Reviewerly/exHarmony huggingface dataset:
ASR Rescoring and Confidence Estimation with ELECTRA

In automatic speech recognition (ASR) rescoring, the hypothesis with the fewest errors should be selected from the n-best list using a language model (LM). However, LMs are usually trained to maximize the likelihood of correct word sequences, not to detect ASR errors. We propose an ASR rescoring method for directly detecting errors with ELECTRA, which is originally a pre-training method for NLP tasks. ELECTRA is pre-trained to predict whether each word is replaced by BERT or not, which can simulate ASR error detection on large text corpora. To make this pre-training closer to ASR error detection, we further propose an extended version of ELECTRA called phone-attentive ELECTRA (P-ELECTRA). In the pre-training of P-ELECTRA, each word is replaced by a phone-to-word conversion model, which leverages phone information to generate acoustically similar words. Since our rescoring method is optimized for detecting errors, it can also be used for word-level confidence estimation. Experimental evaluations on the Librispeech and TED-LIUM2 corpora show that our rescoring method with ELECTRA is competitive with conventional rescoring methods with faster inference. ELECTRA also performs better in confidence estimation than BERT because it can learn to detect inappropriate words not only in fine-tuning but also in pre-training.
DEPICTING INDONESIAN INGENIOUS VALUES AS PORTRAYED IN ARIFIN C. NOER’S THE BOTTOMLESS WELL T0 TEACH BIPA

The aim of this study is to depict and implement the function of Indonesian ingenious values as portrayed in Arifin C. Noer’s The Bottomless Well to Teach Bahasa Indonesia to speakers of other languages (BIPA). The problems of this research, may be formulated as “What Indonesian local values may be revealed in Arifin C. Noer’s The Bottomless Well ?” and “How may such findings provide students of BIPA with better understanding on Indonesian cultural aspects?”The so-called research method, design, and techniques are thoroughly explained in the related papers found in the Journal of Language Teaching and Research , while Journalism and Investigative Critical Writing is to deal with “readership” and “scientific writing style.” Katresnanism theory is principally implementing the ways of thinking of the communal society such as Indonesians and at the same time distinguishing those of the individual societies. Implementing such an approach to teach in BIPA will not only make the students of BIPA better understand and master Bahasa Indonesia , but they will also be avoided from misunderstanding while communicating with Indonesian people. It is, in fact, highlingting the importance of understanding one’s culture in order to master one’s language.
Learning with ANIMA

The paper develops a semi-formal model of learning which modifies the traditional paradigm of artificial neural networks, implementing deep learning by means of a key insight borrowed from the works of Marvin Minsky: the so-called Principle of Non-Compromise. The principle provides a learning mechanism which states that conflicts in the processing of data to be integrated are a mark of unreliability or irrelevance; hence, lower-level conflicts should lead to higher-level weight-adjustments. This internal mechanism augments the external mechanism of weight adjustment by back-propagation, which is typical for the standard models of machine learning. The text is structured as follows: (§1) opens the discussion by providing an informal overview of real-world decision-making and learning; (§2) sketches a typology of decision architectures: the individualistic approach of classical decision theory, the general aggregation mechanism of social choice theory, the local aggregation mechanism of agent-based modeling, and the intermediate hierarchical model of Marvin Minsky's “Society of Mind”; (§3) sketches the general outline of ANIMA – a new model of decision-making and learning that borrows insights from Minsky's informal exposition; (§4) is the bulk of the paper; it provides a discussion of a toy exemplification of ANIMA which lets us see the Principle of Non-Compromise at work; (§5) lists some possible scenarios for the evolution of a model of this kind; (§6) is the closing section; it discusses some important differences between the way ANIMA was construed here and the typical formal rendering of learning by means of artificial neural networks and deep learning.
A LIFELONG LEARNING AS AN INCENTIVE OF LEADERSHIP: KEY ELEMENT IN LEADERSHIP DEVELOPMENT

Lifelong learning is an important factor in leadership development. This article addresses the relationship between lifelong learning and leadership. There were asked 60 participants about lifelong learning. The obtained findings of the study contribute to theory of leadership, lifelong learning and could help in the process of improving the society. A leadership development program can incorporate the findings of theories of learning and improve implementation of the knowledge in everyday life.
Introduce the Result Into Self-Attention

Traditional self-attention mechanisms in convolutional networks tend to use only the output of the previous layer as input to the attention network, such as SENet, CBAM, etc. In this paper, we propose a new attention modification method that tries to get the output of the classification network in advance and use it as a part of the input of the attention network. We used the auxiliary classifier proposed in GoogLeNet to obtain the results in advance and pass them into attention networks. we added this mechanism to SE-ResNet for our experiments and achieved a classification accuracy improvement of at most 1.94% on cifar100.
Convolutional Neural Networks Progress: Architectural and Optimization Methods Survey

