Reviewerly/exHarmony huggingface dataset:
Towards Knowledge Warehousing: Application to Smart Housing

The terms data, information and knowledge should not be treated as synonyms in any context. In fact, a hierarchical order between these entities exists where data become information and information become knowledge. Massive amounts of data are analysed everyday in order to extract valuable knowledge to support decision making. However, the size of the extracted knowledge compromises the speed of reasoning and exploitation of the latter. In this paper, we propose the paradigm of knowledge warehousing to store and analyse big amounts of knowledge through online knowledge processing and knowledge mining techniques. Our proposal is supported by an original knowledge warehouse framework and a case study for the smart housing technology. A multi-agent system built on a knowledge warehouse architecture is illustrated where each agent has a knowledge base about his assigned task. The paradigm is expected to be applicable for other knowledge tasks and domains as well.
A Parallel Approach for Web Session Identification to make Recommendation Efficient

A sequence of web pages visited by the clients over a particular timeframe is called a session. Web log mining is done to analyse the behaviour of the users, using the web access patterns. Sessions are identified as the significant part of the construction of the recommendation model. The novel part of the work makes use of backward moves made by the user, considering both the referrer url and the requested url extracted from the extended web log for session identification which is not taken into consideration in the existing heuristic-based approach. Two noteworthy issues in session identification are: 1) framing excessively numerous smaller length sessions; 2) taking longer time for identifying the sessions. In the proposed work, the length of the sessions are maximised using split and merge technique and the time taken for session identification is reduced using thread parallelisation. For efficient storage and retrieval of information the hash map data structure is used. The proposed work shows significant improvement in performance in terms of execution time, standard error, correlation coefficient and the objective value.
Sparse Recovery for Orthogonal Polynomial Transforms

In this paper we consider the following sparse recovery problem. We have query access to a vector $\vx \in \R^N$ such that $\vhx = \vF \vx$ is $k$-sparse (or nearly $k$-sparse) for some orthogonal transform $\vF$. The goal is to output an approximation (in an $\ell_2$ sense) to $\vhx$ in sublinear time. This problem has been well-studied in the special case that $\vF$ is the Discrete Fourier Transform (DFT), and a long line of work has resulted in sparse Fast Fourier Transforms that run in time $O(k \cdot \mathrm{polylog} N)$. However, for transforms $\vF$ other than the DFT (or closely related transforms like the Discrete Cosine Transform), the question is much less settled. In this paper we give sublinear-time algorithms---running in time $\poly(k \log(N))$---for solving the sparse recovery problem for orthogonal transforms $\vF$ that arise from orthogonal polynomials. More precisely, our algorithm works for any $\vF$ that is an orthogonal polynomial transform derived from Jacobi polynomials. The Jacobi polynomials are a large class of classical orthogonal polynomials (and include Chebyshev and Legendre polynomials as special cases), and show up extensively in applications like numerical analysis and signal processing. One caveat of our work is that we require an assumption on the sparsity structure of the sparse vector, although we note that vectors with random support have this property with high probability. Our approach is to give a very general reduction from the $k$-sparse sparse recovery problem to the $1$-sparse sparse recovery problem that holds for any flat orthogonal polynomial transform; then we solve this one-sparse recovery problem for transforms derived from Jacobi polynomials.
Pattern Discovery in Colored Strings

We consider the problem of identifying patterns of interest in colored strings. A colored string is a string in which each position is colored with one of a finite set of colors. Our task is to find substrings that always occur followed by the same color at the same distance. The problem is motivated by applications in embedded systems verification, in particular, assertion mining. The goal there is to automatically infer properties of the embedded system from the analysis of its simulation traces. We show that the number of interesting patterns is upper-bounded by 𝒪(n²) where n is the length of the string. We introduce a baseline algorithm with 𝒪(n²) running time which identifies all interesting patterns for all colors in the string satisfying certain minimality conditions. When one is interested in patterns related to only one color, we provide an algorithm that identifies patterns in 𝒪(n²log n) time, but is faster than the first algorithm in practice, both on simulated and on real-world patterns.
An Improved Cryptanalysis of Large RSA Decryption Exponent with constrained Secret key

In this study, we revisit the RSA public key cryptosystem in some special case of Boneh and Durfee's attack when the private key d assumes to be larger than the public key e. The attack in this study is the variation of an approach adopted by Luo et al. (2009) based on large decryption exponent. They had chosen a large private key (d > e) and found the weak keys in some specific range between N0.258 ≤ e ≤ N0.857. We highlight the shortcomings and new improvements in our study with more refined bound analysis up to the range between N0.104 ≤ e ≤ N0.923. Our experimental results revealed more refined bounds using lattice-based Coppersmith's method. In our experimental yield, we find the small roots of the devised polynomial, which helps to factorise the RSA modulus of size up to 1,024-bits. We also measure the probability of a specific range of weak keys, which further certify our results about weak keys in an RSA constrained secret key environment.
Dimensionality Reduction for Flow-Based Face Embeddings

