Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intelligent Approach for Data Analysis and Decision Making in Big Data: A Case Study on E-commerce Industry

A recent informational phenomenon has emerged as one of the considerable innovations in information systems, commonly referred to as "Big Data". The latter is currently trendy, both in academia and industry, and is used to describe a wide range of concepts, from data extraction, storage, and management, to data processing and analysis using well-known schemas, to extract patterns in hidden relationships in order to make better decisions and to derive new knowledge using analytical techniques and solutions. The technology that enables the potential of big data to be exploited is called "Big Data Analytics". Big data analytics is a major challenge that enables researchers, analysts and business users to make better decisions faster. Big Data became an important part of marketing research and marketing strategies. The e-commerce industry is one of the industries that currently benefits most from the potential of big data collection and analysis. This paper therefore aims to demonstrate the use of big data to understand customers and to improve and facilitate the decision making process. In this research, we apply multiple machine learning (ML) models on large dataset present in the e-commerce area by studying several practical cases on online markets. Keywords—Big data; data analytics; decision making; big data analytics; big data analysis; machine learning; marketing; ecommerce
Federated Learning in the Lens of Crowdsourcing

The success of artiﬁcial intelligence (AI) is inseparable from large-scale and high-quality data, which is not always available. Involving human forces like crowdsourcing can help provide more training data and improve data quality for AI tasks. But with more privacy concerns and stricter laws, the data isolation problem is becoming worse, just when federated learning (FL) has emerged as a promising solution. In this article, we highlight the core issues in federated learning in the lens of crowdsourcing, including privacy and security, incentive mechanism, communication optimization and quality control. We expect to inspire the design of federated learning systems with existing crowdsourcing techniques. We also discuss emerging future challenges to implement a fully ﬂedged federated learning platform
Opinion Dynamics Optimization by Varying Susceptibility to Persuasion via Non-Convex Local Search

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.
Personalized Compatibility Metric Learning

Recommending sets of items that include both personalized and compatible items is crucial to personalized styling programs such as Amazon’s Personal Shopper. There is both an extensive literature on learning generic fashion compatibility and also on personalization in fashion. However, recommending pairs of items that the customer would like to wear together is still less studied as it involves learning a compatibility metric personalized to each customer. We propose a new framework (PSA-Net) to learn compatibility that is personalized to the customer - a customer dependent subspace learning framework where attention weights of subspaces are learnt using customer representations. We evaluate our approach on compatibility data provided directly by customers. Our approach outperforms the non-personalized approach in predicting compatibility preferences of customers. In other words, an approach that learns a common compatibility metric for all customers. In addition, we compare the significance of feedback collected directly from customers to that of data collected from human stylists in predicting compatibility for Amazon customers.
A Tree Approach for Solving the Exact Multiple String Matching Problem

In this study, we deal with the exact multiple string matching problem. Given a text T and a set of r patterns P 1 , P 2 , . , P r , find the ending positions of all sub-stringss in T which is equal to a pattern. By transforming all sub-strings of T into a reference tree such that each internal node stores a reference string, the exact multiple pattern matching problem can be efficiently solved by searching patterns in the tree via the guidance of the reference strings. We design elegant algorithms to construct the reference tree and to search patterns in the tree in this paper. The experiments involving problem instances from the DNA sequences and the English language are conducted to compare the performances of our approach and the well-known Burrows-Wheeler Transform (BWT) algorithm. The computational results demonstrate the advantage of our approach over BWT. In spite of the simplicity, the performance of our approach is quite efficient and robust.
A Survey on Swarm Intelligence Approaches for Feature Selection

One of the problems in Big Data is a large number of features or dimensions, which causes diﬃculties to apply machine learning, especially classiﬁcation algorithms. Feature selection is an important technique which selects small and informative feature subsets to improve the learning performance. However, feature selection is not an easy task due to its large and complex search space. Recently, swarm intelligence techniques have gained much attention from the feature selection community because of their simplicity and potential global search ability. There have been a few survey papers about applying swarm intelligence to feature selection. However, none of them provided a detailed and systematic discussion about how the search mechanisms (including both representation and updating mechanism) of swarm intelligence have been modiﬁed to solve feature selection problems eﬀectively. This paper presents a comprehensive survey of the state-of-the-art work on swarm intelligence for feature selection. The main focus of this paper is on analyzing the search mechanism of the proposed algorithms. The expectation is to provide an overview to researchers and encourage them to investigate more eﬀective search mechanisms for applying swarm intelligence to feature selection. At the end of the paper, several issues are presented for future research.
Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network