Since the start of the Convolutional Neural Networks (CNN) paradigm, they were applied in a wide range of computer vision tasks such as image classification, object detection, localization, tracking and action recognition where they were able to show breakthrough performance and generate a new state of the art results. This paper surveys the progress of CNN from an architectural and optimization perspective. While many CNN reviews exist in the literature, most of them had focused on providing a survey either from a network architecture prospective or an application one, unlike this one which provides a brief general overview for the key features of CNN, followed by reviewing the progress of the state of the art architectures and finally considers the change in the merit of figure of how the CNN are evaluated to include the optimization methods to provide practical CNN that can be deployed on today’s hardware infrastructure without significantly impacting the achieved accuracy.
DEVELOPMENT OF WEB-BASED LEARNING TO TEACH ENGLISH AS A FOREIGN LANGUAGE

Web-based language learning is a form of adaptation in educational field. It provides many benefits on its implementation, i.e. web-based learning is available anytime, anyplace, to anyone-irrespective of time and distance. Thus, the development of this method becomes a concern during this time. This paper presents the use of EditPlus and HotPotatoes to support the development of web-based language learning, especially in English as a Foreign Language learning. Using this this technology, the project maker can get many benefits, such as (1) teacher can create the web-site as needed so that the content and design would not bother students’ focus, (2) HotPotatoes provides many kinds of interactive exercises which has certain benefits for each type to support students’ understanding and support students’ exploration on the material, and (3) the technologies are accessible and easy to be used. Moreover, the result of this project is a website that has its own benefits. It is well-designed, has a brief and clear explanation, and provides interactive exercises which can stimulate learners’ understanding and critical thinking.
IMPROVING OPINION MINING OF SOCIAL MEDIA POLITICAL CONTENT

Twitter is a micro-blogging website that provides a platform for people to share and express their views about topics, happenings, products, and other services. In numerous fields of standard dialect handling, past investigations of feeling acknowledgment on Twitter, for the most part, centered around the utilization of vocabularies and straightforward classiﬁers on the pack of words models. Tweets can be characterized into various classes dependent on their pertinence with the point looked. The existing system creates only three significant emotions labeled data sets corresponding to diﬀerent classiﬁcation of emotions. Automatic sentiment analysis and opinion mining of texts collected from social networks based on political tweets and microblogging websites are oriented toward the binary classiﬁcation (i.e., classiﬁcation into positive and negative) or classiﬁcation into positive, negative, and neutral (i.e., the ternary classiﬁcation) of texts. Classiﬁcation and result are not more accurate as compared to the proposed system. Most active topics are not mentioned in the existing system.
The effect of using Audio Visual Chat on seventh grade students' oral skills in Jordan and their attitudes toward it: أثر استخدام الدردشة المرئية على المهارات اللفظية لمتعلمي اللغة الإنجليزية لدى طلاب الصف السابع في الأردن واتجاهاتهم نحوها

This study aimed to examine the effect of audiovisual chat on seventh grade students English foreign language (EFL) learners’ oral skills. It also investigated the seventh grade students' attitudes toward using audiovisual chat in teaching and learning oral skills in English. The study participants consisted of 32 seventh grade female students from Al Khanasiri Mixed Elementary School at Al- Mafraq, North West Badia Region during the first semester of the scholastic year 2020/2021. The study's participants were divided into two groups: The first group was experimental consisted of (16) students who used the audiovisual chat and the second group was a control one consisted of (16) students who were submitted to regular instruction. To carry out this study, two instruments were used which were the pre- test and post- test. They were of two parts: a speaking and reading test that examined students’ ability to interact appropriately in communicative settings and a questionnaire. To measure the effects of audiovisual chat on the seventh grade learners’ oral skills, before beginning the experiment, both groups will take a speaking and reading test to measure their existing awareness at the start of the first semester of the academic year 2020/2021. The study's findings show that the experimental group performed better in the oral ability than the control group, due to this new teaching approach that incorporates audiovisual chat. The study's findings reveal that the experimental group is more inspired than the control group. This is due to the effect of using audiovisual chat. Accordingly, a number of conclusions and recommendations are provided.
Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications

Many critical applications rely on cameras to capture video footage for analytical purposes. This has led to concerns about these cameras accidentally capturing more information than is necessary. In this paper, we propose a deep learning approach towards protecting privacy in camera-based systems. Instead of specifying specific objects (e.g. faces) are privacy sensitive, our technique distinguishes between salient (visually prominent) and non-salient objects based on the intuition that the latter is unlikely to be needed by the application.
RECALL: Replay-Based Continual Learning in Semantic Segmentation

