Reviewerly/exHarmony huggingface dataset:
Automatic Speech Recognition for Real Time Systems

Automatic Speech Recognition (ASR) systems have proven to be a useful tool to perform various day to day operations when used along with systems like Personal AI Assistants. Various industries require ASR to be trained in their domains. Music on Demand (MoD), over IVR, is one such industry where the user interacts with the dialogue system to play music using voice commands only. Domain adaptation of the model is expected to perform well on this domain as systems trained on public datasets are very generic in nature and not very domain-specific. To train the ASR for MoD, we experiment with the HMM-based classical approach and DeepSpeech2 on Voxforge dataset. We then fine-tune the DeepSpeech2 model on MoD data. With very limited data and little finetuning of the model, we were able to achieve 14.727% Word Error Rate (WER).
Design Consideration of Malay Text Stemmer Using Structured Approach

Word stemmer (or text stemmer) is used to remove bound morphemes from derived words so that various morphological variants are mapped into common base forms. It is usually used as one of the preprocessing tools in text classification, text mining, and information retrieval tasks. Therefore, the design of an effective text stemmer is crucial for ensuring text stemming process maps morphological variants into correct base forms. This paper investigates the design consideration of an effective text stemmer from the perspective of the Malay language. These design considerations are based on current challenges faced by previous researchers in performing text stemming against Malay texts. By adopting these considerations, an effective text stemmer is expected to address common stemming errors and also, expected to produce promising stemming accuracy.
Minimax Estimation of Solutions of the First Order Linear Hyperbolic Systems with Uncertain Data

In this paper, we focus on optimal estimation of solutions of the Cauchy problem for the first order linear hyperbolic equation systems (or, more generally, estimation of values of some functionals on their solutions) under incomplete data.
Research on Font Design Tutoring System Based on Mobile Devices

This paper develops a mobile device-based font design teaching system for practical teaching of font design courses. The teaching innovation of the system is through the interactive teaching methods, learning data acquisition methods, and personal ability of three aspects to solve the standard font transfer training that does not reach the designated position. The analysis of characteristics of stroke inaccuracy is the teaching focus to conquer the teaching difficulty of the unbalanced type design, and reform the traditional teaching to resolve the current situation of poor student learning effect. It creates the corresponding location for the training of students' creative thinking and innovates the teaching process. To evaluate the teaching effect of this teaching system and its influence on students' innovative thinking training, we conducted a teaching pilot experiment. The experimental results show that the typeface design teaching system based on mobile devices can improve students' learning effect, promote the completion of teaching objectives, and cultivate students' creative thinking ability.
Primary School Teachers and Learning Media in the Era of Education 4.0

Abstract Education era 4.0 demands teachers to be more competent since the nation’s progress is determined by education quality, particularly in the primary education level. However, education in Indonesia, especially in Eastern Indonesia, is very apprehensive due to limited facilities and infrastructure as one of the factors that trigger the low quality of education and human resources. The information technology utilization, specifically computer applications for primary school teachers, has not yet run well and equal. Other educational levels that still implement conventional learning that is no longer able to fulfil educational needs for complex societies require innovations and media to address this issue. A preliminary survey in several primary schools in Gorontalo revealed that teachers wanted to use computer applications as the learning media. The training of utilizing computer applications for primary school teachers is not the only solution to improve students’ comprehension about learning topics. A good cooperation between related institutions or agencies is required to produce teachers who understand technology in education era 4.0. Higher education institutions that will produce quality teachers should arrange a course curriculum that highly influences students as the teacher candidate. School members should also provide supporting facilities for an effective learning process.
An Image Thresholding Method Based on Differential Evolution Algorithm and Genetic Algorithm

Thresholding is the simplest but most effecttive segmentation technique for image analysis. However, the computational complexity increases exponentially with the increase of threshold number in order to seek the most appropriate threshold values. Therefore, stochastic optimization algorithm are often used to overcome excessive computational problems, but the single optimization algorithm often falls into the local optimum. In general, hybrid algorithm is able to produce better performance. As a result, a parallel coupled mode(DE_GA in brief) of differential evolution algorithm (DE) and genetic algorithm (GA) is proposed for solving multi-threshold problem and The maximum variance is used as the fitness function. The experimental result displays that compared with a single algorithm, the results of the hybrid algorithm are relatively stable, which means that the parallel coupled DE_GA algorithm combined with Otsu might be an effect and practical image segmentation method.
Mixed Noise Reduction in Hyperspectral Imagery

