based on both the Gilbert-Varshamov bound and concrete code constructions using BCH codes. By means of our constructions, we can either increase the total bitrate (by a factor of $1.84$ for Kyber and by factor of $7$ for NewHope) while guaranteeing the same DFR or for the same bitrate, we can significantly reduce the DFR for all schemes considered in this work (e.g., for NewHope from $2^{-216}$ to $2^{-12769}$).
eLife ending old publishing model

eLife’s decision to cease the acceptance-rejection mechanism for manuscript publication has caught the great attention of many publishers and scholars.
Collective creativity in ensemble music performance: A comparison of professional human performances and multi-agent performances

Ensemble music is a familiar example of collective creativity. However, it remains unclear how the process of changes from individual creativity (i.e. solo performance) leads to collective creativity (i.e. ensemble performance) and how we can simulate this process. Thus, this study investigated whether a multi-agent system can sufficiently simulate human ensemble performances using recorded solo performances by skilled professional performers. The original solo performance of a professional human performer was assigned to an agent as an initial value, and a simulation was conducted. Focusing on the timing profile, which is crucial for both expression and coordination during music performances, we compared the human music ensemble performances with the multi-agent ensemble performances. The main findings are as follows: (1) the multi-agent performance shares many common aspects with the human ensemble performance; (2) at the structural boundaries of the musical piece, where the tempo changes drastically, the difference between the human performance and the multi-agent performance became significant; (3) by making the agent’s negotiation strategy similar to that of a human, the similarities of the multi-agent and human performances increased. These results can be used to increase our understanding of collective creativities, the creation of new musical performances, and human–agent collaboration.
Capacity and selection in immersive visual working memory following naturalistic object disappearance

Visual working memory – holding past visual information ‘in mind’ for upcoming behaviour – is commonly studied following the abrupt removal of visual objects from static 2D displays. In everyday life, visual objects do not typically vanish from the environment in front of us. Rather, visual objects tend to enter working memory following self or object motion: disappearing from view gradually and changing the spatial relation between memoranda and observer. Here, we used virtual reality (VR) to investigate whether two classic findings from visual working memory research – a capacity of around three objects and the reliance on space for object selection – generalise to more naturalistic modes of object disappearance. Our static reference condition mimicked traditional laboratory tasks whereby visual objects were held static in front of the participant and removed from view abruptly. In our critical flow condition, the same visual objects flowed by participants, disappearing from view gradually and behind the observer. We considered visual working memory capacity as well as space-based mnemonic selection, indexed by directional biases in gaze. Despite vastly distinct modes of object disappearance and altered spatial relations between memoranda and observer, we found comparable capacity and comparable gaze signatures of space-based mnemonic selection. This reveals how classic findings from visual working memory research generalise to immersive situations with more naturalistic modes of object disappearance and with dynamic spatial relations between memoranda and observer.
Discovering Evolution Strategies via Meta-Black-Box Optimization

Optimizing functions without access to gradients is the remit of black-box methods such as evolution strategies. While highly general, their learning dynamics are often times heuristic and inflexible - exactly the limitations that meta-learning can address. Hence, we propose to discover effective update rules for evolution strategies via meta-learning. Concretely, our approach employs a search strategy parametrized by a self-attention-based architecture, which guarantees the update rule is invariant to the ordering of the candidate solutions. We show that meta-evolving this system on a small set of representative low-dimensional analytic optimization problems is sufficient to discover new evolution strategies capable of generalizing to unseen optimization problems, population sizes and optimization horizons. Furthermore, the same learned evolution strategy can outperform established neuroevolution baselines on supervised and continuous control tasks. As additional contributions, we ablate the individual neural network components of our method; reverse engineer the learned strategy into an explicit heuristic form, which remains highly competitive; and show that it is possible to self-referentially train an evolution strategy from scratch, with the learned update rule used to drive the outer meta-learning loop.
Deterministic generation of qudit photonic graph states from quantum emitters

We propose and analyze deterministic protocols to generate qudit photonic graph states from quantum emitters. We exemplify our approach by constructing protocols to generate absolutely maximally entangled states and logical states of quantum error correcting codes. Some of these protocols make use of time-delayed feedback, while others do not. These results significantly broaden the range of multi-photon entangled states that can be produced deterministically from quantum emitters.
Relating Regularization and Generalization through the Intrinsic Dimension of Activations

Given a pair of models with similar training set performance, it is natural to assume that the model that possesses simpler internal representations would exhibit better generalization. In this work, we provide empirical evidence for this intuition through an analysis of the intrinsic dimension (ID) of model activations, which can be thought of as the minimal number of factors of variation in the model's representation of the data. First, we show that common regularization techniques uniformly decrease the last-layer ID (LLID) of validation set activations for image classification models and show how this strongly affects generalization performance. We also investigate how excessive regularization decreases a model's ability to extract features from data in earlier layers, leading to a negative effect on validation accuracy even while LLID continues to decrease and training accuracy remains near-perfect. Finally, we examine the LLID over the course of training of models that exhibit grokking. We observe that well after training accuracy saturates, when models ``grok'' and validation accuracy suddenly improves from random to perfect, there is a co-occurent sudden drop in LLID, thus providing more insight into the dynamics of sudden generalization.
Reconstruction of helm graph and web graph

The graph reconstruction conjecture asserts that every simple undirected graph with number of vertices (where <em>n</em> ≥ 3) is uniquely determined up to isomorphism, by its collection of unlabeled form of vertex deleted sub graphs. If a vertex deleted sub graph of <em>G</em> is given in unlabeled form, then it is called a “card” of <em>G</em>. The collection of all cards of <em>G</em> is called the “deck” of <em>G</em>, and is denoted by <em>D</em>(<em>G</em>) . If we can determine the original graph <em>G</em> from the deck of the graph <em>G</em>, we can say that it is reconstructible. In this paper we propose a new method to reconstruct the helm graph (<em>H<sub>n</sub></em>) and the web graph (<em>W<sub>n</sub></em>), by using the degree sequence of its collection of vertex deleted sub graphs. Helm graph is obtained by adjoining a pendant edge to each node of the cycle of the -wheel graph. Web graph is obtained by joining pendant vertices to the vertices in the outer cycle of the helm graph to form a cycle and again adding pendant vertices to the new cycle. For both graphs we found decks and obtained the degree sequence of each cards. Finally, we reconstructed the original graph using the degree sequences.