Reviewerly/exHarmony huggingface dataset:
The role of ICT development in knowledge creation and the burden of knowledge nexus: A threshold analysis

The burden of knowledge effects phenomena deserves further attention. Motivated by the previous study on the idea of the threshold limitation to new knowledge creation, this study attempts to examine the burden of knowledge impact on new knowledge. This study postulated that countries that have a higher burden of knowledge may depend on ICT development to support greater knowledge creation. Therefore, this study used threshold regression techniques to estimate the cross-section data from 1995 to 2019. The empirical results show that, with the absence of significant influence on the burden of knowledge, the role of ICT development in shaping new knowledge is likely to be significant. In relation to that, this finding implicates the need to lessen the burden of knowledge rather than depending on ICT development, to serve as a driver of knowledge creation.
Usable Amharic text corpus for natural language processing applications

In this paper, we describe the preparation of a usable Amharic text corpus for different Natural Language Processing (NLP) applications. Natural language applications, such as document classification, topic modeling, machine translation, speech recognition, and others, suffer greatly from a lack of digital resources. This is especially true for Amharic, a resource-constrained, morphologically rich, and complex language. In response to this, a total of 67,739 Amharic news documents consisting of 8 different categories from online sources are collected. The collected corpus passes through a number of pre-processing steps including; data cleaning, text normalization and punctuation correction. To validate the usability of the collected corpora from different domains, a baseline document classification experiment was conducted. Experimental results show that, 84.53% accuracy is registered using deep learning in the absence of linguistic information. Finding indicated that it is possible to use the prepared corpora for different natural language applications in the absence of linguistic resources such as stemmer and dictionary despite the complexity of Amharic language. We are further working towards Amharic news document classification by incorporating a linguistic independent stop-word detection, stemming and unsupervised morphological segmentation of Amharic documents.
Foundations of Differentially Oblivious Algorithms

It is well-known that a program’s memory access pattern can leak information about its input. To thwart such leakage, most existing works adopt the technique of oblivious RAM (ORAM) simulation. Such an obliviousness notion has stimulated much debate. Although ORAM techniques have significantly improved over the past few years, the concrete overheads are arguably still undesirable for real-world systems — part of this overhead is in fact inherent due to a well-known logarithmic ORAM lower bound by Goldreich and Ostrovsky. To make matters worse, when the program’s runtime or output length depend on secret inputs, it may be necessary to perform worst-case padding to achieve full obliviousness and thus incur possibly super-linear overheads. Inspired by the elegant notion of differential privacy, we initiate the study of a new notion of access pattern privacy, which we call “ (ϵ , δ) -differential obliviousness”. We separate the notion of (ϵ , δ) -differential obliviousness from classical obliviousness by considering several fundamental algorithmic abstractions including sorting small-length keys, merging two sorted lists, and range query data structures (akin to binary search trees). We show that by adopting differential obliviousness with reasonable choices of ϵ and δ , not only can one circumvent several impossibilities pertaining to full obliviousness, one can also, in several cases, obtain meaningful privacy with little overhead relative to the non-private baselines (i.e., having privacy “with little extra overhead”). On the other hand, we show that for very demanding choices of ϵ and δ , the same lower bounds for oblivious algorithms would be preserved for (ϵ, δ) -differential obliviousness.
Some Results on the Non-Homogeneous Hofmann Process

The classical counting processes (Poisson and negative binomial) are the most traditional discrete counting processes (DCPs); however, these are based on a set of rigid assumptions. We consider a non-homogeneous counting process (which we name non-homogeneous Hofmann process – NHP) that can generate the classical counting processes (CCPs) as special cases, and also allows modeling counting processes for event history data, which usually exhibit under- or over-dispersion. We present some results of this process that will allow us to use it in other areas and establish both the probability mass function (pmf) and the cumulative distribution function (cdf) using transition intensities. This counting process (CP) will allow other researchers to work on modelling the CP, where data dispersion exists in an efficient and more flexible way.
Extractive text summarisation using Bayesian state estimation of sentences: A Markovian framework

