Big Data and Cognitive Computing

14 pages, 1844 KiB

Open AccessArticle

An AI-Based Automated Continuous Compliance Awareness Framework (CoCAF) for Procurement Auditing

by Ke Wang, Michael Zipperle, Marius Becherer, Florian Gottwalt and Yu Zhang

Big Data Cogn. Comput. 2020, 4(3), 23; https://doi.org/10.3390/bdcc4030023 - 03 Sep 2020

Cited by 5 | Viewed by 6044

Compliance management for procurement internal auditing has been a major challenge for public sectors due to its tedious period of manual audit history and large-scale paper-based repositories. Many practical issues and potential risks arise during the manual audit process, including a low level [...] Read more.

Compliance management for procurement internal auditing has been a major challenge for public sectors due to its tedious period of manual audit history and large-scale paper-based repositories. Many practical issues and potential risks arise during the manual audit process, including a low level of efficiency, accuracy, accountability, high expense and its laborious and time consuming nature. To alleviate these problems, this paper proposes a continuous compliance awareness framework (CoCAF). It is defined as an AI-based automated approach to conduct procurement compliance auditing. CoCAF is used to automatically and timely audit an organisation’s purchases by intelligently understanding compliance policies and extracting the required information from purchasing evidence using text extraction technologies, automatic processing methods and a report rating system. Based on the auditing results, the CoCAF can provide a continuously updated report demonstrating the compliance level of the procurement with statistics and diagrams. The CoCAF is evaluated on a real-life procurement data set, and results show that it can process 500 purchasing pieces of evidence within five minutes and provide 95.6% auditing accuracy, demonstrating its high efficiency, quality and assurance level in procurement internal audit. Full article

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19 pages, 3738 KiB

Open AccessEditor’s ChoiceArticle

Keyword Search over RDF: Is a Single Perspective Enough?

by Christos Nikas, Giorgos Kadilierakis, Pavlos Fafalios and Yannis Tzitzikas

Big Data Cogn. Comput. 2020, 4(3), 22; https://doi.org/10.3390/bdcc4030022 - 27 Aug 2020

Cited by 15 | Viewed by 5589

Abstract

Since the task of accessing RDF datasets through structured query languages like SPARQL is rather demanding for ordinary users, there are various approaches that attempt to exploit the simpler and widely used keyword-based search paradigm. However this task is challenging since there [...] Read more.

Since the task of accessing RDF datasets through structured query languages like SPARQL is rather demanding for ordinary users, there are various approaches that attempt to exploit the simpler and widely used keyword-based search paradigm. However this task is challenging since there is no clear unit of retrieval and presentation, the user information needs are in most cases not clearly formulated, the underlying RDF datasets are in most cases incomplete, and there is not a single presentation method appropriate for all kinds of information needs. As a means to alleviate these problems, in this paper we investigate an interaction approach that offers multiple presentation methods of the search results (multiple-perspectives), allowing the user to easily switch between these perspectives and thus exploit the added value that each such perspective offers. We focus on a set of fundamental perspectives, we discuss the benefits from each one, we compare this approach with related existing systems and report the results of a task-based evaluation with users. The key finding of the task-based evaluation is that users not familiar with RDF (a) managed to complete the information-seeking tasks (with performance very close to that of the experienced users), and (b) they rated positively the approach. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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14 pages, 4711 KiB

Open AccessArticle

Data-Assisted Persona Construction Using Social Media Data

by Dimitris Spiliotopoulos, Dionisis Margaris and Costas Vassilakis

Big Data Cogn. Comput. 2020, 4(3), 21; https://doi.org/10.3390/bdcc4030021 - 19 Aug 2020

Cited by 25 | Viewed by 6134

Abstract

User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design [...] Read more.

User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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14 pages, 1997 KiB

Open AccessArticle

Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network

by Giuseppe Ciaburro

Big Data Cogn. Comput. 2020, 4(3), 20; https://doi.org/10.3390/bdcc4030020 - 17 Aug 2020

Cited by 33 | Viewed by 6061

Abstract

Parking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business revenue. Underground parking areas during off-peak hours [...] Read more.

Parking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business revenue. Underground parking areas during off-peak hours are uncrowded places, where user safety is guaranteed by company overseers. Due to the large size, ensuring adequate surveillance would require many operators to increase the costs of parking fees. To reduce costs, video surveillance systems are used, in which an operator monitors many areas. However, some activities are beyond the control of this technology. In this work, a procedure to identify sound events in an underground garage is developed. The aim of the work is to detect sounds identifying dangerous situations and to activate an automatic alert that draws the attention of surveillance in that area. To do this, the sounds of a parking sector were detected with the use of sound sensors. These sounds were analyzed by a sound detector based on convolutional neural networks. The procedure returned high accuracy in identifying a car crash in an underground parking area. Full article

(This article belongs to the Special Issue Big Data Analytics for Social Services)

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21 pages, 5532 KiB

Open AccessArticle

Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources

by Nehal K. Ahmed, Elsayed E. Hemayed and Magda B. Fayek

Big Data Cogn. Comput. 2020, 4(3), 19; https://doi.org/10.3390/bdcc4030019 - 06 Aug 2020

Cited by 11 | Viewed by 4700

Abstract

In this paper, we propose an unconstrained face verification approach that is dependent on Hybrid Siamese architecture under limited resources. The general face verification trend suggests that larger training datasets and/or complex architectures lead to higher accuracy. The proposed approach tends to achieve [...] Read more.

