Reviewerly/exHarmony huggingface dataset:
A Transformer with Interleaved Self-attention and Convolution for Hybrid Acoustic Models

Transformer with self-attention has achieved great success in the area of nature language processing. Recently, there have been a few studies on transformer for end-to-end speech recognition, while its application for hybrid acoustic model is still very limited. In this paper, we revisit the transformer-based hybrid acoustic model, and propose a model structure with interleaved self-attention and 1D convolution, which is proven to have faster convergence and higher recognition accuracy. We also study several aspects of the transformer model, including the impact of the positional encoding feature, dropout regularization, as well as training with and without time restriction. We show competitive recognition results on the public Librispeech dataset when compared to the Kaldi baseline at both cross entropy training and sequence training stages. For reproducible research, we release our source code and recipe within the PyKaldi2 toolbox.
Turning challenges into opportunity: Potential for adoption of e-extension in Lesotho

Farmers in Lesotho mostly depend on rain-fed agriculture and are in need of access to agricultural information and knowledge in a timely, complete and quality manner. The traditional practice for delivering agricultural information has mainly relied on agricultural extension staff visiting farmers in order to provide advisory services. Due to the limited number of extension staff and other challenges, the delivery of agricultural extension services through the traditional farm and home visits has been ineffective. This paper presents challenges that the agricultural extension system in Lesotho faces, which necessitate the adoption of information and communication technologies (ICTs) as a basis for improving access of farmers to extension services. The advent of ICTs has given rise to e-extension, which is the delivery of extension services using web tools that allow online sharing, collaboration and networking. A recent study has revealed that agricultural extension professionals in Lesotho recognize the potential and use of e-extension in the country and are well positioned to embrace its introduction. The paper, therefore, recommends that efforts should be made to gradually introduce e-extension in the country, capitalizing on the existing enthusiasm among extension professionals and lessons of best practice from elsewhere.&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Key words: Agricultural extension, e-extension, information and communication technology (ICT). &nbsp
A new partial image encryption method for document images using variance based quad tree decomposition

The proposed method partially and completely encrypts the gray scale Document images. The complete image encryption is also performed to compare the performance with the existing encryption methods. The partial encryption is carried out by segmenting the image using the Quad-tree decomposition method based on the variance of the image block. The image blocks with uniform pixel levels are considered insignificant blocks and others the significant blocks. The pixels in the significant blocks are permuted by using 1D Skew tent chaotic map. The partially encrypted image blocks are further permuted using 2D Henon map to increase the security level and fed as input to complete encryption. The complete encryption is carried out by diffusing the partially encrypted image. Two levels of diffusion are performed. The first level simply modifies the pixels in the partially encrypted image with the Bernoulli’s chaotic map. The second level establishes the interdependency between rows and columns of the first level diffused image. The experiment is conducted for both partial and complete image encryption on the Document images. The proposed scheme yields better results for both partial and complete encryption on Speed, statistical and dynamical attacks. The results ensure better security when compared to existing encryption schemes.
Automatic body mass index detection using correlation of face visual cues

Body mass index (BMI) is used widely as an indicator in general health. Determination of BMI using non-intrusive measurements are of interest and recent advancements in the availability of digital imaging sensors have paved the way for performing quick and automatic measurements. In this work, we c onsider automatic computation of BMI using correlation features from face images. We show that using face detection based facial fiducial points analysis provides good BMI prediction. Experimental results on comparing the correlation coefficients of facial ratios along with the colour feature has higher significance in BMI of a person.
Rapid visual categorization is not guided by early salience-based selection

The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements. Within this paradigm, visual saliency is seen by many to have a specific role, namely that of early selection. Early selection is thought to enable very fast visual performance by limiting processing to only the most salient candidate portions of an image. This strategy has led to a plethora of saliency algorithms that have indeed improved processing time efficiency in machine algorithms, which in turn have strengthened the suggestion that human vision also employs a similar early selection strategy. However, at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision. How would the best of the current saliency models perform on the stimuli used by experimentalists who first provided evidence for this visual processing paradigm? Would the algorithms really provide correct candidate sub-images to enable fast categorization on those same images? Do humans really need this early selection for their impressive performance? Here, we report on a new series of tests of these questions whose results suggest that it is quite unlikely that such an early selection process has any role in human rapid visual categorization.
State2vec: Off-Policy Successor Features Approximators