Flow-based neuralPoliakov,S. networks are promising generative image models. OneBelykh,I. of their main drawbacks at the moment is the large number of parameters and the large size of the hidden representation of modeled data, which complicates their use on an industrial scale. This article proposes a method for isolating redundant components of vector representations generated by flow-based networks using the Glow neural network as an example to the face generation problem, which has shown effective ten times compression. The prospects of using such compression for more efficient parallelization of training and inference using model parallelism are considered.
Maximum Entropy Based Semi Supervised Learning for Automatic Detection and Recognition of Objects Using Deep Convnets.

Object detection and localisation is one of the major research areas in computer vision that is growing very rapidly. Currently, there is a plethora of pre-trained models for object detection including YOLO, mask RCNN, RCNN, fast RCNN, multi-box, etc. In this paper, we proposed a new framework for object detection called 'maximum entropy-based semi-supervised learning for automatic detection and recognition of objects'. The main objective of this paper is to recognise objects from a number of visual object classes in a realistic scene simultaneously. The major operations of our proposed approach are preprocessing, localisation, segmentation and object detection. In the preprocessing, three processes, noise reduction, intensity normalisation, and morphology are considered. Then localisation and object segmentation is performed using maximum entropy in which optimal threshold is detected and in the end, object detection is performed using deep ConvNet. The performance of the proposed framework is evaluated using MATLAB-R2018b and it is compared with some previous state of the art techniques in terms of localisation error, detection and segmentation accuracy along with computation time.
Polarization Dependence Of Incident Angle Sensitivity In Soi Photodiode With 2d Hole Array Grating

The plenoptic imaging technique requires angle sensitive pixel (ASP) to capture the direction (θ, ϕ) of light. In this work, we experimentally demonstrate the polarization dependence of incident angle sensitivity in the silicon-on-insulator (SOI) photodiode (PD) stacked with 2D hole array grating. Measured results can be explained based on phase matching condition between the diffracted light from the grating and the SOI waveguiding mode. The theoretical prediction and the measured results show that the incident light with specific incident angle shift in the contours of the peak angle when the grating period is varied. The proposed device has potential application as image sensor pixels in lensless imaging, refocusing and depth mapping.
Cryptanalysis of certificateless authenticated key agreement protocols

In this work, we cryptanalyse two certificateless authenticated key agreement (CL-AKA) protocols, Bala et al. (2018) and Xie et al. (2019), which are recently proposed claiming provable security. Specifically, we show impersonation attacks against the two protocols with successful probability 1 in extended eCK model using at most two queries. Then, the process of our cryptanalysis is abstracted to a general method for cryptanalysing a specific class of CL-AKA protocols which we call linearly-expressible CL-AKA protocol. Our method suggests new security requirements of CL-AKA protocols.
Construction of Complete Minimal Test Set for Single intra-level Bridging and Stuck-at Faults in Reversible Circuits

Reversible logic computing is gaining enough interest in the field of low-power circuit design technology. Moreover, the concept of reversible computing is widely used in quantum circuit computation. Therefore, it has motivated researchers to explore reversible logic as a circuit design alternative. Several problems of synthesis and optimisation of the reversible circuits have been reported in the present literature. In this context, the testing plays an important role in the effective performance of these circuits. For detecting all possible faults in the reversible circuits, numerous fault models have been proposed, where many of the proposed fault models common to conventional logic circuits. In this paper, we consider the problem of testing in the NCT or GT library-based reversible circuits with respect to an efficient test set generation for single intra-level bridging faults and single stuck-at faults. The proposed test generation method has been developed based on the concept of one-to-one mapping mechanism (reversible circuit property). Finally, experimental results show that the generated test is capable of 100% fault coverage for both the fault models and also the results are compared with the existing methods for analysing the test set size.
Impact of Organisational Innovation on Non-Financial Organisational Performance: An Exploratory Study of Higher Education Institutions in Oman