Deep networks allow to obtain outstanding results in semantic segmentation, however they need to be trained in a single shot with a large amount of data. Continual learning settings where new classes are learned in incremental steps and previous training data is no longer available are challenging due to the catastrophic forgetting phenomenon. Existing approaches typically fail when several incremental steps are performed or in presence of a distribution shift of the background class. We tackle these issues by recreating no longer available data for the old classes and outlining a content inpainting scheme on the background class. We propose two sources for replay data. The first resorts to a generative adversarial network to sample from the class space of past learning steps. The second relies on web-crawled data to retrieve images containing examples of old classes from online databases. In both scenarios no samples of past steps are stored, thus avoiding privacy concerns. Replay data are then blended with new samples during the incremental steps. Our approach, RECALL, outperforms state-of-the-art methods.
The Empowerment of Home Assimilation Reporting Supervision System Application (Sappar) for Correctional Assisted of Child Prisoners Residents

One of the issues in the COVID-19 pandemic is the liberation of prisoners in order to control the spread of the COVID-19 by rescuing the child prisoners through the program of assimilation and integration.This program is under the supervision of the advisor community using the SAPPAR application (System Application for Monitoring and Reporting of the House Assimilation) which has been disseminated by the local office (Kanwil) of ministry of law and human rights (Kemenkumham) in West Java, Indonesia.This study aims to determine the effectiveness of SAPPAR as an assimilation program of the correctional inmates (WBP).This application is used to facilitate the monitoring and supervision of children inmates while performing assimilation in their house.The study of sociological law (socio legal research) was conducted to describe the phenomena of assimilation program during the COVID-19 pandemic for the correctional child prisoners.A qualitative approach and descriptive data analysis were conducted.The study focuses on legal analysis and psychological behavior of the child prisoners during assimilation as well as community behavior analysis when receiving prisoners back to their environment, the interaction between children, and the community assistance.
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization

We show that stochastic acceleration can be achieved under the perturbed iterate framework (Mania et al., 2017) in asynchronous lock-free optimization, which leads to the optimal incremental gradient complexity for finite-sum objectives. We prove that our new accelerated method requires the same linear speed-up condition as the existing non-accelerated methods. Our core algorithmic discovery is a new accelerated SVRG variant with sparse updates. Empirical results are presented to verify our theoretical findings.
Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs

We propose nonparametric Bayesian estimators for causal inference exploiting Regression Discontinuity/Kink (RD/RK) under sharp and fuzzy designs. Our estimators are based on Gaussian Process (GP) regression and classification. The GP methods are powerful probabilistic machine learning approaches that are advantageous in terms of derivative estimation and uncertainty quantification, facilitating RK estimation and inference of RD/RK models. These estimators are extended to hierarchical GP models with an intermediate Bayesian neural network layer and can be characterized as hybrid deep learning models. Monte Carlo simulations show that our estimators perform comparably to and sometimes better than competing estimators in terms of precision, coverage and interval length. The hierarchical GP models considerably improve upon one-layer GP models. We apply the proposed methods to estimate the incumbency advantage of US house elections. Our estimations suggest a significant incumbency advantage in terms of both vote share and probability of winning in the next elections. Lastly we present an extension to accommodate covariate adjustment.
Challenges and Opportunities of Speech Recognition for Bengali Language

Speech recognition is a fascinating process that offers the opportunity to interact and command the machine in the field of human-computer interactions. Speech recognition is a language-dependent system constructed directly based on the linguistic and textual properties of any language. Automatic Speech Recognition (ASR) systems are currently being used to translate speech to text flawlessly. Although ASR systems are being strongly executed in international languages, ASR systems' implementation in the Bengali language has not reached an acceptable state. In this research work, we sedulously disclose the current status of the Bengali ASR system's research endeavors. In what follows, we acquaint the challenges that are mostly encountered while constructing a Bengali ASR system. We split the challenges into language-dependent and language-independent challenges and guide how the particular complications may be overhauled. Following a rigorous investigation and highlighting the challenges, we conclude that Bengali ASR systems require specific construction of ASR architectures based on the Bengali language's grammatical and phonetic structure.
Systematic Assessment of Formal Methods Based Models Quality Criteria

When presented with two fully proved formal methods-based specifications, how can a System Engineer decide which is superior when both models specify the same requirements, but in two different ways? This paper investigates and propose a methodology by which formal methods (using the specific example of the Event-B notation) can be differentiated in terms of their quality, using criteria that may be highly subjective in nature. Established complexity functions applied to software are not applicable to formal methods, thus the paper proposes a new function which quantifies the “quality” of a given model. Complexity is not the only factor involved in determining the quality of formal methods, the quality of system thinking involved also play an impactful role. We propose a quality function which uses the well-established properties of axiomatic systems in theoretical mathematics with the addition of a specifically formulated complexity function. The distinction criteria are based on evaluating how four main properties have been achieved: “Consistency”, “Completeness”, “Independence” and “Complexity”. We base our approach according to the paradigm of; “if the formal specification looks visually complicated for a set-theory novice, then it is a poorly modeled specification”. Furthermore, we explore the notion of Miller’s rule (magic No. 7) to define what “good” should look like. We conclude that we need more than Miller’s 7, we need 1, 2 and 3 to help us with defining what good quality looks like, by taking human cognitive capacity as a benchmark. This novel approach implies considerable further research, described in future work section.
The Influence of English Ability and Educational Methods on the Military Vehicles Operationalization for Cadets in the Army and Military Academy at Cavalry Educational Center, Padalarang, Indonesia

