Reviewerly/exHarmony huggingface dataset:
A Process of Developing an ASR System for Malay and Tamil Languages

The fundamental way of interacting with people was speech.  It is used to express our message, thoughts and feelings to others.  Languages like Malay and Tamil was the process to convert speech into acoustic message.  This paper highlights the instinctive recognition of speech for Malay and Tamil language.  This instinctive process was known an Automatic Speech Recognition method.  An important issue of Automatic Speech Recognition was analyzed in this study.  For various under resourced languages, this Automatic Speech Recognition method was a correct starting point for everyone who interested in this topic.  This research must be clear and many approaches and issues are applied to speech technique to analyze this method.
Saliency Prediction on Omnidirectional Images with Brain-Like Shallow Neural Network

Deep feedforward convolutional neural networks (CNNs) perform well in the saliency prediction of omnidirectional images (ODIs), and have become the leading class of candidate models of the visual processing mechanism in the primate ventral stream. These CNNs have evolved from shallow network architecture to extremely deep and branching architecture to achieve superb performance in various vision tasks, yet it is unclear how brain-like they are. In particular, these deep feedforward CNNs are difficult to mapping to ventral stream structure of the brain visual system due to their vast number of layers and missing biologically-important connections, such as recurrence. To tackle this issue, some brain-like shallow neural networks are introduced. In this paper, we propose a novel brain-like network model for saliency prediction of head fixations on ODIs. Specifically, our proposed model consists of three modules: a CORnet-S module, a template feature extraction module and a ranking attention module (RAM). The CORnet-S module is a lightweight artificial neural network (ANN) with four anatomically mapped areas (V1, V2, V4 and IT) and it can simulate the visual processing mechanism of ventral visual stream in the human brain. The template features extraction module is introduced to extract attention maps of ODIs and provide guidance for the feature ranking in the following RAM module. The RAM module is used to rank and select features that are important for fine-grained saliency prediction. Extensive experiments have validated the effectiveness of the proposed model in predicting saliency maps of ODIs, and the proposed model outperforms other state-of-the-art methods with similar scale.
Cloud-edge coupling to mitigate execution failures

This paper examines the doability of cloud-edge coupling to mitigate execution failures and hence, achieve business process continuity. These failures are the result of disruptions that impact the cycles of consuming cloud resources and/or edge resources. Cloud/Edge resources are subject to restrictions like limitedness and non-shareability that increase the complexity of resuming execution operations to the extent that some of these operations could be halted, which means failures. To mitigate failures, cloud and edge resources are synchronized using messages allowing proper consumption of these resources. A Microsoft Azure-based testbed simulating cloud-edge coupling is also presented in the paper.
An Energy-Efficient Deep Neural Network Training Processor with Bit-Slice-Level Reconfigurability and Sparsity Exploitation

This paper presents an energy-efficient deep neural network (DNN) training processor through the four key features: 1) Layer-wise Adaptive bit-Precision Scaling (LAPS) with 2) In-Out Slice Skipping (IOSS) core, 3) double-buffered Reconfigurable Accumulation Network (RAN), 4) momentum-ADAM unified OPTimizer Core (OPTC). Thanks to the bit-slice-level scalability and zero-slice skipping, it shows 5.9 x higher energy-efficiency compared with the state-of-the-art on-chip-learning processor (OCLPs).
A Comparison of CNN and Conventional Descriptors for Word Spotting Approach: Application to Handwritten Document Image Retrieval

Natural images are easier to represent in feature space than textual images due to the reduced complexity and thus do not require greater learning capacity. The visual information representation is an important step in content-based image retrieval (CBIR) systems, used for searching relevant visual information in large image datasets. The extraction of discriminant features can be carried out using two approaches. Manual (conventional CBIR), based on preselected features (colors, shapes, or textures) and automatic (modern CBIR) based on auto-extracted features using deep learning models. This second approach is more robust to the complexity relative to textual images, which require a deep representation reaching the semantics of text in the image. DIRS (Document Image Retrieval Systems) are CBIR systems related to documents images that propose a set of efficient word-spotting techniques, such as the interest points based techniques, which offer an effective local image representation. This paper presents an overview of existing word retrieval techniques and a comparison of our two proposed word-spotting approaches (interest points and CNN description), applied on handwritten documents. The results obtained on degraded and old Bentham datasets are compared with those of the literature.
Network Embedding via Deep Prediction Model

