Reviewerly/exHarmony huggingface dataset:
Protection of mental healthcare documents using sensitivity-based encryption

As all the domains move towards digitisation, maintaining voluminous digital data becomes a vital issue in organisations and enterprises. Especially ensuring data security and privacy is the most important problem. In the domains such as healthcare, data security breaches and patient identity theft are the growing concerns in current scenario. Adopting IT services which are provided under cloud-based technologies again increases the security threats in healthcare data maintenance. Cloud-based technologies also ensures data security to a certain level by employing cryptographic techniques though several cryptographic techniques exist to protect data, the selection of appropriate technique increases the security while reducing the processing cost. Cryptographic techniques can be chosen based on the content of the digital document involved in various applications. The proposed method analyses the sensitive terms involved in textual medical documents and determines the adoption of appropriate cryptographic techniques. As security remains top concern for cloud adoption the proposed sensitivity-based encryption improves the security and encryption efficiency at a significant level. The experimentation reveals that about 4% of time complexity gets reduced in encryption.
Assessing the risk reducing effect of measures against intelligent attacks: review and discussion of some common approaches

It is well-established that there is a fundamental phenomenological distinction between intentional events and non-intentional events. Different approaches have been proposed for assessing risk from intentional events, including a conditional screening approach ignoring the probability of attack, probabilistic risk analysis, game theoretic modelling, and a semi-quantitative approach highlighting the knowledge dimension and surprises. In the present paper, we review and compare these four approaches in terms of how they assess the effect of risk reducing measures. The potential theft of paintings from an art museum is used as an illustrating example. The comparison illustrates how the different fundamental assumptions underlying these approaches influence the baseline conclusions made regarding risk reducing measures. A scheme for assessing assumptions and their justification is suggested, linked to the semi-quantitative approach and showing how this approach can contribute to the structuring and evaluation of the decision basis provided by the other approaches.
Depth-based support vector classifiers to detect data nests of rare events

The aim of this project is to combine data depth with support vector machines (SVM) for binary classification. To this end, we introduce data depth functions and SVM and discuss why a combination of the two is assumed to work better in some cases than using SVM alone. For two classes X and Y , one investigates whether an individual data point should be assigned to one of these classes. In this context, our focus lies on the detection of rare events, which are structured in data nests: class X contains much more data points than class Y and Y has less dispersion than X. This form of classification problem is akin to finding the proverbial needle in a haystack. Data structures like these are important in churn prediction analyses which will serve as a motivation for possible applications. Beyond the analytical investigations, comprehensive simulation studies will also be carried out.
AI3SD Video: Outlier detection in Scientific Discovery

Detecting anomalous readings in data is a problem. Humans are good at some types, for example with images, however machines find it rather more difficult. Detecting anomalies in time series data is even more tricky. Discriminating between data that is part of the same distribution, or caused by some other process is also nontrivial. Anomaly detection is used in a wide range of applications, for example fraud detection for bank accounts, condition monitoring of mechanical systems, and in medical imagery. In all these applications, an outlier is indicative of a problem that requires further attention. A range of outlier detection methods is presented, and tested on a range of synthetic multivariate time series data. A novel method, cyclic regression, is presented and compared to more traditional methods. The application of these methods to real world data is demonstrated.
IoT-based Smart Home Device Monitor Using Private Blockchain Technology and Localization

Internet of Things (IoT)-based smart home applications are rising in popularity. However, this trend attracts malicious activity, which causes cost-efficient security to be in high demand. This paper proposes a low-end design that reinforces the security of a home network. It uses private blockchain technology and localization via RSSI-based trilateration. We investigated the benefits of private blockchains over their public counterpart, and we improve the precision of the localization algorithm by testing it against different wireless technologies. The results conclude that using a private blockchain with a WiFi-based communication system produces the most efficient iteration of the proposed design.
Development of Visual Language Design Guideline For User Experience-Based Interactive Systems

Websites and web applications have become more complex with the advancement of technologies in our industry. It used to be a one-way static medium has evolved into a very highly interactive system. The developer of an organization’s websites or web application needs to be dynamic and efficient to accept any frequent change requests. However, some of the main challenges faced by the front-end developer are mainly caused by the inconsistency of the webpages design and incomplete portal change requests. The change request often provides incomplete resources such as incorrect banner sizes, blur images, and incomplete information. Furthermore, some of the requests need to be completed within a very short time-frame. To address these issues, in this paper, we developed a visual language design guideline for a telecommunication company in Malaysia, based on the customization of a widely used visual language design. The developed guideline is then evaluated by the company's internal team. The evaluation shows that the visual language design guideline did help the team in achieving a consistent website.
A comparative analysis of SIFT, SURF and ORB on sketch and paint based images

