Reviewerly/exHarmony huggingface dataset:
A New Corpus of Elderly Japanese Speech for Acoustic Modeling, and a Preliminary Investigation of Dialect-Dependent Speech Recognition

We have constructed a new speech data corpus consisting of the utterances of 221 elderly Japanese people (average age: 79.2) with the aim of improving the accuracy of automatic speech recognition (ASR) for the elderly. ASR is a beneficial modality for people with impaired vision or limited hand movement, including the elderly. However, speech recognition systems using standard recognition models, especially acoustic models, have been unable to achieve satisfactory performance for the elderly. Thus, creating more accurate acoustic models of the speech of elderly users is essential for improving speech recognition for the elderly. Using our new corpus, which includes the speech of elderly people living in three regions of Japan, we conducted speech recognition experiments using a variety of DNN-HNN acoustic models. As training data for our acoustic models, we examined whether a standard adult Japanese speech corpus (JNAS), an elderly speech corpus (S-JNAS) or a spontaneous speech corpus (CSJ) was most suitable, and whether or not adaptation to the dialect of each region improved recognition results. We adapted each of our three acoustic models to all of our speech data, and then re-adapt them using speech from each region. Without adaptation, the best recognition results were obtained when using the S-JNAS trained acoustic models (total corpus: 21.85% Word Error Rate). However, after adaptation of our acoustic models to our entire corpus, the CSJ trained models achieved the lowest WERs (entire corpus: 17.42%). Moreover, after readaptation to each regional dialect, the CSJ trained acoustic model with adaptation to regional speech data showed tendencies of improved recognition rates. We plan to collect more utterances from all over Japan, so that our corpus can be used as a key resource for elderly speech recognition in Japanese. We also hope to achieve further improvement in recognition performance for elderly speech.
Object Detection Based Security System Using Machine learning algorthim and Raspberry Pi

Conventional security systems that use surveillance cameras to monitor the property lacks the ability to notify the security administrator in the event of trespassing. A security camera when used along with a digital video recorder (DVR) is only effective as a source to gather evidence unless the video feed is constantly being monitored by a dedicated personnel. This paper discusses the implementation of a cost effective, intelligent security system that overcomes drawbacks of conventional security cameras by utilizing a machine learning and Viola-Jones algorithm under image processing literature to identify trespassers and multiple object detection in real time. The paper presents the design and implementation details of the intelligent object detection based security system in two different computing environment, MATLAB and Python respectively using Raspberry Pi 3 B single board computer. The security system is capable of alerting the security administrator through email via internet while activating an alarm locally.
A Novel Approach Towards an Efficient Load Balancing Algorithm in Cloud Computing

The need of cloud computing (CC) technology is growing vastly, as it is easier and cheaper to deploy the large scale applications on cloud. Hence the system stability and user amusement is very important. To enact it, it is paramount to use load balancing algorithms (LBA). It acts in the medial of virtual machines (VM) and the data centers (DC) and makes the system dynamic. In the interest to perform load balancing many algorithms has been contemplated to minimize overall response time (ORT), data center processing time (DPT), processing cost. This article analyses the contraposition performances of three subsisting algorithms namely round robin (RR), equally spread current execution (ESCE) and the throttled algorithm (THR). On the basis of study of subsisting algorithms we have contemplated a new algorithm named as “ProposedDualIndex” (PDI) which performs better and overcome the issues of subsisting algorithms. On the thoroughfare to determine a new optimal LBA we maintain two tables for virtual machines (VMs). One is for available VM and other for busy VM by doing so we eliminate process of multiple scanning. Hence reduce the available VM searching time which eventually reduces processing time and cost. The simulation of contemplated scheme is done on cloud analyst tool and assessed on three metrics that are ORT, Datacenter processing time and processing cost. Specifically, the results show that PDI algorithm takes less time than the other three algorithms to access cloud data. The contemplated algorithm can be used for faster accessing of data on CC.
Rapid Heteronym Disambiguation for Text-to-Speech System

This paper is about the text analysis component for heteronym words for Malay Text-to-Speech (TTS) system. Heteronym is a homograph words which the meaning and the pronunciation differ according to the semantic of the sentence. There is only 12 unique heteronym in Malay. However, these words can be expanded depending on the morphology of the sentence. Malay language is also very rich with prefix and suffixes. Thus, one word can be expanded either to two unique words or more. For the list of 12 unique pair of words, we can expand the list to 98 unique words. Thus, there are greater variation possibility of enunciating a heteronym words. This research showed the importance of a study on heteronym words specifically in TTS case as well as what would be the best approach of handling heteronym ambiguity when only the sampled data is available but not tagged data of a particular target language.
The e-Learning Environment in Developing Countries: Case of Catholic University of Mozambique