Network-structured data becomes ubiquitous in daily life and is growing at a rapid pace. It presents great challenges to feature engineering due to the high non-linearity and sparsity of the data. The local and global structure of the real-world networks can be reflected by dynamical transfer behaviors among nodes. This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models. We first design a degree-weight biased random walk model to capture the transfer behaviors on the network. Then a deep network embedding method is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into conventional deep prediction models, including Long Short-Term Memory Network and Recurrent Neural Network, to utilize the sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various datasets including social networks, citation networks, biomedical network, collaboration network and language network. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization, classification, reconstruction and link recovery, and achieve promising performance compared with state-of-the-arts.
The Influence of English Song and Joox Application toward Students’ Pronunciation (A True Experimental Study at the Eighth Grade of SMPN 6 Kota Serang)

The aim of this research was to find out the influence of English song and Joox Application toward students’ pronunciation at the eighth grade of SMPN 6 Kota Serang. The researcher used quantitative research by applying true experimental design for conducting the research. The population of this research was the eighth grade students of SMPN 6 Kota Serang.The groups were classified into Experimental Group and Control Group. Each group consists of 30 students. Pre-test was given to the groups at the beginning of the phase. Then treatment was given only to experimental group for three meetings. Post-test was given to the groups. After having post test, close-ended questionnaire was given to measure students’ response after using the treatment. In testing the hypothesis the researcher used t-test as formulas to know the influence of English song technique toward students’ pronunciation at the eighth grade of SMPN 6 Kota Serang. The calculation showed the result of  is higher than  (4.26 ≥ 2.00) with the significance 0.05 (5%). Based on students’ response in questionnaire, most of students answered strongly agree (35.9%) and agree (47.2%), it means that most of students have positive responds after receiving treatments using Joox application. It can be concluded that there is a significant influence of English song and Joox application toward students’ pronunciation at the eighth grade of SMPN 6 Kota Serang.
Attack of Two Certificateless Aggregate Signature Schemes Based on VANET

The Vehicular Ad-hoc Network is the fundamental of smart transportation system in the future. The rapid development of wireless communication technology makes it possible to communicate between vehicles and vehicles, between vehicles and roadside infrastructures. Wang et al. and Hong et al. proposed a certificateless aggregate signature algorithm for Vehicular Ad-hoc Network respectively, and proved the algorithm to be safe under the random oracle model. In this paper, these two programs are analyzed in depth, and it is found that the two solutions both have security problems. The scheme of Wang et al. cannot resist the attack of type II attacker, while the scheme of Hong et al. can neither resist the attack of type I attacker nor the attack of type II attacker. This paper details the attack process of the two schemes, analyzes reasons for the signature forgery of each algorithm and gives improvement measures. This work provides an idea for designing a more secure and efficient certificateless aggregate signature scheme based on Vehicular Ad-hoc Network.
Tackling the Imbalanced Data in Software Maintainability Prediction Using Ensembles for Class Imbalance Problem

Early prediction of software maintainability is a potential solution to save the cost of software maintenance which is almost 60–70% of the software development cost. Various machine learning (ML) techniques have been investigated in literature to predict software maintainability. The performance of well-established ML techniques degrades considerably if the training dataset contains irregularities in the form of data skewness or class imbalance. This study empirically investigates the performance of two types of ensembles for class problem, namely Bagging-based ensembles and Boosting-based ensembles. The study also compares the predictive performance of proposed ensembles with classic ensembles: Bagging and AdaBoost. The results of the empirical analysis favor the use of proposed ensembles to develop effective Software Maintainability Prediction (SMP) models from imbalanced datasets.
Investigation of the spatial variation of sediments heavy metals along the Nun River using kriging interpolation technique

This study employs geospatial statistical technique to assess the spatial distribution of heavy metals along the Nun River. Core sediment samples were collected from relatively undisturbed areas (twenty-five different stations) using Uwitec Triple sediment cutter. The rectangular coordinates of the sediment sample location were determined with the aid of Germin handheld GPS receiver. The concentrations of cadmium, lead chromium and zinc present in the sediments was determined with the aid of an atomic absorption spectrophotometer. For geospatial analysis, five semi-variogram models (stable, circular, spherical, exponential and K-Bessel) were fitted for each of the four critical parameters (heavy metals). In addition, four goodness-of-fit statistics (mean square error, root mean square error, root mean square standardized error and average standard error) were utilized to decide the most suitable model used to develop the final prediction map for each parameter. From the results obtained, it was observed that; regions with red color codes signify higher concentrations of cadmium, lead, chromium and zinc. Further assessment of the results showed that Otuan, Obeleli, Angiama, Odobio, Kasama, Akedda and Akele experienced high concentration of cadmium while Tombia, Ewoi, Abilabio, Agudama and Yenikpa experienced high concentration of lead.
Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study