A major challenge in reinforcement learning (RL) is the design of agents that are able to generalize across tasks that share common dynamics. A viable solution is meta-reinforcement learning, which identifies common structures among past tasks to be then generalized to new tasks (meta-test). In meta-training, the RL agent learns state representations that encode prior information from a set of tasks, used to generalize the value function approximation. This has been proposed in the literature as successor representation approximators. While promising, these methods do not generalize well across optimal policies, leading to sampling-inefficiency during meta-test phases. In this paper, we propose state2vec, an efficient and low-complexity framework for learning successor features which (i) generalize across policies, (ii) ensure sample-efficiency during meta-test. We extend the well known node2vec framework to learn state embeddings that account for the discounted future state transitions in RL. The proposed off-policy state2vec captures the geometry of the underlying state space, making good basis functions for linear value function approximation.
Performance Evaluation of WebGL and WebVR Apps in VR Environments

This paper presents an evaluation of devices and browsers performance when executing web applications in WebGL and WebVR. For this purpose, the measurement of several computational resources is performed to determine which technology optimizes hardware resources when working with this type of technologies. For the purpose of this study, an application has been developed in Unity 3D that provides the exact values that the computational resources consume. In this context objects and particle systems are used to give a stress behavior on hardware entities. As a result, it was determined that WebGL and WebVR have similar performances in executions on a PC while for WebVR mobile devices it has a remarkable performance compared to WebGL.
Enhancement Of Breast Cancer Diagnosis Accuracy With Deep Learning

Breast cancer is a highly fatal disease that is very prevalent among the female population.In this study, a new type of approach is proposed with the aim of improving the accuracy of breast cancer diagnosis, an important problem of our present time, by means of deep learning, one of the techniques in machine learning.In the designed method, the original data set of Breast Cancer Wisconsin being available in the Irvine Machine Learning Repository of University of California was used.Within this data set, there were 699 data consisting of 10 independent variables and 1 dependent variable.The complete utilization of the entire data set was ensured by correction of 16 incorrect data.A normalization process was applied in the data set for the purpose of reducing the time required for learning process.The used data set was allocated as 80% for training, 10% for validation, and 10% for testing.An artificial neural network was designed for the deep learning model.The neural network was set up of a total of 5 layers which were an input layer with 10 neurons, 3 hidden layers with 1000 neurons for each layer, and an output layer with 3 neurons.The software, developed for implementation was written by using Spyder which is an interactive development environment for Python programming language.In addition, Keras neural network API was used.The performance of the model was evaluated with Confusion Matrix and ROC (Receiver Operating Characteristic) analysis.According to the test data obtained at the end of the training, it was observed that the implemented model provided successful results.It is considered that the proposed method will contribute to the improvement of breast cancer diagnosis accuracy.
Signal Combination for Language Identification

Google's multilingual speech recognition system combines low-level acoustic signals with language-specific recognizer signals to better predict the language of an utterance. This paper presents our experience with different signal combination methods to improve overall language identification accuracy. We compare the performance of a lattice-based ensemble model and a deep neural network model to combine signals from recognizers with that of a baseline that only uses low-level acoustic signals. Experimental results show that the deep neural network model outperforms the lattice-based ensemble model, and it reduced the error rate from 5.5% in the baseline to 4.3%, which is a 21.8% relative reduction.
Forecasting Measles Immunization Coverage Using ARIMA Model

This study aimed to find a model to forecast monthly measles immunization coverage using Autoregressive Integrated Moving Average (ARIMA).The monthly registered data for measles immunization coverage from January 2014 to December 2018 were used for the development of the model.The best model with the smallest Normalized Bayesian Information Criterion (BIC) of 8.673 is ARIMA (0, 1, 0).ARIMA (0, 1, 0) was used to forecast the monthly measles immunization coverage for the next 36 months from January 2018 to December 2020.The results obtained prove that this model can be used for forecasting future immunization coverage and will help decision-makers to establish strategies, priorities, and proper use of immunization resources.
Detecting gender differences in perception of emotion in crowdsourced data