Identifying and extracting valuable information from textual documents in the form of cohesively and appropriately developed summaries is one of the most challenging tasks in text mining and natural language processing. In this article, we present a sequential Markov model, equipped with Bayesian inference, to estimate the degree of importance of sentences in a document and thereby address the text summarisation problem. The proposed methodology models the extractive sentence summarisation as a Bayesian state estimation problem, where the system state is the importance degree of each sentence in a document. The transition and observation models are derived using a nonlinear dynamical system identification based on a recurrent feedback neural model that predicts the sentence observation using the sentence input data. In the end, the transition and observation probability density functions are modelled using a mixture density network. The performance assessment of the system has been carried out by investigating the optimal feature dimensionality and the impact of the model parameters on the system accuracy, using entropy-based risk and loss-based risk measures. Finally, the superiority of the proposed methodology over the state of the art in extractive summarisation is discussed and verified by reporting the recall, precision and accuracy on the real-world benchmark data sets.
Quantum Computing: Unraveling the Hype

Quantum computing portends to be a disruptive technology but, more disturbing (or exciting), a societal disruptor. Computational processing will reach speeds resulting in autonomous adaptability, outreaching human ability to perceive, understand, identify appropriate questions, let alone reach answers, and act. Quantum computation will enable mathematical calculations impractical and impossible for conventional computing. Conventional cryptographic methods will be rendered useless. New possibilities for combinatorics (permutation analysis) will help enable DNA-tailored pharmaceuticals. Complex system modeling will enable dynamic adaptations at speeds that will redefine military strategy and tactics. This chapter introduces terminology and unravels the hype around the science of quantum computing. All explanations are in narrative form, i.e., an introduction to quantum computing minus the math.
Metadata dating the digital city: a software archaeological approach

De Digitale Stad was a pioneering website of the early Dutch web. Historical study of its remnants has required extensive system knowledge, which may pose a new challenge for digital humanity researchers. The study of remnants is covered by the metaphor of “archaeology.” In view of software character of web artefacts, the metaphor “web archaeology” is implicitly limiting. An approach is proposed that considers the working character of the software, “software archaeology.” In a case study it is shown by the example of “metadata dating” how the software archaeological approach can be brought to bear on born digital artefacts. The case illustrates the historical value of extracting time-related metadata from digital artefacts, “metadata dating.” Metadata dating is performed on three archives related to De Digitale Stad. By aggregation of time-related metadata, new historical insights into De Digitale Stad have been gained. Whereas the early internet has often been presented as ever changing, novel and revolutionary, the results bring to light strong continuities in the usage patterns shown in De Digitale Stad. Contrary to expectations, it is observed that the citizens of De Digitale Stad, were primarily interested in the preservation of the traditional content in their files, and less in the results of their own coding efforts.
Hyper-parameters Study for Breast Cancer Datasets: Enhancing Image Security and Accuracy for Prediction Class

This study is all about to propose a method for prediction of Breast Cancer (BC) classification which is a very important field for diagnosis of cancer diseases in medical field. All the research studies that have done earlier are centered to get better accuracy in prediction level of BC using different Machine Learning (ML) algorithms. The earlier paper of the same study proposed comparative study chart of different supervised machine learning algorithm in which SVM found to be good for classification. Now this study proposed a comparative study of selection of different kernel categories and Grid Search (GS) implementation for optimal selection for hyper-parameters which is very important to get better results for prediction in classification process. The choices of hyper-parameters affect the rate of accuracy of algorithm. This study used three datasets WBC (Wisconsin Breast Cancer), WDBC (Wisconsin Breast Cancer for Diagnosis) and WPBC (Wisconsin Breast Cancer for Prognosis) in which a for loop structure and GS is used to get best hyper-parameter choices for model training for better accuracy prediction in classification problem of breast cancer. For the diagnosis of breast cancer the most important part is medical image data and its privacy, security is also very important. Due to the use of cloud computing approaches health care sector is also going to store data to the third party server.
INTELLIBOT - Intelligent Voice Assisted Chatbot with Sentiment Analysis, COVID Dashboard and Offensive Text Detection

Chatbot has become an essential crowd puller in the world today and is used in various domains and professions. With increasing technologies and advancements in AI, components of voice assistance have been gaining prolific importance when integrated with chatbots. INTELLIBOT is a smart bot that not only interacts with its users through an interactive and aesthetic platform but also had added features for customized experience. It makes use of speech to text and text to speech processing to listen to the user and speak back to them. This paper would give insights on the various applications of chatbots and existing systems along with the system we have proposed to overcome and curb the challenges posed by them through the INTELLIBOT framework. Further, the paper would elucidate the use of Naïve-Bayes algorithm and pattern matching algorithms for the same.
Preventing Fault Attacks on S-Boxes of AES-Like Block Ciphers