Deep learning (DL) frameworks have been extensively designed, implemented, and used in software projects across many domains. However, due to the lack of knowledge or information, time pressure, complex context, etc., various uncertainties emerge during the development, leading to assumptions made in DL frameworks. Though not all the assumptions are negative to the frameworks, being unaware of certain assumptions can result in critical problems (e.g., system vulnerability and failures, inconsistencies, and increased cost). As the first step of addressing the critical problems, there is a need to explore and understand the assumptions made in DL frameworks. To this end, we conducted an exploratory study to understand self-claimed assumptions (SCAs) about their distribution, classification, and impacts using code comments from nine popular DL framework projects on GitHub. The results are that: (1) 3,084 SCAs are scattered across 1,775 files in the nine DL frameworks, ranging from 1,460 (TensorFlow) to 8 (Keras) SCAs. (2) There are four types of validity of SCAs: Valid SCA, Invalid SCA, Conditional SCA, and Unknown SCA, and four types of SCAs based on their content: Configuration and Context SCA, Design SCA, Tensor and Variable SCA, and Miscellaneous SCA. (3) Both valid and invalid SCAs may have an impact within a specific scope (e.g., in a function) on the DL frameworks. Certain technical debt is induced when making SCAs. There are source code written and decisions made based on SCAs. This is the first study on investigating SCAs in DL frameworks, which helps researchers and practitioners to get a comprehensive understanding on the assumptions made. We also provide the first dataset of SCAs for further research and practice in this area.
An application performance optimization model of mobile augmented reality based on hd restoration

In order to realize the energy consumption of video transmission in mobile augmented reality, a content adaptive image uninstall model is proposed. Taking into account the impact of limited uplink bandwidth resources on transmission latency, the model based on the complexity of shooting content and user expectations, low-resolution video sampling and upload to the cloud, and in the cloud using ultra-resolution algorithm for high-definition restoration of key frame images to ensure the accuracy of target recognition and tracking, and provide users with a high-quality viewing experience. In this paper, 289 videos are analyzed for complexity, machine learning models are constructed to select suitable image sample configurations, and low-resolution images are uploaded to reduce upload delays. The experimental results show that the model reduces upload by 77% to 82%.
End-to-End Learning for Autonomous Driving in Secured Smart Cities

Autonomous driving is an indispensable component in the future secured smart cities. The benefits of autonomous driving are numerous, including improving road traffic safety, reducing traffic-related economic loss, reducing traffic congestion, and enabling new vehicle applications. With the recent development of deep learning and sensor technologies, the autonomous vehicle becomes a highly complex networked system that heavily relies on sensor data to perceive the surrounding environment and make the correct decision. Such a system inevitably exposes a large attack surface and multiple attacks have been developed. It is thus crucial to protect the data and use secured machine learning algorithms to prevent, detect, and mitigate these attacks while keeping the autonomous driving system low-cost, low-latency, high-accuracy, and high-reliability. The proposed chapter presents an overview of research in autonomous driving, focusing on using end-to-end deep learning technologies for enhancing performance and security in autonomous vehicles in dynamic, adversarial environments. The chapter introduces autonomous driving paradigms, associated deep-learning methods for end-to-end learning, and the defenses against adversarial attacks. A new method utilizing temporal information for secured autonomous driving is presented; its design and implementation using CNN-LSTM include defenses against adversarial attacks. Experiments and performance demonstrating its success prediction rates are illustrated. Future research directions are described, which include both improving the autonomous driving system and enhancing its security defenses.
Activating Factors Of The Community Based Education Network By Region Type For Improving Young Children’s Character

The purpose of this study was to examine the differences of current states and analyze the importance-performance of activating factors in the community based education network (CEN) for improving young children's character by region type. The subjects in this study were 344 teachers of kindergartens and child care centers located at 13 provinces in Korea. The questionnaire was composed of the perception of the purpose of young children’s character education through the CEN, current states of community connected & collaborative education activities, and the importance and performance of the activating factors of CEN. The results of examining current states of CEN showed that In terms of 'utilization of human resources in the community’ and 'use of institutions and facilities', the rural town found to be lower than big cities and small & medium sized cities. Importance-Performance Analysis results according to region types show that big cities and small and medium-sized cities need intensive improvement efforts in aspects of rich human and material resources of the community and professional education staff in community organizations. In rural towns, it is found that 'leadership' are insufficient. These results can be used as basic data for cultivating the character of young children and the direction to activate the CEN.
The application research of Da Meng Database in the reform project of localization of government information reporting system