This paper present the results of on study based on a questionnaire about the use of ICTs in the environment of e-Learning in Mozambique, having as study object the Catholic University of Mozambique (CUM). For this purpose, 600 forms were sent to the participants (students of CUM) through the Google form. We received back 203 questionnaires, wherein 186 of the sample was selected on a confidence-basis of 90% with an error margin of 5%; The results show that the majority of online students live in provincial capitals and in rural areas and often use ICTs, in daily life as well as for the academic activities. In order to accomplish a quality e-learning there should be taken into account, in addition to the education programmes, the issue of basic infrastructure such as the electric power, the internet access and speed of platforms and the possession of mobile devices connected to network and skills of the end users.
First Steps Toward Concealing the Traces Left by Reversible Image Data Hiding

Reversible image data hiding (RIDH) is a special class of data hiding techniques, where the host image can be perfectly reconstructed upon data extraction. Due to this reversibility property, RIDH has been widely adopted in many critical scenarios. However, almost all the existing methods focus on improving the capacity-distortion performance; and the hiding ability is ambiguously referred as the perceptual unawareness of a human observer. In this brief, we show that the prevalent RIDH framework, prediction error expansion histogram shifting (PEE-HS), would leave quite obvious traces after embedding, suffering the risks to expose the data hiding action. To address this issue, several countermeasures are proposed to conceal the embedding traces while retaining the conventional reversibility feature. The experimental results demonstrate the effectiveness of our proposed method. We believe this brief could shed light on the security aspect of RIDH.
Feature Selection approach using KNN supervised learning for Content-Based Image Retrieval

Digital images have a serious influence over all the world, since, images have a very tangible importance, which impacts several people needs, this gave image retrieval researches this importance. So, Content-based image retrieval (CBIR) became one of the challenges in information retrieval and image processing fields, its services needs to apply for both business and science domains. In this paper we have proposed a CBIR approach that based on statistical methods feature selection via k-nearest neighbor (KNN) technique. Hence, supervised learning is employed for CBIR to train WANG database images, then the query image was the tested sample, and among predicted class we have filtered the final results. Number of randomly selected query images have been tested via this approach, with a variation of CBIR criteria, which have varied among image types, category query, and threshold distances. The average results have presented in three result groups, some RGB images results have showed good evaluation, in term of precisions measure, also grayscale and RGB images have varied values; on the evaluation of f-measures results, which have changed according some different CBIR factors. Proposed feature selection have made CBIR simpler and effective, this have been showed in high precision evaluation in final results.
Modelling uncertainty in reinforcement learning

This paper discusses the model problem presented in "A model for system uncertainty in reinforcement learning", Systems and Control Letters, 2018, for certain tasks in reinforcement learning. The model provides a framework to deal with situations in which the system dynamics is not known and encodes the available information about the state dynamics as a measure on the space of functions. Such a measure is updated in time, taking into account all the previous measurements of the state variable and extracting new information from them. Here we will mainly focus on the differences between the present model and central algorithms used in reinforcement learning (i.e. value iteration and Thompson sampling).
A Collaborative Filtering Recommendation Algorithm Based on Density Peak Clustering

With the increasing of data, the recommendation performance of the traditional collaborative filtering recommendation algorithm is getting lower. In this paper, a new clustering algorithm is proposed, which is combined with collaborative filtering recommendation to improve the recommendation efficiency. The proposed algorithm uses density peak clustering (FDP) to preliminarily determine the clustering center as the initial center of K-means algorithm which solves the disadvantage that K-means algorithm is easy to fall into local optimization. The clustering algorithm is used to divide users, and then collaborative filtering recommendation is carried out according to the category to which the target user belongs, which provides users with more "personalized" recommendation. The experimental results show that compared with K-means, the proposed clustering algorithm improves the clustering accuracy. In addition, the clustering algorithm combined with collaborative filtering recommendation is verified on Group Lens-Movie Lens dataset, and the results show that the algorithm is more effective than the traditional single cooperative filtering recommendation algorithm.
A Hybridized Adaptive Fruit Fly Optimization Based Online Sequential Extreme Learning Machine for Bio-Medical Data Classification