In this paper, a hyperspectral mixed noise reduction technique is proposed called HyMiR. We assume that hyperspectral images include Gaussian and sparse noise types. First, the Gaussian noise is removed using a recently-developed method called hyperspectral restoration (HyRes). Then, the sparse noise is reduced by proposing a sparse noise removal technique. Experimental results confirm improvements in terms of signal to noise ratio and spectral angle distance. In addition, visual improvements can be clearly observed in the case of real dataset experiments.
A Sentiment Analysis Model Based on Attention Mechanism and Compound Model

The following topics are dealt with: optimisation; convolutional neural nets; object detection; learning (artificial intelligence); road traffic; ships; natural language processing; mobile robots; Internet; road safety.
A Multitenant Container Platform with OKD, Harbor Registry and ELK

This paper summarizes the open container [2] journey of the University of Oslo’s Center of Information technology, (Division for Infrastructure). It describes the background for adopting containers in the first place, the pitfalls of early attempts, the learning that was obtained from stepping into those pitfalls, how they were mended and some thoughts about future direction for container usage. Challenges regarding organizational aspects and increased demand for rapid delivery, combined with the established expectations of security and stability, is also described in relation to container technology. It distills the findings and explains the rationale behind the chosen direction of adapting Openshift community Distribution of Kubernetes (OKD) [1] as our main container platform for long running core services, and how it was adapted to best integrate it with existing automation, monitoring and logging: Elasticsearch, Logstash and Kibana (ELK).
The application of Research Based Learning Model in electric power system protection course

The learning model should be adapted to the learning needs of today and the future. The achievement of student learning in each competency courses one of which depends the learning model used. Among the models of learning that develop current that brings the students can develop their ability in mastering the learning material is Research Based Learning (RBL) model. This model focuses on the students as learning resources that integrate research in learning. This research aims to assess the effectiveness of student learning on courses of electric power system protection after used RBL model. This study applies the nine-step RBL model in courses of electric power system protection consisting of identifying research topic, objectives and research question, research tools both data collection tools and intervention tools, applying theory, research methodology and design, data analysis, results and discussion, recommendation, and strengthen of the research. After using the RBL model in learning then an increase in measurable student learning outcomes in the assessment of the effectiveness of formative and summative evaluation.
Soundex Algorithm for Hindi Language Names

In the current period of extensive globalization where people all over the globe are beginning to have access to Internet and information, software companies and major tech conglomerates and starting to look forward to localization and internationalization (i18n) to better acclimatize their material to the local populace for their better understanding. In this scenario, the young and upcoming demographic of India possesses a limitless potential for development on both the local and international levels. In such cases, we need to have to create a cohesive structure where we can sort and match the different components of a local language to its correct set, according to a pre-governed paradigm where the possibility of error is reduced through a preset database containing a large number of entries to match from. In this research work, we have used the Soundex algorithm to match similar-sounding words from an user’s entry to the entries in the database. We have started with Hindi names which are entered in a form and then matched to their respective Soundex codes in the database, thus generating a correct match.
Regression with Uncertainty Quantification in Large Scale Complex Data

While several methods for predicting uncertainty on deep networks have been recently proposed, they do not always readily translate to large and complex datasets without significant overhead. In this paper we utilize a special instance of the Mixture Density Networks (MDNs) to produce an elegant and compact approach to quantity uncertainty in regression problems. When applied to standard regression benchmark datasets, we show an improvement in predictive log-likelihood and root-mean-square-error when compared to existing state-of-the-art methods. We demonstrate the efficacy and practical usefulness of the method for (i) predicting future stock prices from stochastic, highly volatile time-series data; (ii) anomaly detection in real-life highly complex video segments; and (iii) the task of age estimation and data cleansing on the challenging IMDb-Wiki dataset of half a million face images.
Corporate Finance in the New Business Ecosystem in the Digital Age