Substitution functions (S-boxes) are essential for the design of AES-like cryptosystems as they play an important role in the security of the cryptosystems. Most of the cryptosystems are highly vulnerable against fault-attacks. So, countermeasures against fault-attacks are necessary to make the cryptographic devices secure. This paper presents a fault resilient design of S-boxes for AES-like block ciphers. It is done by fault detection and correction. The S-boxes are designed based on the synthesized nonlinear cellular automata. The proposed design guarantees $$100\%$$ coverage of double-byte fault detection and single-byte fault correction.
Learning Co-occurrence Embeddings for Sentence Classification

The sequence and co-occurrence information of the words constituting the sentence is crucial for the sentence classification task. Distributed representations like word embeddings successfully learned for classification tasks. Learning longer word sequences like sentence embeddings for text classification incorporating word sequences and co-occurrence information remains a challenging task. This research article, proposed a CNN-based embedding learning for sentence classification that incorporates sequence as well as co-occurrence information of the words. The empirical result shows that the proposed approach performance better over other baseline models.
Privacy Preserving of Two Local Data Sites Using Global Random Forest Classification

In the rapidly growing computing world, the data is being generated every minute, and it has been a big challenge to manage and maintain privacy of the data when it is required to share among different locations. In this regard the privacy preserving data mining is playing a major role to design and develop the methods and models to preserve privacy of data in data owners perspectives, when ever the necessity of exchanging the information between multiple parties to be done. There has been lot of research going on in the field of computational intelligence, machine learning and soft computing domains and some good number of algorithms and secured models are being developed by the researchers. We have worked on the random forest classification as the best example model to preserve privacy of two parties when a combined classification to be done in order to build a privacy preserving global random forest classifier. The process has two phases from local random forest to global random forest. The aim was to build a final global random forest by aggregating local random forests of local or individual parties based on voting between parties. In this work there are two proposed algorithms for horizontal and vertical partitioning of data Horizontal-PPGRF and Vertical-PPGRF. Both were performing well in building PPGRF without disclosing the input information of any party to each other. The ensemble learning is applied through random forest classifier to build a secured global random forest of two parties.
Privacy Preserving and Secured Clustering of Distributed Data Using Self Organizing Map

Preserving privacy of data in data mining domain has been a challenging objective to achieve. There has been lot of progress in applying secure algorithms in order to achieve privacy preserving of sensitive information. In distributed data mining perspective, there is necessity to share the information among the different parties aiming for combined outcomes. Privacy preserving methods and algorithms helps in preserving privacy of data when it is compulsory to share the information between the collaborated parties to perform various data mining operations like Classification and Clustering. It is always important to address the privacy issues of an individual when the data is shared among different parties to achieve a common goal. It is observed that there has been great development in addressing the privacy issues and designing privacy preserving algorithms applied to achieve a secured model. Adopting neural network learning for privacy preserving of data has became very much promising approach. In this paper we adopt self organizing map (SOM) neural network trained in order to preserve the privacy of data of two different parties, in combined environment to perform combined clustering. We present two novel algorithms to preserve privacy of horizontally and vertically partitioned data while combined clustering is performed using SOM. We apply perturbation of data and training the SOM for horizontal partitioning and Cryptography based solutions for vertical partitioning. The proposed algorithms, Horizontal-PPSOM and Vertical-PPSOM are novel contributions in this paper and all the experiments for both algorithms shows good results with an assured privacy level.
Uncertainty-aware non-autoregressive neural machine translation

Most existing non-autoregressive neural machine translation (NAT) models generally employ the posterior probability to indicate the model confidence during training, which seems to lag behind the novel uncertainty estimations (UEs) methods successfully deployed in other natural language processing (NLP) tasks. Previous research has practically ignored the large-scale exploration of UE methods in the NAT problem. In this paper, we propose a strategy based on Active Learning employed to investigate whether these sophisticated uncertainty-aware methods are more effective in the NAT problem. Besides, we provide an in-depth analysis of the impact of different widely employed UE methods and propose several tailored ones. In the end, we incorporate these exceptional ones into the practical one-pass GLAT model to obtain enhanced performance. Experimental results demonstrate that sophisticated uncertainty-aware UE methods with the two-step training paradigm are potentially superior to represent the model confidence in facilitating token-level decision-making compared to the posterior probability in NAT to a certain extent.
Study of Various Attacks Over Images Transferred Optically Through Communication Channel