Image retrieval has been one of the most interesting and emergent research areas in the field of computer vision. Content-based image retrieval (CBIR) systems are used in order to automatically index, search, retrieve and browse images from the databases. Content-based image retrieval system consider colour and texture features of the image, however those features are different in transformed images even though it is the toughest challenge for the CBIR to understand the image. Human perspective that has been based on input is essential for any retrieval system. Hand drawing images and painting images are considered as query image for this retrieval system. This paper has explored few eminent feature extraction techniques like scale invariant feature transform (SIFT), speeded up robust features (SURF) and oriented FAST and rotated BRIEF (ORB) as well as the performances of these techniques for sketch and paint based images. The suitable extraction technique is identified by this examination, the significance of SIFT, SURF and ORB features are listed.
Performance Evaluation of DWT based Multiple Input Multiple Output OFDM for Internet Telephony Applications

Digital communication plays a vital role in transmitting and receiving the information. With the advent of advancements in telecommunications, requirement of high data rate is in evident in Digital communication for the transmission of the signal. The information which has to be transmitted over a long distance needed to be error free while receiving in order to obtain the quality of the information. Emphasizing on the high data rate and also error free requirements, the role of Multiple Input Multiple Output and Wavelet as a combination makes progress towards high extent in achieving successful transmission. The proposed work emphasizes on the application of Discrete wavelet transform for filtering the Quadrature Amplitude modulated wave for the internet telephonic communication where the digital communication plays a major role. This proposed work purely concentrates on the digital communication part of the internet telephony communications. Stein's Unbiased Risk Estimate is used to obtain the information samples with soft thresholding. The Bit to Error rate performance is measured for various modulation order and the simulated results are shown and also the values of the Bit to Error rate are tabulated.
A New Efficient Learning Approach E-PDLA in assessing the Knowledge of Breast Cancer Dataset

Breast cancer is a deadly cancer that develops from the breast tissue. It is one among few reasons for women deaths in the world. Data mining and modern data analytics methods provide excellent support to infer knowledge from the existing database. Application area like medical world needs such efficient automated knowledge inferring tools and methods for better decision making. In this work, efficient data analytic methods like, K-nearest neighbours (KNN), C4.5 decision tree, naïve Bayes (NB), support vector machine (SVM), expert-pattern driven learning architecture (E-PDLA) are performed against the Wisconsin Breast Cancer (WBC) dataset. The aim of the work is to diagnose the proficiency and operational capabilities of the algorithms that have been tested. The results have been tabulated with the possible performance metrics and found the E-PDLA gives highest accuracy (99%) on classifying the dataset which gives insight of knowledge. All experiments have been simulated in J2EE environment with support of weka tool.
Enhancing Data Privacy through Decentralized Predictive Model with Blockchain-based Revenue

Federated learning (FL) permits a vast number of connected to construct deep learning models while keeping their private training data on the device. Rather than uploading the training data and model to the server, FL only sends the local gradients gradually. Hence, FL preserves data privacy by design. FL leverages a decentralised approach where the training data is no longer concentrated. Similarly, blockchain uses the same approach by providing a digital ledger that can cover the flaws in the centralised system. Motivated by the merits of a decentralised approach, we construct a collaborative model of simultaneous distributed learning by employing multiple computing devices over shared memory with blockchain smart contracts as a secure incentive mechanism. The collaborative model preserves a value-driven of distributed learning in enhancing users' privacy. It is supported by blockchain with a secure decentralised incentive technique without having a single point of failure. Furthermore, potential vulnerabilities and plausible defences are also outlined. The experimental results positively recommend that the collaborative model satisfies the design goals.
A Data Pipeline for Optical and Near-infrared Solar Eruption Tracer

With the advent of large astronomical equipments, the traditional development model for data reduction faces problems such as redundancy of programs and conflicting environmental dependencies; Besides as a cluster is a highly coupled computing resource, serious environmental conflicts can lead to the unavailability of the entire cluster. To address this problem, we have developed a new pipeline framework using the concept of microservices. This paper presents the ONSET data pipeline developed through this framework. To achieve near real-time data processing, we optimize the core program using MPI and GPU technologies and evaluate the final performance. The results show that this development model can be built in a short time to meet the requirements of the pipeline, and we believe that this development model has implications for future multi-band and multi-terminal astronomical data processing.
NITCO: an intelligent agent technique for optimising of resource utilisation in cloud