In the digital age of continuous technological evolution, business ecosystems are being renovated. This study aims to explore the impacts of blockchain technology—a foundationally innovative technology—which is reshaping and even revolutionizing corporate finance. This underlying technology in the heart of all financial innovations has a great disruptive potential for the financial sector. In this context, it is deeply focused on how corporate finance is being transformed by blockchain technology and its extensions (distributed ledgers and smart contracts) and what the reflections will be on corporate governance, capital markets, corporate voting, and accounting matters in corporate finance.
Marking Mechanism in Sequence-to-sequence Model for Mapping Language to Logical Form

Semantic parsing in Natural language processing (NLP) is a challenging task, which has been studied for many years. The main purpose is to model the language as a logical form like a machine translation task. Recently, an approach which uses a Neural network with Sequence to sequence model (Seq2seq) has achieved positive results. However, there are many challenges which have not been solved thoroughly yet, especially in the problem of rare words. Rare words in a natural sentence are usually the name of an object, a place or number, time, etc. Although these words are very various and difficult for the model to capture meaning, it holds a key information role in human communication (for example: name all the rivers in colorado ?). There are some methods to solve this problem such as using Attention or using Copy mechanism. However, these methods still difficult to copy phrase rare words, especially in case these phrases are variable in size. This paper proposes a novel approach to solve this problem, namely Marking mechanism in Seq2seq. The main idea is to label special words which are rare-words in a sentence by the encoder (marking step) and the decoder represents the logical form based on those labels (transforming step). Our experiments demonstrate that this approach works effectively, achieved a competitive result with old methods on all 3 datasets Geo, Atis, Jobs and special outperformed on our Artificial dataset.
Lightweight Efficient Simeck32/64 Crypto-Core Designs and Implementations, for IoT Security

IoT is a rapidly expanding environment. More and more devices, from the most high-end smartphones, to the most modest environmental sensors, already exhibit or acquire the ability to communicate with each other and be parts of networks. Connectivity is mainly accomplished through wireless communications and privacy is the way to go, regarding security over the air. A common approach dictates the incorporation of a crypto-core inside the transceiver. However, a privacy implementation must take into account several restrictions, imposed by the constrained resources of a small, power efficient device. For this purpose, an incorporation of a hardware implemented lightweight Simeck32/64 block cipher for IEEE 802.15.4 transceiver was proposed. In this work, we examine the FPGA implementation cost of such an approach, and we propose newly designed and efficient implementations of the proposed Simeck32/64 Crypto-Core. Our implementations achieve great improvement in throughput values, with a reasonable increase in hardware area. Furthermore, modifications of these approaches, exploiting the special functional blocks of a Xilinx FPGA, namely BRAM and DSP blocks, are implemented and the achieved results are evaluated. Additionally, the effects of applying the DSP multi-pumping methodology on the aforementioned implementations are also examined.
The Design of Educational Tool for Jury's Stability Test

Stability analysis in systems is a very important topic. Many methods are available in this area. One of these is the Jury&amp;apos;s stability test. In this study, a software tool that analyzes the stability of user defined systems according to Jury&amp;apos;s stability test has been developed. Stability of the linear time-invariant discrete-time systems can be realized step by step (Jury table, stability test conditions, etc.) easily and effectively according to this criterion with the software tool which has user-friendly interface, including the topic description and can be used for educational purposes.
Image Compression Technique Based on Some Principal Components Periodicity

In this chapter, the almost periodicity of the first principal components is used to carry out the reconstruction of images. First, the principal component analysis technique is applied to an image. Then, the periodicity of its principal components is analyzed. Next, this periodicity is used to build periodic vectors of the same length of the original principal components, and finally, these periodic vectors are used to reconstruct the original image. The proposed method was compared against the JPEG (Joint Photographic Experts Group) compression technique. The mean square error and peak signal-to-noise ratio were used to perform the above-mentioned comparison. The experimental results showed that the proposed method performed better than JPEG, when the original image was reconstructed using the principal components modified by periodicity.
Design Of Server Performance Monitoring Application Integrated Administration Service System In Electronic Engineering Department