—Detection of anomalies from the medical image dataset improves prognosis by discovering new facts hidden in the data. The present study aims to discuss anomaly detection using autoencoders and convolutional neural networks. The autoencoder identifies the imbalance between normal and abnormal samples. They create learning models flexible and accurate on training data. The problem is addressed in four stages: 1) training: an autoencoder is initialized with the hyper-parameters and trained on the lung cancer CT scan images, 2) test: the autoencoder reconstructs the input from the latent space representation with a slight variation from the original data, indicated by a reconstruction error as Mean Squared Error (MSE), 3) evaluate: the MSE value of the training and test dataset are compared. The MSE values of anomalous data are higher than a base threshold, detecting those as anomalies, 4) validate: the efficiency metrics such as accuracy and MSE scores are used at both training and validation phases. The dataset was further classified as benign and malignant. The accuracy reported for outlier detection and the classification task is 98% and 97.2%. Thus, the proposed autoencoder-based anomaly detection could positively isolate anomalies from the CT scan images of lung cancer.
A Systematic Survey of Semantic Web Technologies for Bias in Artiﬁcial Intelligence Solutions

Bias in artiﬁcial intelligence (AI) is a critical and timely issue due to its sociological, economic and legal impact, as decisions made for humans by algorithms could lead to unfair treatment of certain individuals or groups of individuals. Multiple surveys have emerged to give a multidisciplinary overview of bias [1–3] or to review bias in applied areas such as social sciences [4–6], business research [7], criminal justice [8], or data mining [9–14]. Due to the capability of Semantic Web (SW) technologies to fulﬁl data validity gaps in many AI areas [15], we revise the extent to which they can contribute to bringing solutions to this problem. To the best of our knowledge, there exists no previous work to bring together bias and semantics, so we review their intersectionality following a systematic approach [16]. Consequently, we provide in-depth analysis and categorisation of different types and sources of bias addressed with semantic approaches and discuss their advantages to improve frequent limitations in AI systems. We ﬁnd works in the areas of information retrieval, recommendation systems, machine and deep learning, and natural language processing, and argue through multiple use cases that semantics can help especially dealing with technical, sociological
A Formal Framework for Maximum Error Estimation in Approximate Logic Synthesis

Approximate logic synthesis techniques have become popular in error-resilient systems, where accuracy requirements can be traded for improved energy efficiency. Many of these techniques operate on a circuit by substituting or removing some of its portions under a predefined error constraint; however, the research on systematic methods to determine the error induced by such transformations is still at an early stage. We propose herein a generic framework for modeling maximum error in a circuit, called partition and propagate, which is a fundamental preliminary step for ALS. This framework is based on circuit partitioning and error propagation among the subcircuits. We provide a sound, complete formal description of such framework, and we illustrate how two state-of-the-art algorithms can be subsumed by it. Moreover, we propose a novel gate-level error-modeling algorithm, which is able to identify the whole range of possible errors induced by a given approximate transformation. We compare the three strategies and illustrate the efficiency of the new error-propagation methodology, which is able to identify accurate error bounds and, hence, guide ALS techniques to more valuable solutions.
Leap Motion Controller for sign language recognition: A review of the literature

Sign language facilitates communication both within the community and between the deaf and the hearing. Sign language recognition can be used to ease the learning of sign language, or even to circumvent it altogether. This paper provides a review of the current literature of the use of the Leap Motion Controller (LMC) to interpret sign language and a comparison of methods and their resulting accuracies. The classification algorithms explored include: k-Nearest Neighbour, Support Vector Machine, Random Forests, Artificial Neural Networks, Bayesian algorithms, and Linear Discriminant Analysis. The most promising combination of variables, across different sign languages, seems to be the SVM with sequential feature selection.
Multivariate time series forecasting with diffusion kernels: Freeway traffic prediction

Forecasting multivariate time series is challenging as the variables are intertwined in time and space. Estimating such complex spatiotemporal correlations between variables is an important factor in achieving accurate prediction results. This paper shows that this complex relationship can be effectively estimated by a composition of simple linear functions in the case of traffic signals. Particularly, we propose to regularize the prediction model by the structural information of the transportation network with the help of graph heat diffusion kernels. We confirm by extensive experiments that our proposed simple model shows comparable or better forecasting performance to Deep Neural Network-based predictors with fewer free parameters.
Safe Elective Surgical Practice During COVID-19 Pandemic – A Prospective Observational Study of 303 Elective Surgeries in the UK