Do men and women perceive emotions differently? Popular convictions place women as more emotionally perceptive than men. Empirical findings, however, remain inconclusive. Most prior studies focus on visual modalities. In addition, almost all of the studies are limited to experiments within controlled environments. Generalizability and scalability of these studies has not been sufficiently established. In this paper, we study the differences in perception of emotion between genders from speech data in the wild, annotated through crowdsourcing. While we limit ourselves to a single modality (i.e. speech), our framework is applicable to studies of emotion perception from all such loosely annotated data in general. Our paper addresses multiple serious challenges related to making statistically viable conclusions from crowdsourced data. Overall, the contributions of this paper are two fold: a reliable novel framework for perceptual studies from crowdsourced data; and the demonstration of statistically significant differences in speech-based emotion perception between genders.
Towards Kikamba Computational Grammar

The under-resourced Kikamba language has few language technology tools since the more efficient and popular data driven approaches for developing them suffer from data sparseness due to lack of digitized corpora.To address this challenge, we have developed a computational grammar for the Kikamba language within the multilingual Grammatical Framework (GF) toolkit.GF uses the Interlingua rule-based translation approach.To develop the grammar, we used the morphology driven strategy.Therefore, we first developed regular expressions for morphology inflection and thereafter developed the syntax rules.Evaluation of the grammar was done using one hundred sentences in both English and Kikamba languages.The results were an encouraging four n-gram BLEU score of 83.05% and the Position independent error rate (PER) of 10.96%.Finally, we have made a contribution to the language technology resources for Kikamba including multilingual machine translation, a morphology analyzer, a computational grammar which provides a platform for development of multilingual applications and the ability to generate a variety of bilingual corpora for Kikamba for all languages currently defined in GF, making it easier to experiment with data driven approaches.
The dynamics of indexical information in speech: Can recognizability be controlled by the speaker?

Human voices are individual and humans have elaborate skills in recognizing speakers by their voice, phenomena that are deeply rooted in the evolution of human behavior.To date, the mechanisms of speaker recognition are not well understood because of the high variability of the acoustic cues to a speaker's identity.We wondered what role the speaker plays in making his/her voice more or less well recognizable.While it is evident from the literature that humans can control vocal properties to enhance their intelligibility, it is unclear whether speakers can and/or do control vocal characteristics to be better recognizable and whether such control mechanisms play a role in the communication process.In this paper, we reviewed results from the literature supporting the view that speaker idiosyncratic information is dynamic and that humans have the ability to control how well they can be recognized.We suggest possible experimental setups by which the control over identity in voice can be tested and present pilot acoustic characteristics of speech that was produced to be either targeted at being (a) intelligible (clear speech) and (b) suitable for person recognition (identity marked speech).Results revealed that there is reason to believe that speakers apply different mechanisms when making their individuality identifiable as opposed to making their speech better understood.We discuss predictions that a control of recognizability and intelligibility has within major theories of speech perception.
Exploring the Perceptions of Applying Blockchain Technology in the Higher Education Institutes in the UAE

Based on the increasing demand for having a reliable method to verify student records in the education sector in the UAE, this research will attempt to get the perception of the managerial unit of higher education organizations in the UAE about the expected value of applying this technology.The study will test two main areas: student academic records and student campus life happiness.Mentioned about the methodology used.The research will finally suggest a possible way to adopt this technology, like spreading the awareness about the advantages of the Blockchain technology, providing support from the governmental organizations and creating the brainstorming & skills workshops that assist in understanding this new technology.These methods will help to grow up the technology and support its implementation process.Finally, evaluations should be conducted after the implementation to review and rate the success in reaching the main organizational goals.
The Decision Making Trial and Evaluation Laboratory (Dematel) and Analytic Network Process (ANP) for Learning Material Evaluation System

Evaluate the learning that will be given or given to diagnose success and failure in understanding teaching materials. Failure to provide learning can be caused by the use of inappropriate teaching or media methods, and difficult language to convey. By applying information technology in evaluating learning materials, a web-based evaluation system can be developed to conduct assessments and as a way to improve the quality of learning materials with certain criteria. So that the problem of failure in the learning process can be known based on the learning material used. This study integrates the DEMATEL and ANP methods that support decision making by evaluating lecturer learning material to students. Based on the problems and explanations described earlier, the use of the DEMATEL and ANP methods is expected to assist in evaluating learning materials with the aim of always trying to provide good learning materials based on the right factors or criteria. The results of making an evaluation system aim to assess the suitability of learning materials with the criteria used as further improvement efforts and to find out where the lack of success in learning. Thus it can be sustainable between the objectives and the final results of teaching.
Secured Cloud Storage Scheme Based On Blockchain