Abstract The objective of this study is to make an application design to monitor server performance that can be used to monitor and inform server performance to administrators. The implementation of this server performance monitoring application is carried out on the Integrated Administrative Service System server of the Electronics Engineering Department, Faculty of Engineering, Padang State University. Server performance monitoring application built using the waterfall method. Making the application is also supported by the Script Shell programming language in retrieving information from the server, server information generated by the SNMP and SNORT applications and then reported to the administrator by the Sendemail and Gammu applications. The server performance monitoring application website is built using the Laravel framework, the LTE Admin template and the Canvas JS chart. This server performance monitoring application is able to monitor, recap and report server performance. Application reporting is done using e-mail and sms media, if a problem occurs on the server. So that the application of server performance monitoring applications on the server will be able to help the work of administrators to maintain the availability of a reliable Integrated Administrative Service System in the Electronics Engineering Department.
Learnable Gabor Convolutional Networks

Commonly used convolutional operation does not have the ability to learn invariant information of images. However, some handcrafted image feature extractors, like Gabor wavelets, are robust to object's scale and orientation transformations. Hence, how to combine Gabor filters with convolutional kernels for image feature extraction is an interesting and urgent issue in recent research of image representation learning using deep convolutional neural networks (DCNNs). In this paper, we propose a new method, named learnable Gabor convolutional networks (LGCNs), to combine the Gabor filters and convolutional kernels together to form the Gabor convolutional filters (GCFs) for invariant information learning of images. The scale and orientation parameters in the Gabor function can be learned simultaneously with other parameters during the networks' training. Experimental results show that LGCNs perform better than the corresponding DCNNs and other related methods in image classification tasks.
An Improved Two Factor User Authentication Framework Based on CAPTCHA and Visual Secret Sharing

To prevent unauthorized access to adversaries, strong authentication scheme is a vital security requirement in client-server inter-networking systems. These schemes must verify the legitimacy of such users in real-time environments and establish a dynamic session key fur subsequent communication. Of late, T. H. Chen and J. C. Huang proposed a two-factor authentication framework claiming that the scheme is secure against most of the existing attacks. However we have shown that Chen and Huang scheme have many critical weaknesses in real-time environments. The scheme is prone to man in the middle attack and information leakage attack. Furthermore, the scheme does not provide two essential security services such user anonymity and session key establishment. In this paper, we present an enhanced user participating authenticating scheme which overcomes all the weaknesses of Chen et al.'s scheme and provide most of the essential security features.
Learning Transition Statistics in Networks of Interacting Agents

Studying the decision-making of agents can reveal group behavior and internal lines of influence. We work with systems of interacting agents, where the decision-making of each agent is affected by their neighbors within some graph structure. As agents make choices, the stochastic transitions between chosen group actions can be learned, and thus the group behavior can be characterized and predicted. We express each element of the transition matrix P as a product of factors that depends on the agent neighborhood structure and leading to a separable estimator for the unknown p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ij</sub> of interest. This enables us to find a maximum likelihood estimator (MLE) for each factor and thus effectively estimate each p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ij</sub> with reduced complexity. We derive analytical concentration bounds for the error rates of this approach and demonstrate it on data sets.
Review: Data Security Models Developed by Blockchain Technology for Different Business Domains

This study investigates a range of data security models developed to achieve data security using blockchain technology for different industrial domains. Current industries utilize data-driven mechanisms for decision making with concern focussed on ensuring data security. Blockchain has the potential to secure data by integrating different information systems since data is decentralized, encrypted and validated by the whole network. This study includes an analysis of blockchain security models using data extracted from 30 peer-reviewed scientific publications over two years (2017-2019). This study analyzed three components of the publications, including the process involved in securing data, the stage of development for securing data and in which industry a model is best applied. Results of the research show that the majority of articles (51.11%) cited Blockchain as a key feature of data security for improvising the data sharing process in industries. This study also finds that the stage of implementation most commonly featured is the proposal stage with potential architectures yet to be implemented (30%). Finally, this study shows that models are applied in industrial domains such as enterprises using data analytics, finance, Internet of Things (IoT), healthcare, education, and cloud service providers. This study finds that security models are most often applied to industries and supply chain management models (28.13%). It is recommended that industry professionals conduct further research to customize the data security models in their own domain. This study gives clear guidelines to researchers of suitable frameworks, processes and consensus mechanism to utilize Blockchain in Industries for data security.