English skills are needed in the military field which always faces dynamic conditions. This research was intended to determine the relationship between English ability and educational methods used by military cadets at the Pusat Pendidikan Kavaleri on their ability to operate military vehicles. The method used is a quantitative method with population were all Army Secapa cadets and the Indonesian Army Military Academy who were currently studying at the Cavalry Education Center (Pusdik Kav) in Padalarang, West Java Province, Indonesia. Data collection technique used was observational technique, which was to examine directly by using a questionnaire and interview. The result of this research is that there is a significant relationship between the English Ability and the Educational method on military vehicles operationalization. In other words, if the English Ability and the Educational method is increased, the military vehicles operationalization for the Indonesian Army will be greater. In addition, the findings of this research are useful for military cadets and institution itself.
Fast and accurate phase-unwrapping algorithm based on the transport of intensity equation: reply

In [ Appl. Opt. 56 , 7079 ( 2017 ) APOPAI 0003-6935 10.1364/AO.56.007079 ], a phase-unwrapping algorithm has been reported that is based on the transport of intensity (TIE) equation. Section 3 of that manuscript presented one way to derive an analytical expression for the axial intensity derivative using the paraxial angular spectrum (AS) method. In the recent comment by Yoneda et al. [ Appl. Opt. 60 , 7500 ( 2021 ) APOPAI 0003-6935 10.1364/AO.417146 ], the authors misunderstand the utility of the angular spectrum method and conclude that the corresponding derivation is ambiguous. In this response, we take the opportunity to correct a typo that clarifies the calculation of the axial derivative of the intensity.
An Efficient Secure Division Protocol Using Approximate Multi-Bit Product and New Constant-Round Building Blocks

Integer division is one of the most fundamental arithmetic operators and is ubiquitously used. However, the existing division protocols in secure multi-party computation (MPC) are inefficient and very complex, and this has been a barrier to applications of MPC such as secure machine learning. We already have some secure division protocols working in ℤ2n. However, these existing results have drawbacks that those protocols needed many communication rounds and needed to use bigger integers than in/output. In this paper, we improve a secure division protocol in two ways. First, we construct a new protocol using only the same size integers as in/output. Second, we build efficient constant-round building blocks used as subprotocols in the division protocol. With these two improvements, communication rounds of our division protocol are reduced to about 36% (87 rounds → 31 rounds) for 64-bit integers in comparison with the most efficient previous one.
Mineralogy, geochemistry and genesis of Qara Sabalan manganese deposit, north of Meshginshahr (Ardabil Province)

Mineralogy, geochemistry and genesis of Qara Sabalan manganese deposit, north of Meshginshahr (Ardabil Province)
Self Supervision to Distillation for Long-Tailed Visual Recognition

Deep learning has achieved remarkable progress for visual recognition on large-scale balanced datasets but still performs poorly on real-world long-tailed data. Previous methods often adopt class re-balanced training strategies to effectively alleviate the imbalance issue, but might be a risk of over-fitting tail classes. The recent decoupling method overcomes over-fitting issues by using a multi-stage training scheme, yet, it is still incapable of capturing tail class information in the feature learning stage. In this paper, we show that soft label can serve as a powerful solution to incorporate label correlation into a multi-stage training scheme for long-tailed recognition. The intrinsic relation between classes embodied by soft labels turns out to be helpful for long-tailed recognition by transferring knowledge from head to tail classes. Specifically, we propose a conceptually simple yet particularly effective multi-stage training scheme, termed as Self Supervised to Distillation (SSD). This scheme is composed of two parts. First, we introduce a self-distillation framework for long-tailed recognition, which can mine the label relation automatically. Second, we present a new distillation label generation module guided by self-supervision. The distilled labels integrate information from both label and data domains that can model long-tailed distribution effectively. We conduct extensive experiments and our method achieves the state-of-the-art results on three long-tailed recognition benchmarks: ImageNet-LT, CIFAR100-LT and iNaturalist 2018. Our SSD outperforms the strong LWS baseline by from $2.7\%$ to $4.5\%$ on various datasets. The code is available at https://github.com/MCG-NJU/SSD-LT.