Efficient task scheduling is significant to meet the quality of service (QoS) requirements in cloud computing. Cloud is a large pool of virtual access resources to perform thousands of computational and storage operations. Task scheduling is an NP-hard problem, unsuitable matching leads to performance degradation and violation of service level agreement (SLA). The growing complexity of cloud services needs an extension of existing scheduling algorithms. In this paper, the scheduling problem has been explored based on growing application trends. Cloud dynamic resource provisioning can satisfy users' requirements if execution of tasks performed: identifying of task requirements; workflow of application scheduling using a sufficient amount of resources. In this research work, we present an intelligent agent technique for optimising resource utilisation named NITCO. NITCO considers the above mentioned challenge, identification of task requirements and configuration of resource. The performance of proposed NITCO has been evaluated on simulated cloud environment and comparison of results show that NITCO performed better in terms of execution cost, execution time, VM utilisation and SLA violation while it delivers quality of service.
The logic of entrapment: Digital proletarians; digital fascism; symbiosis between a virus and autocracy

Abstract This text was written on the occasion of the 200th issue of Maska magazine. My goal is to identify and interpret the time when the text is written and when the 200th issue of Maska will be published. I identified a situation of sociability at the time of the coronavirus pandemic and the dominance of digital/postdigital communications. I am interested in the difference between media representation, virus events and political-or-artistic interpretation of modern and transitional forms of human life. If we are talking about digital art/culture/society in relation to the technological turns from the mechanical to the analogue-electronic world, from the analogue to the digital world, from the digital world to the post-digital world, and from the post-digital world into a De Re media possible world, then we are facing a conflict between the dialectic of emancipation through the new and the differentiation of the production/and/consumption of the new in a time and space where the human being is becoming the product of its own product. It is important to index the contemporary antagonism between the ‘digital proletarian’ and ‘digital fascism’. Confronted with digital fascism, digital proletarians pursue a risky process of self-fulfilment and thereby liberation/emancipation in complex digital practices and their impacts on other forms of existence in a critical and dialectic ontology. Therein lies the essential difference between the politics of functionalism and that of liberation.
A Wikipedia-based semantic tensor space model for text analytics

This paper proposes a third-order tensor space model that represents textual documents, which contains the 'concept' space independently of the 'document' and 'term' spaces. In the vector space model (VSM), a document is represented as a vector in which each dimension corresponds to a term. In contrast, the model described here represents a document as a matrix. Most current text mining algorithms only take vectors as their input, but they suffer from 'term independence' and 'loss of term senses' issues. To overcome these problems, we incorporate the 'concept' as a distinct space in the VSM. For this, it is necessary to produce the concept vector for each term that occurs in a given document, which is related to word sense disambiguation. As an external knowledge source for concept weighting, we employ the Wikipedia Encyclopedia, which has been evaluated as world knowledge and used to improve many text-mining algorithms. Through experiments using two popular document corpora, we demonstrate the superiority of the model in terms of text clustering and text classification.
Self-adaptive network structure tuning method based on NSGA-III

Neural networks are arising a wave in the various areas of artificial intelligence and they have adopted in daily life successfully. The structure tuning of neural networks is crucial when building the relative models. The structure of neural networks is usually designed and tuned with experience and plenty of attempts. To reduce the difficulty and cost of structure tuning meanwhile improving its rationality, we propose a new method to tune the structure of neural networks adaptively. In this method, the related structure parameters are optimised. A many-objective algorithm is employed as the optimised tool to get a better structure. We design the experiments combining convolutional neural network (CNN) with Non-dominated Sorting Genetic Algorithm III (NSGA-III). The related experiments are conducted on the MNIST and Malware image datasets. Results show that the method has promising performance on neural networks tuning and can improve the robustness.
An upgraded model of query expansion using inverse-term frequency with pertinent response for internet of things

The IOT plays an important role for recent internet access-based task to future internet and information retrieval and it is a field which is relate our study to the structure, examination, associa...
The Development Of Multimedia-Based Digital Simulation E-Book For Vocational Schools