Organisational innovation may contribute in enhancing the skills and knowledge for the employees which will lead to improving the organisational performance. However, the research literature has been dominated with studies examining the association between organisational innovation and financial organisational performance. There has been increased interest in the non-financial organisational performance (NFOP) in higher education institutions (HEIs) in the Sultanate of Oman. The aim of the study is to measure the impact of organisational innovation on non-financial organisational performance in the HEIs in the Sultanate of Oman. The study used exploratory research design to validate the proposed relationships described in the research objectives and to develop a reliable discussion extending to the findings attained. To achieve the research objectives, primary data were collected by surveying 329 staff working in three HEIs in Oman through a structured questionnaire. The finding of the study showed that organisational innovation has a positive impact on non-financial organisational performance.
Antibiotics-Resistant Bacteria Infection Prediction Based on Deep Learning

The World Health Organization (WHO) and other government agencies aroundthe world have warned against antibiotic-resistant bacteria due to abuse of antibiotics and are strengthening their care and monitoring to prevent infection. However, it is highly necessary to develop an expeditious and accurate prediction and estimating method for preemptive measures. Because it takes several days to cultivate the infecting bacteria to identify the infection, quarantine and contact are not effective to prevent spread of infection. In this study, the disease diagnosis and antibiotic prescriptions included in Electronic Health Records were embedded through neural embedding model and matrix factorization, and deep learning based classification predictive model was proposed. The f1-score of the deep learning model increased from 0.525 to 0.617when embedding information on disease and antibiotics, which are the main causes of antibiotic resistance, added to the patient’s basic information and hospital use information. And deep learning model outperformed the traditional machine hospital use information. And deep learning model outperformed the traditional machine learning models.As a result of analyzing the characteristics of antibiotic resistant patients, resistant patients were more likely to use antibiotics in J01 than nonresistant patients who were diagnosed with the same diseases and were prescribed 6.3 times more than DDD.
The Validity of Pop-up Book Media at Changing the Form of Objects Topic

The results of the analysis of the fifth-grade elementary school students' books in the second-semester Theme 7 and the results of observations in elementary schools state that the manuals used by teachers and students in the learning process are still limited especially in science material. The appearance of the book also less attractive, so students become less understanding of the material explained. In addition, the lack of use and development of media in the teaching and learning process also underlies the implementation of this research. The purpose of this research was to develop a pop-up book media on the topic of changing the form of objects with validity tested. This research was conducted using the ADDIE development model (analyze, design, development, implementation, evaluation). However, due to limited time, resources, and finances, the research was only carried out until the development stage. The subject in this research was the pop-up book media on the topic of changing the form of objects. Whereas the object of this research was the validity of the pop-up book media on the topic of changing objects. The method used in this study was a questionnaire given to two teachers and two lecturers as experts. The instrument used was a rating scale in the form of media assessment sheets for experts. The data analyzed with the mean formula to find out its validity. The average score of the pop-up book media after being reviewed by experts was 4.87 with very good qualifications. The implication of this research was the existence of pop-up book media that have very good qualifications. Based on the results of the analysis, the developed media was valid and can be used by the teacher to facilitate the teacher in explaining the material, especially on the topic of changing the form of objects in the fifth grade of elementary school.
The influence of multi cloud strategy

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Cyber Legislation and Cyber-Related Legal Issues in Bangladesh: Inadequacies and Challenges

Technology has inevitably evolved, driving significant human progress in every sphere of life. Amid this technological development, laws and regulations have been enacted to control offences relating to global advances in technology, including in Bangladesh. Bangladesh government has taken many initiatives to make the country digital. But in Bangladesh, the cyber-related laws are full of shortcomings and the customary laws are extremely outdated and most of the laws do not fit the era of ICT. Although Bangladesh government passed the ICT Act and the Digital Security Act to control cybercrime, the country's existing laws for fighting the menace are inadequate due to certain limitations. The article examines contemporary cyber legislation and legal issues in Bangladesh, together with the customary laws and existing policies, with a view to identifying their inadequacies and challenges. This article makes use of secondary data, i.e., books, journals, customary laws, acts etc.
Extracting useful reply-posts for text forum threads summarisation using quality features and classification methods

Text forums threads have a large amount of information furnished by users who discuss on a specific topic. At times, certain thread reply-posts are entirely off-topic, thereby deviating from the main discussion. It negatively affects the user's preference to continue replying to the discussion. Thus, there is a possibility that the user prefers to read certain selected reply-posts that provide a short summary of the topic of the discussion. The objective of the paper is to choose quality reply-posts regarding a topic considered in the initial-post, which also serve a brief summary. We offer an exhaustive examination of the conversational patterns of the threads on the basis of 12 quality features for analysis. These features can ensure selection of relevant reply-posts for the thread summary. Experimental outcomes obtained using two datasets show that the presented techniques considerably enhanced the performance in selecting initial-post replies pairs for text forum threads summarisation.
HUPM-MUO: High Utility Pattern Mining under Multiple Utility Objectives