The objective of classification in high dimensional biomedical data is to map an input feature space to a predefined class labels with higher classification accuracy and less computational time. Although various machine learning techniques have been developed till now, still it is a challenging issue among researchers to develop accurate classifier for better diagnosis of various diseases. Therefore, in this study a new classifier named as adaptive fruit fly optimization (AFFO) based online sequential extreme learning machine (OSELM) is presented to classify biomedical data like Indian diabetes, Parkinson and Indian liver patient available in UCI repository. The proposed model performance is also compared with various models like AFFO-ELM and OSELM. The performances of these models are validated using some performance measures such as G-mean, sensitivity, F-Score, accuracy, specificity, and precision. The results confirm that AFFO-OSELM performance outperforms over other models used in this study.
Chapter 12. Agreement alternations in Modern Hebrew

Agreement is a type of relationship between two linguistic elements, often characterized as an asymmetric relationship where one element, the controller, determines the agreement features of another, the target, within a particular syntactic domain. Although according to prescriptive grammars, agreement relationships are stable and deterministic, usage-based data reveal considerable variation. Building on data retrieved from heTenTen 2014, a billion-token web-crawled Hebrew corpus, we present and discuss two types of agreement alternations: (1) agreement targets which alternate between exhibiting feminine vs. masculine gender, full vs. default agreement, and formal vs. semantic agreement, and (2) controller competition, where an agreement target is controlled by one of two possible controllers. Naturally, this perspective on agreement highlights the exceptions and overlooks the regularities, yet we argue that an examination of such alternations provides clues as to the true nature of the agreement relation.
Myanmar News Retrieval in Vector Space Model using Cosine Similarity Measure

Information Retrieval (IR) is an effective means of retrieving the best relevant document to user query. Nowadays, the problem of documents similarity deals with IR is retrieving required information from a large amount of data. In this paper we studied a Vector Space Model (VSM) that is used in IR and represents a document as a vector in an n-dimensional space, where each dimensional represents a term and measured through cosine angle between two vectors. The objective of this paper is to retrieve the relevant file from Myanmar news data sets using cosine similarity measure in VSM to user's query. Evaluations are done in terms of similarity score by Precision, Recall and F-score.
Effectiveness of Graph Data Visualization of News using VR Experience

The need for complex bibliographic search in the scientific community makes it possible to propose tools that help researchers for such task [1]. The Virtual Reality (VR) environment also provides new possibilities for interaction design in visualization applications and help users to process graphics visualization tasks [2], [3]. Currently, hardware technology is more affordable. Therefore, it is convenient and timely to develop visualization applications that make use of said technology [4]. In this paper, we develop a data visualization application with Virtual Reality for explore the reference between news using graphs. We analyze related work and we selected some design guidelines such as Rendering Techniques and Interaction Techniques [3], [4]. Subsequently, we develop a virtual reality application using Unity [5] and we used HTC Vive [6] to show graphs in immersive environments. We create a graph of news references that shows the abstract of each article when interacting with the nodes using the controls of the HTC Vive. The result expects to show the impact of virtual reality in order to maximize the understanding and interaction of the data.
A Survey on Blockchain Security

The blockchain is a revolutionary concept whose premise has been adopted by numerous industries. Applications of the blockchain have single handily driven an age of innovations. There has been a rapid race to use the blockchain to replace old centralized institutions and measures thereby greatly reducing the inefficiency. In this paper, we will survey some unique blockchain implementation measures to update and innovate inefficient sectors in different industries. We also review the security implementations inherent to blockchain along with upgrades done by specific researchers. The blockchain, while it does improve security is not attack proof. We will then discuss attack vectors that apply specifically to the blockchain.
Two-Phase Multimodal Neural Network for App Categorization using APK Resources

Following an exponential increase in the number of applications created every year, there are currently over 2.5 million apps in the Google Play Store. Consequently, there has been a sharp rise in the number of apps downloaded by users on their devices. However, limited research has been done on navigability, grouping, and searching of applications on these devices. Current methods of app classification require manual labelling or extracting information from the app store. Such methods are not only resource-intensive and time-consuming but are also not scalable. To overcome these issues, the authors propose a novel architecture for classification of applications into categories, utilizing only the information available in their application packages (APKs) - consequently removing any external dependency and making the entire process completely on-device. A multimodal deep learning approach is followed in a 2-phase training scheme by independently training neural models on distinct sets of information extracted from the APKs and assimilating and fine-tuning the learned weights to incorporate combined knowledge. Our experiments show significant improvement in the evaluation metrics for app classification and clustering over the set benchmarks. The proposed architecture enables a fully on-device solution for app categorization.
Adaptive bag‐of‐visual word modelling using stacked‐autoencoder and particle swarm optimisation for the unsupervised categorisation of images