In recent years, the electronic service system has been greatly developed, for the government units at all levels of the country for this system also has an urgent need, more need for a home-based e-government system to meet the work overture. First of all, government information security and technology should be autonomously and controllable. Therefore, this paper selects Da Meng, a domestic database, to build an Intranet business platform. Secondly, this article research object for the provincial party committee office in the process of specific functional and non-functional requirements, specific research focus for this project development framework, the framework of database platform for the domestic database of dream, several parts together: B/S architecture model, for the presentation layer, and the application of simple logic processing with front JSP, complex business function for each module, adopt the back-end to implement the Translator layer. And, in order to guarantee the safety of the user login, the current relatively popular USERKEY authentication encryption technology at home and abroad combined with C/S architecture model, makes the Intranet security clients can increase user personalized functions such as authentication, message to remind, and in terms of the security of network activity, PKI technology and control characters. Finally, the design and development of Intranet business platform are completed with these open frameworks.
A model of detecting cyberattacks on cyber-physical systems of critical infrastructure nuclear facilities

The article presents an approach to classification that will help to identify specific areas of possible cyberattacks and significantly narrow the range of targets vulnerable to attackers. Criteria are considered that allow one to classify cyber-physical systems (attack targets) that are part of nuclear facilities. It is proposed to enter into the basis of the classification such concepts as an object, an attacking and attacking action. The novelty in this work lies in the development of a classification that can be used most effectively to assess the vulnerabilities of systems of critical infrastructure facilities.
Modeling and security analysis of CBTC signal failure scenario based on ScOLA

Abstract For the entire phase of the design, manufacture and operation of the Communication Base Train Control (Communication Base Train Control, CBTC) signal system, safety is the primary issue that needs to be considered. This article introduces ScOLA’s modeling language, combined with the situation of the wireless loss fault of the vehicle-mounted signal equipment, and carries out a formal description and step-by-step simulation of the system. It further illustrates that the application of the ScOLA modeling language can perform the safety analysis of the CBTC signal system faster and more accurately.
Determining the most relevant input parameter set by using extreme learning machine

In this work, Extreme Learning Machine (ELM) algorithm is used to estimate the GDP per capita. The amount of electricity production, from four different sources, is chosen as input parameters. To find out the most relevant input data for a reasonable estimation of GDP, different sources introduced separately to ELM. By following the coefficient of determination of estimation, by trial and error, results are obtained. The residuals are also given to show that model perform well. Renewable energy sources produce the best results in the estimation of GDP.
Determination Price Volatility of Bitcoin with Autoregressive Conditional Heteroscedasticiy Models

Purpose: The purpose of this research is to analyze the price movements of bitcoin, which has become a new phenomenon in financial markets since 2009, the first year of its release, and can be defined as virtual money or crypto money, to be seen as a financial investment tool. Design/Methodology: In the study, volatility, return behavior and reliability as a financial investment tool are examined with autoregressive Conditional Variable Variance modeling. In this context, symmetrical and asymmetrical ARCH models were used. Findings: As a result of the analysis; it has been found that it has an asymmetric effect in the first period for the bitcoin return series examined with symmetric and asymmetric ARCH models. In addition, it has been determined that shocks occurring in the bitcoin return series according to the half-life criteria are exposed to the volatility effect for more than 30 days in each period. It has been determined that bitcoin, which is examined by periods, has higher volatility in its first years. Limitations: The volatility of bitcoin, which has become a new phenomenon in financial markets today, can be defined as virtual money or crypto money, has been analyzed. Originality/Value: In fact, there are many virtual currencies or cryptocurrencies traded in the market. However, among many virtual currencies, bitcoin is the most known and the most market volume. Analyzing the price movements of bitcoin, which has started to be seen as a financial investment tool, is of great importance in the framework of reliability. The examination made in this respect constitutes the original value of the research.
Feature Point Matching Algorithm Based on Local Relative Velocity Consensus

Feature matching is still a crucial and challenging problem in image processing. In this paper, a novel feature matching algorithm based on local relative velocity consensus (LRVC) is proposed to find two accurate matched feature points. To remove the outliers accurately and robustly, the relative velocity between the putative matches and their corresponding neighbors are exploited. Based on the consensus of relative velocity and the consensus of neighborhood elements, the similarity of each putative matches is evaluated. With a two iteration outlier removal strategy, the feature points are matched accurately and robustly. In the experiment, VGG dataset are used to verify the accuracy and robustness of the proposed method LRVC. The matching results indicate that our method is superior to three other classic feature matching methods.