This research is development research aimed to develop a learning product in the form of a Multimedia-based E-book on Digital Simulation Subjects which are feasible or valid and easy to use or practical. The test subjects in this research and development consisted of content experts, media experts and subject teachers of digital simulation and grade 10th students of SMK Negeri 4 Gowa. The ADDIE development model used in this study consisted of five stages, namely: (1) analysis stage, (2) design stage, (3) development stage, (4) implementation stage (5) evaluation stage. The development of Â multimedia-based digital simulation e-book through the material expert validation stage with the score of 4.73 and media validation 4.60 shows that the material and design or media evaluation in the e-book is feasible or valid. Then, the teacher's response obtained a score of 4.71, the average score of individual student trials was 92.14, and the average score of the student group trial was 90, which means the e-book is easy to use by teachers and students. there were no students who acquired a low score from the test results after learning using the e-book which means it is effectively used.
Distill on the Go: Online knowledge distillation in self-supervised learning

Self-supervised learning solves pretext prediction tasks that do not require annotations to learn feature representations. For vision tasks, pretext tasks such as predicting rotation, solving jigsaw are solely created from the input data. Yet, predicting this known information helps in learning representations useful for downstream tasks. However, recent works have shown that wider and deeper models benefit more from self-supervised learning than smaller models. To address the issue of self-supervised pre-training of smaller models, we propose Distill-on-the-Go (DoGo), a self-supervised learning paradigm using single-stage online knowledge distillation to improve the representation quality of the smaller models. We employ deep mutual learning strategy in which two models collaboratively learn from each other to improve one another. Specifically, each model is trained using self-supervised learning along with distillation that aligns each model's softmax probabilities of similarity scores with that of the peer model. We conduct extensive experiments on multiple benchmark datasets, learning objectives, and architectures to demonstrate the potential of our proposed method. Our results show significant performance gain in the presence of noisy and limited labels and generalization to out-of-distribution data.
The nexus between FDI inflows and economic development in Ghana: empirical analysis from ARDL model

This study examined FDI and economic progress in Ghana. Using annualised time series data obtained from the World Bank over a period from 1984 to 2018, an ARDL approach was implemented. Our findings show that external factors affirm positive impact on FDI attraction and economic development. Specifically, inflation and population have a long and short run substantial impact on attraction of FDI into Ghana. Similarly, at the micro level, financial expansion in the financial sector exerts long- and short-term substantial positive effect on FDI attraction. A 1% rise in FDI inflow and population would result in 234% and 200% economic expansion in the long run. Financial development in the private sector, however, exerts a considerable effect in the short run. This implies a weak resilience against shock for FDI in the private sector. Overall, our model confirms FDI-led economic expansion in Ghana in the long-run. To sustain this, recommendation is policymakers should reduce total control and involvement in the markets by allowing and empowering private sector expansion.
Automatic speaker verification system using three dimensional static and contextual variation-based features with two dimensional convolutional neural network

Automatic speaker verification (ASV) systems are being used as potential alternatives for authentication in security systems. This paper discusses the development of an ASV system trained by logica...
Organisational commitment, work engagement and job performance: empirical study on Nigeria's public healthcare system

Currently, nurses’ job performance in Nigeria’s public healthcare system has been called into question and is under more pressure due to global COVID-19 pandemic In order to enhance the effectiveness, efficiency and promptness of Nigeria’s public healthcare service delivery, this study seeks to explore how work engagement could play a key role in explaining the influence of organisational commitment on nurses’ job performance A survey based questionnaire, using a simple random technique was employed in collecting in data from 406 nurses of public hospitals in Kaduna State The data were analysed employing the partial least squares-structural equation model (PLS-SEM) Results demonstrate that affective and normative commitment wield positive significant effects on nurses’ job performance, while work engagement significantly mediates affective commitment, normative commitment and nurses’ job performance relationships Consequently, this study recommends that the management of public healthcare system and policymakers should improve the commitment of nurses through work engagement Copyright © 2021 Inderscience Enterprises Ltd
A new image binarisation technique for segmentation of text from digital images

Text segmentation in digital images is requisite for many image analysis and interpretation tasks. In this article, we have proposed an effective binarisation technique towards text segmentation from digital images. This image binarisation technique creates numerous texts as well as non-text connected components. Next, it is required to separate the possible text components from the obtained connected components. Further, to distinguish between text and non-text components, a set of features are considered. Then, during training, we consider the two feature files namely text and non-text prepared by us. Here, K-nearest neighbour (K-NN) and support vector machine (SVM) classifiers are considered for the present two class classification problem. The experiments are based on ICDAR 2011 born digital dataset. Our binarisation technique is also applied on publically available dataset street view text dataset (SVT), DIBCO 2009 and ICDAR 2011 Roust Reading Competition. We have accomplished in binarisation and as well as segmenting between text and non-text.