Mining the pattern of interesting items plays a significant role in data analysis and decision-making strategies of real-time applications. Often the term 'interest' in pattern discovery denotes the frequency of the pattern. In recent research, the domain of data mining is considering the utility of the item instead of frequency, which indicates often profit. This manuscript argues that neither utility nor frequency of the itemset alone influence the target objective. Moreover, the profit is not only the utility factor of the itemset, apart from profit, the objectives like storage, saleability and other domain specific requirements can also be the utility factors. In regard to this argument, the manuscript endeavoured to define a novel model that discovers the top-k high utility patterns under multiple utility objectives (HUPM-MUO). The experimental study was carried on various datasets, which portray the performance advantage of the proposed model over the other contemporary models.
The Diffusion of New Entrepreneurial Practices Around the World: The Case of Initial Coin Offerings

We examine the cross-country diffusion of a new entrepreneurial practice, namely Initial Coin Offerings (ICOs). Drawing on institutional theory, we argue that governments (including their financial regulators), the media and academic institutions affect the institutional legitimacy of ICOs, thereby influencing their cross-country diffusion. Further, we hypothesize that ICO bans in one country have internationally mobile governance implications that affect the diffusion of ICOs in other countries. We introduce new quarterly data on ICO activity from 73 countries between 2013 and 2017 (inclusive), and present evidence consistent with our new hypotheses and institutional theory. Overall, we offer novel insights on the cross-country diffusion of ICOs, a new entrepreneurial practice, in an increasingly global and decentralized world.
Effects of Visuoauditory Stimuli on the Acoustic Features of Swallowing in the Elderly

Objective : To investigate the effect of audiovisual stimulation on swallowing sounds in the elderly. Method : Mirror therapy (MT) videos were prepared and divided into AMs, LMs, AFs, and LFs. Sixty videos were randomly selected from AMs, LMs, AFs, and LFs. The selected videos were divided into two sections (10 min per section). The control videos were extracted from the film "Le Peuple Migrateur." Finally, the TD (ms), TE (dB), DHE (ms), DHE/TD (%), PI (dB), DPI (ms), FPI (Hz), and PF (Hz) were analyzed. Result : TD of AS was significantly shorter than that of AS. Lower TE and PI were observed in AS compared to those observed in visual and auditory stimuli (VAS). DHE/TD and DPI were longer in AS relative to VAS. In addition, a lower FPI was observed in AS than in VAS. Conclusion : VAS can significantly improve swallowing frequencies, speed up swallowing movements and increase swallowing functional reserve in the elderly. In addition, the decreased swallowing efficacy under auditory stimuli could be reversed by visual stimuli.
A Practical Privacy-preserving Method in Federated Deep Learning

Although federated learning improves privacy of training data by exchanging model updates rather than raw data, many research results show that sharing the model updates may still involve risks. To alleviate this problem, many privacy-preserving techniques have been introduced to federated learning. However, considering deep learning models in federated learning, the resulting schemes either cannot implement non-linear activation functions well, or cannot remain the same model accuracy as the original training, or suffer from unaffordable costs. In this paper, we customize a \emph{practical privacy-preserving method for federated deep learning}, which is versatile and applicable to most state-of-the-art models, such as ResNet and DenseNet. In particular, this method can support non-linear activation functions well on the encrypted domain, hence supporting semi-trusted clients to efficiently train deep neural network locally over encrypted model iterates (i.e., protecting the privacy of the model for server-side). Meanwhile, it can be combined with the secret sharing technique to further ensure the semi-trusted server cannot obtain local gradients of each client (i.e., protect the privacy of training data for client-side). Detailed security analysis and extensive experiments demonstrate that the proposed method can achieve privacy-preservation without sacrificing model accuracy and introducing too much extra costs.
Jordanian Beauty Influencers and their Effect on Consumers

This paper will investigate the beauty influencers on social media platforms, their behaviours and attributes, and most importantly their effect on consumers. Our hypothesis is that the future of advertisement is social media, the traditional ways are fading, and this shift is facing resistance from the older generations. And with the appearance of advertisement through social media, influential users arose, creating this new phenomenon called 'influencers'. This research aims to investigate the dynamic relationship between the consumers and influencers, and how to control and maintain an ethical relation between any of the two parties most importantly, the influencer's effect on consumers and their behaviour. This study will be carried out through the use of primary research, where a questionnaires been distributed to as many Jordanian beauty consumers to see which influencers attract them the most, the level on influence they are under, and their behaviour in response to this influence.