Aim During the COVID-19 pandemic, there has been worldwide cancellation of elective surgeries to protect patients from nosocomial viral transmission and peri-operative complications. With the unfolding situation, there is a definite need for an exit strategy to reinstate elective services. Therefore, more literature evidence supporting exit plans for elective surgical services is imperative to adopt a safe working principle. This study aims to provide evidence for safe elective surgical practice during the pandemic. Methodology This single centre, prospective, observational study included adult patients who were admitted and underwent elective surgical procedures in the trust’s COVID-free environment at the Birmingham Treatment Centre between May 19 and July 14, 2020. Data were collected on demographic parameters, peri-operative variables, surgical specialities, COVID-19 reverse transcription polymerase chain reaction (RT-PCR) testing results, post-operative complications and mortality. The study also highlighted the protocols it followed for the elective services during the pandemic. Results A total of 303 patients were included with mean age of 49.9 years (SD 16.5) comprising of 59% (178) female and 41% (125) male. They were classified according to the American Society of Anaesthesiologist Grade, different surgical specialities and types of anaesthesia used. Ninety-six percent (96%) of patients were discharged on the same day. Hundred percent (100%) compliance with pre-operative COVID-19 RT-PCR testing was maintained. There was no 30-day mortality or major respiratory complications. Conclusion Careful patient selection, simultaneous involvement of the pre-assessment and anaesthetic team, strict adherence to peri-operative protocols and delivering vigilant post-operative care for COVID-19 infection can help provide safe elective surgical services if the community transmission is under reasonable control. However, it is particularly important to maintain COVID-free safe environment for such procedures.
Improved Bit Allocation Algorithm for Multiview High Efficiency Video Coding

This paper, aiming at the problem of the traditional RD rate control model and inaccurate bit allocation of multi-view high-efficiency video coding (MV-HEVC), proposes a rate control algorithm for MV-HEVC based on the similarity analysis of the views and time-domain complexity of the frame activity. First, the algorithm core uses the similarity analysis of the views to reasonably perform bit allocation among the views. Then, the frame layer and basic unit layer bit allocation and rate control are performed based on the frame rate, target buffer capacity, actual buffer size, and active time domain complexity of the frame. The experimental results show that compared with the fixed bit allocation algorithm applied to views, the proposed method not only effectively controls the bit rate of multi-view video coding with a rate control accuracy of over 99% but also improves the PSNR by 0.38dB.
Insights into telomere protection by the Ku heterodimer

Im Menschen spielt der Ku Heterodimer eine essenzielle Rolle indem er das Ende der Telomere beschutzt. Er ist auch einer der Schlusselfaktoren im DNA Doppelstrangbruch Reparaturmechanismus “Non-Homologous end joining” (NHEJ). Der Schutz der Telomere, deren Instandhaltung und auch NHEJ sind wichtig fur die Genomintegritat eines Organismus, und Fehler in diesen Prozessen sind mit Krankheiten im menschlichen Organismus asoziiert. Die Funktion von Ku an den Telomeren ist erstaunlich, da nicht klar ist wie Ku das Telomer beschutzen kann, ohne NHEJ zu aktivieren, was zu Telomerfusionen fuhren wurde. In vielen Eukaryoten wird Ku benotigt um das Telomerende vor nukeolytischer Resektion, Rekombination und Fusion zu beschutzen. Es wurde vorgeschlagen, dass Ku das Telomereende beschutzt, indem es direkt an die DNA bindet. Um diese direkte DNA-Protein Interaktion zu testen, entwarf ich in vitro rekonstituierte humane und Arabidopsis thaliana Ku Komplexe mit Mutationen in konservierten DNA Resten die sich an der DNA-Protein Interaktionsoberflache befinden. Ich analysierte die DNA Bindekapazitat dieser mutanten Komplexe mit Hilfe biochemischer Verfahren und identifizierte Mutationen die die DNA Bindefahigkeit veranderten. Um die Signifikanz der Ku-DNA Bindung fur Telomerbiologie zu evaluieren, testete ich funkionelle Komplementationen von humanen Zellen und A. thaliana mit ausgesuchten, mutierten Komplexen. Die Daten, die ich von den komplementierten A. thaliana Mutanten erhielt, deuten darauf hin, dass eine direkte Interaktion von Ku mit DNA fur dessen Telomerfunktion notwendig ist. Weiters, schlage ich vor, dass die Ku-DNA Bindungsaktivitiat, im Hinblick auf telomerisches Chromatin, durch Telomerbindeproteine (TBPs) modifiziert wird. Allerdings sind die TBPs in Pflanzen noch nicht identifiziert worden, wahrscheinlich auf Grund von genetischer Redundanz in hoheren Pflanzen. Um diese TBPs zu bestimmen, untersuchte ich das Genom der einzelligen Grunalge Chlamydomonas reinhardtii und charakterisierte auf biochemische Weise eine putatives TBP. Eine weitere Analyse dieses Gens konnte helfen, die TBPs in der Pflanzenlinie zu identifizieren.
Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout

Generating annotated pairs of realistic tissue images along with their annotations is a challenging task in computational histopathology. Such synthetic images and their annotations can be useful in training and evaluation of algorithms in the domain of computational pathology. To address this, we present an interactive framework to generate pairs of realistic colorectal cancer histology images with corresponding tissue component masks from the input gland layout. The framework shows the ability to generate realistic qualitative tissue images preserving morphological characteristics including stroma, goblet cells and glandular lumen. We show the appearance of glands can be controlled by user inputs such as number of glands, their locations and sizes. We also validate the quality of generated annotated pair with help of the gland segmentation algorithm.
Eﬃcient and Accessible Experimental Designs for Discrete Choice Experiments: DCEtool

In recent years, computational methods have been employed for the construction of eﬃcient discrete choice experiments. However, many of these methods are only available on paid software or free software without a user-friendly interface. This paper introduces DCEtool, a free app coded in shiny R to make good design properties accessible to new and experienced researchers.
Study of crosslinked copolymer nanospheres with temperature resistance, salinity resistance, and deep profile control

Three crosslinked copolymer nanospheres were prepared by inverse microemulsion polymerization. Resistance temperature and resistance salinity of the copolymer CP(AM-AA-AMPS) (cross-linked poly(acrylamide-acrylic acid-2-acrylamide-2-methylacrylic sulfonic acid) nano-spheres were superior to the CPAM (cross-linked polyacrylamide) nano-spheres and CP(AM-AA) (cross-linked poly(acrylamide-acrylic acid) nano-spheres. The nanospheres had a tendency to plug the high-permeability layer and drive crude oil from the low-permeability layer. Meanwhile, these nanospheres displaced crude oil that existed in the pores or throats when they were conveyed in porous media. Consequently, deep profile control and oil displacement could be entirely actualized, and the ultimate goal of enhanced oil recovery could be achieved for high-temperature and high-salinity reservoirs. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 000, 45131.
High Frequency Response of Auto-Ignition and Heat Release to Acoustic Perturbations

This work is related to the thermoacoustic stability in the high frequency range of auto-igniting, turbulent flames. Analytical and numerical methods are employed to study the flame response, in particular to transverse acoustic perturbations. A superposition of different flame feedback mechanisms is observed and characterised using a novel mathematical description of local heat release rate fluctuations.
CHALLENGING ENGINEERING STUDENTS WITH UNCERTAINTY IN A VUCA SITUATION

Engineering practice is gradually becoming more affected by the need for agility, and the dynamic nature of today’s world has impacted how situations are addressed, projects are managed and decisions are made. Anticipating a rapidly changing world, future engineers must have competence to manage situations which may be volatile, uncertain, complex and ambiguous (VUCA). Engineering education programs may therefore want to consider instilling some VUCA aspects into their training, aiming to prepare graduates to confront unexpected situations in the context of decision-making and leadership as recommended in the CDIO Syllabus 2.0. The engineering programs at Reykjavik University (RU) have for several years run a two day “Disaster Week” early in the first semester, an event were students are faced with a disaster of some sort. In the fall semester event 2017 it was decided to analyze by a survey carried out at the completion of the event, the VUCA dimensions of the event and the dynamics within the teams. The participants were a group of 230 first-year students, working in 40 teams. This study shows that challenging engineering students with uncertainty in the VUCA-spirit is a good way to both train and instill a positive view towards teamwork among students, and may lead to a more confident and positive attitude when faced with volatile and uncertain tasks later in their studies.