On the basis of double random phase encoding (DRPE), we present an improved picture encryption technique in this work. In order to protect images from being compromised, a secure picture encryption system with a large key domain and low quantization error must be developed, as previously stated. DRPE, the second lens is unable to offer any security contribution, and it is always feasible to compute the FT to offset this operation if one knows the final cipher-image. This is regarded to be one of the most important components of the DRPE system. A variety of attacks on DRPE-type encryption schemes have been developed in recent years. This optical technology and traditional cryptography are being compared in an attempt to acquire a better knowledge of the system and overcome the attack susceptibility of DRPE systems. In order to avoid some of the current issues in traditional cryptosystems and prevent the comfortable realization of the cipher-image in the transform domain, a safe image encryption symmetric method must be designed and implemented.
Object Detection Through Computer Vision

This research work is focused on the field of object detection through computer vision, focusing on a very popular topic nowadays, smart cities. Therefore, a proposal is presented, after evaluating the different systems that have been used in the literature, based on deep learning techniques together with the use of images to detect traffic density. The developed technique corresponds to a You Only Look Once (YOLO) algorithm, which allows using previously trained weights or training the weights with a personalized dataset, for traffic detection. In relation to the results obtained after the application of these techniques, it can be observed how, depending on the data set used for training, the algorithm obtains different detections.
A Spanish Political Tweets Fine-Tuned Sentiment Analysis Model

Society is dominated by the era of data; this data is of great importance as it provides a great variety of information about people and their context. One of the tools that provide great information about users is social networks; therefore, Twitter is one of the most used social networks and has great relevance in the world of politics. Thus, extracting knowledge and creating user profiles can be of great importance for certain sectors. For this reason, in this article, we propose to create the first model for sentiment analysis of political tweets in Spanish that is open to the scientific community. The results shown in the article are promising.
An effective multilingual retrieval with query optimization using deep learning technique

• Multilingual retrieval is the information that are retrieved in multiple languages, for the response to the query given by the user. • In a cross lingual search, the user query is given to the various information sources thus enabling the retrieved results of different languages. • The single retrieved results would term to be the results merging problem of multilingual search aspects. • The presented model involves processes namely multilingual search and query optimization. • The stacked autoencoder (SAE) model is used for Multilingual Search process. Multilingual retrieval is the information that are retrieved in multiple languages, for the response to the query given by the user. In a cross lingual search, the user query is given to the various information sources thus enabling the retrieved results of different languages. The single retrieved results would term to be the results merging problem of multilingual search aspects. This paper presents a novel query optimization enabling the user to provide a query in Tamil language and the results are retrieved for across languages. The presented model involves processes namely multilingual search and query optimization. The stacked autoencoder (SAE) model is used for Multilingual Search process. In addition, the query optimization process considered as a NP-hard problem, can be resolved by the use of gray wolf optimization (GWO) algorithm. Besides, global vectors (GloVe) technique is applied to construct the domain-specific sentiment lexicon. An extensive set of experimentation analysis was performed and the results are investigated under distinct aspects. The resultant experimental value of the proposed technique in multilingual search obtains the accuracy of 75% and the optimization algorithm gains the supremacy of the existing algorithms.
Bias Analysis on Twitter

Sentiment analysis on comments retrieved from social networks has become an area of great interest in recent years, both at academic and corporate level. This analysis, including trend analysis and control of hate posting, is required to moderate forums or even to eliminate spam, and only with the help of automation it is possible to efficiently process the enormous volume of information that is generated daily. An important application of this analysis is the detection of sexist bias in social networks, which can help to identify early behavior that could later lead to violent actions. However, there are various obstacles to the correct treatment of the messages and the detection of possible biases, among which the absence of context in the analysis of these messages and the use of tools and libraries focused on the English language stand out. In this article we study the issues associated with this automatic analysis, and we introduce the foundation of an analysis method for biases or prejudices in the messages retrieved from the Twitter network, considering the context, and using a mechanism to reduce dependencies with the analyzed language. A supervised classifier was developed whose objective is to be able to identify possible biases on any topic, taking into account context and minimizing the dependency on language. The method was tested in a case study for the detection of sexism, using idioms typical of the geographical area analyzed. In the experiments carried out, an accuracy of 73% was obtained, which can be improved by expanding the training set.