The bag-of-visual words (BOVWs) have been recognised as an effective mean of representing images for image classification. However, its reliance on a visual codebook developed using handcrafted image feature extraction algorithms and vector quantisation via k-means clustering often results in significant computational overhead, and poor classification accuracies. Therefore, this study presents an adaptive BOVW modelling, in which image feature extraction is achieved using deep feature learning and the amount of computation required for the development of visual codebook is minimised using a batch implementation of particle swarm optimisation. The proposed method is tested using Caltech-101 image dataset, and the results confirm the suitability of the proposed method in improving the categorisation performance while reducing the computational load.
Distributed Estimation using Square Root Decompositions of Dependent Information

Sensor networks allow robust and precise estimation by fusing estimates from several distributed sensor nodes. Because of the often limited communication resources, a trade-off between the amount of information communicated and the quality of the fusion result has to be made. On the one hand, obtaining the optimal fusion result often needs an infeasible amount of additional information, but on the other hand, conservative methods usually lead to more pessimistic results in comparison. This paper proposes a square root decomposition of the incorporated noise terms to reconstruct the cross-covariance matrices between sensor nodes. To save communication bandwidth, a residual is defined that allows bounding of the cross-covariance matrix with a reduced number of noise terms. The consistency of the proposed method is demonstrated by two simulation examples featuring a linear and a nonlinear setup and is compared with other state-of -the-art fusion methods.
Age and Gender Recognition Using Multi-task CNN

The investigation into age and gender identification has been receiving more attention from researchers since social and multimedia networks are becoming more popular nowadays. Recently published methods have yielded quite good results in terms of accuracy but have also proven to be ineffective in realtime applications because the models were too complicated. In this paper, we propose a lightweight model that can classify both age and gender. The number of parameters used in this model is 5 times less than existing models. Experiment results show that the accuracy of the proposed method is equivalent to state-of-the-art methods, while the speed of age and gender recognition decreases by 4 times on the Audience benchmark.
Towards Performance Modeling of Speculative Execution for Cloud Applications

Interesting approaches to counteract performance variability within cloud datacenters include sending multiple request clones, either immediately or after a specified waiting time. In this paper we present a performance model of cloud applications that utilize the latter concept, known as speculative execution. We study the popular Join-Shortest-Queue load-balancing strategy under the processor sharing queuing discipline. Utilizing the near-synchronized service property of this setting, we model speculative execution using a simplified synchronized service model. Our model is approximate, but accurate enough to be useful even for high utilization scenarios. Furthermore, the model is valid for any, possibly empirical, inter-arrival and service time distributions. We present preliminary simulation results, showing the promise of our proposed model.
Modifying Queries Strategy for Graph-Based Speculative Query Execution for RDBMS

The paper relates to parallel speculative method that supports query execution in relational database systems. The speculative algorithm is based on a dynamic analysis of input query stream in databases serviced in SQLite. A middleware called the Speculative Layer is introduced, which based on a specific graph representation of query streams chooses the Speculative Queries to be executed. The paper briefly presents the structure of the Speculative Layer and graph modeling method. Then an extended version of speculative algorithm is presented which assumes an increased number of modifying queries in input query stream. Each modifying query present in the analysed query stream endangers already executed Speculative Queries with possibly invalid data and blocks their further use. We propose more sophisticated modifying queries analysis which aims in reducing the number of Speculative Queries which have to be deleted and thus decreases the necessary data manipulations. Experimental results are presented based on the proposed algorithms assessment using a real testbed database serviced in SQLite.
PYTHON AS A BASIC LANGUAGE FOR TEACHING PROGRAMMING IN SCHOOL

The article deals with the issues related to the choice of programming language for initial training in programming at school. The choice of Python as the main programming language in the basic course of informatics in grades 7–9 is justified. The characteristics of Python whose allow to recommend it for initial programming training in school are described. The article also presents the results of a survey of informatics teachers who have been teaching programming on Python for several years at informatics lessons in grades 7–9.
ICAN: Introspective Convolutional Attention Network for Semantic Text Classification

Semantic text classification involves a deep understanding of natural language by going beyond mere syntactic information and capturing complex semantic properties like synonymy, polysemy and negation. We propose a novel attention mechanism called Introspective Semantic Attention embedded within a cascaded CNN architecture. We call our network Introspective Convolutional Attention Network (ICAN). In addition to extracting semantic information using convolution operations, ICAN derives semantic attention from its primary convolutional features instead of using a separate attention module. We also introduce a novel hybrid pooling strategy for our architecture which aids in preserving pertinent information encapsulated within a sentence, while discarding meaningless noise. Our architecture, while light-weight and efficient, promises high accuracy with respect to state of the art architectures - making it ideal for embedded systems and commercial servers alike.