To achieve data security, Cloud Service Providers (CSPs) use cryptographic techniques to encrypt the data and disclose decryption keys to only authorized users. However, when data is shared amongst many users, the data management system should not only support access control, but also provide traceability of the different operations made on the sensitive data. Also data is duplicated to ensure the availability of the data. Unfortunately the amount of redundant copies of the same data leads to storage waste. In our paper we propose an architecture for data management based on Blockchain Principle to provide traceability of the updates on data while guaranteeing its security through a combination of hashing and encryption techniques. Our model will adapt the Blockchain technology to a centralized environment and supports a heterogeneous deduplication of redundant data in data centers. We evaluate the efficiency of the scheme on the basis of the time consumed for encryption and decryption of big or small data as well as the security level of the proposed model.
A Study of AI Agent Commitment in One Night Ultimate Werewolf with Human Players

Social deduction games are a genre of board games in which a group of players is secretly assigned roles and each player tries to determine the other players’ roles. However, some roles have an incentive to not be found, and the games typically allow players to lie freely. Playing such games is a challenging task for AI agents, because they need to not only determine the probability that each statement made by the other players is truthful, but also come up with convincing lies themselves. In this paper, we present AI agents designed to play one particular such game, One Night Ultimate Werewolf, with human players. We discuss the different deliberation strategies our agents use to determine what they should say, and when they should change their plan. To determine how these different deliberation strategies are perceived by human players, we performed an experiment in which participants played a Unity implementation of the game with each of the three deliberation strategies. We present the results of this experiment, which show that commitment to plans has a measurable effect on player perception and provide a trade-off between consistency and potential for high performance of the agent.
DFSMN-SAN with Persistent Memory Model for Automatic Speech Recognition

Self-attention networks (SAN) have been introduced into automatic speech recognition (ASR) and achieved state-of-the-art performance owing to its superior ability in capturing long term dependency. One of the key ingredients is the self-attention mechanism which can be effectively performed on the whole utterance level. In this paper, we try to investigate whether even more information beyond the whole utterance level can be exploited and beneficial. We propose to apply self-attention layer with augmented memory to ASR. Specifically, we first propose a variant model architecture which combines deep feed-forward sequential memory network (DFSMN) with self-attention layers to form a better baseline model compared with a purely self-attention network. Then, we propose and compare two kinds of additional memory structures added into self-attention layers. Experiments on large-scale LVCSR tasks show that on four individual test sets, the DFSMN-SAN architecture outperforms vanilla SAN encoder by 5% relatively in character error rate (CER). More importantly, the additional memory structure provides further 5% to 11% relative improvement in CER.
Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error

Bottlenecks of binary classification from positive and unlabeled data (PU classification) are the requirements that given unlabeled patterns are drawn from the same distribution as the test distribution, and the penalty of the false positive error is identical to the false negative error. However, such requirements are often not fulfilled in practice. In this paper, we generalize PU classification to the class prior shift and asymmetric error scenarios. Based on the analysis of the Bayes optimal classifier, we show that given a test class prior, PU classification under class prior shift is equivalent to PU classification with asymmetric error. Then, we propose two different frameworks to handle these problems, namely, a risk minimization framework and density ratio estimation framework. Finally, we demonstrate the effectiveness of the proposed frameworks through experiments.
Pengembangan Assesment Pembelajaran pada Nilai Karakter untuk Menghadapi Era Sustainable Development Goals

The purpose of research and development is to produce learning assessment products based on character values ​​that have validity, applicability, and legibility. Research and development methods use the OMalley &amp; Pierce model which is devoted to developing learning assessments, namely: building a team, setting goals, setting results targets, conducting staff development, collecting assessment examples, adjusting existing assessments or developing new, assessment trials. and review assessment. The results of the character aspect validation showed that the criteria were very valid and could be used without revision (93%), the results of the language aspect validation showed very valid criteria and could be used without revision (93.8%). The results of the applicability test showed very good can be used without revision (88.2%). Whereas the legibility test shows very interesting criteria (97.2%) according to the conversion of the validity level, that is almost the entire contents of the assessment book is easy to understand. This product only consists of each of the 4 character values ​​developed in each lesson, this development is arranged only on theme 7 grade IV SD.