In this paper, we propose an unconstrained face verification approach that is dependent on Hybrid Siamese architecture under limited resources. The general face verification trend suggests that larger training datasets and/or complex architectures lead to higher accuracy. The proposed approach tends to achieve high accuracy while using a small dataset and a simple architecture by directly learn face’s similarity/dissimilarity from raw face pixels, which is critical for various applications. The proposed architecture has two branches; the first part of these branches is trained independently, while the other parts shared their parameters. A multi-batch algorithm is utilized for training. The training process takes a few hours on a single GPU. The proposed approach achieves near-human accuracy (98.9%) on the Labeled Faces in the Wild (LFW) benchmark, which is competitive with other techniques that are presented in the literature. It reaches 99.1% on the Arabian faces dataset. Moreover, features learned by the proposed architecture are used in building a face clustering system that is based on an updated version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). To handle the cluster quality challenge, a novel post-clustering optimization procedure is proposed. It outperforms popular clustering approaches, like K-Means and spectral by 0.098 and up to 0.344 according to F1-measure. Full article

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19 pages, 380 KiB

Open AccessArticle

See with Your Eyes, Hear with Your Ears and Listen to Your Heart: Moving from Dyadic Teamwork Interaction towards a More Effective Team Cohesion and Collaboration in Long-Term Spaceflights under Stressful Conditions

by Aggeliki Vlachostergiou, Andre Harisson and Peter Khooshabeh

Big Data Cogn. Comput. 2020, 4(3), 18; https://doi.org/10.3390/bdcc4030018 - 28 Jul 2020

Viewed by 4386

Abstract

The scientific study of teamwork in the context of long-term spaceflight has uncovered a considerable amount of knowledge over the past 20 years. Although much is known about the underlying factors and processes of teamwork, much is left to be discovered for teams [...] Read more.

The scientific study of teamwork in the context of long-term spaceflight has uncovered a considerable amount of knowledge over the past 20 years. Although much is known about the underlying factors and processes of teamwork, much is left to be discovered for teams who operate in extreme isolation conditions during spaceflights. Thus, special considerations must be made to enhance teamwork and team well-being for long-term missions during which the team will live and work together. Being affected by both mental and physical stress during interactional context conversations might have a direct or indirect impact on team members’ speech acoustics, facial expressions, lexical choices and their physiological responses. The purpose of this article is (a) to illustrate the relationship between the modalities of vocal-acoustic, language and physiological cues during stressful teammate conversations, (b) to delineate promising research paths to help further our insights into understanding the underlying mechanisms of high team cohesion during spaceflights, (c) to build upon our preliminary experimental results that were recently published, using a dyadic team corpus during the demanding operational task of “diffusing a bomb” and (d) to outline a list of parameters that should be considered and examined that would be useful in spaceflights for team-effectiveness research in similarly stressful conditions. Under this view, it is expected to take us one step towards building an extremely non-intrusive and relatively inexpensive set of measures deployed in ground analogs to assess complex and dynamic behavior of individuals. Full article

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27 pages, 4915 KiB

Open AccessEditor’s ChoiceArticle

MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

by Suriya Priya R. Asaithambi, Ramanathan Venkatraman and Sitalakshmi Venkatraman

Big Data Cogn. Comput. 2020, 4(3), 17; https://doi.org/10.3390/bdcc4030017 - 09 Jul 2020

Cited by 27 | Viewed by 9785

Abstract

Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes [...] Read more.

Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods. Full article

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20 pages, 4375 KiB

Open AccessEditor’s ChoiceArticle

TranScreen: Transfer Learning on Graph-Based Anti-Cancer Virtual Screening Model

by Milad Salem, Aminollah Khormali, Arash Keshavarzi Arshadi, Julia Webb and Jiann-Shiun Yuan

Big Data Cogn. Comput. 2020, 4(3), 16; https://doi.org/10.3390/bdcc4030016 - 29 Jun 2020

Cited by 8 | Viewed by 6201

Abstract

Deep learning’s automatic feature extraction has proven its superior performance over traditional fingerprint-based features in the implementation of virtual screening models. However, these models face multiple challenges in the field of early drug discovery, such as over-training and generalization to unseen data, due [...] Read more.

Deep learning’s automatic feature extraction has proven its superior performance over traditional fingerprint-based features in the implementation of virtual screening models. However, these models face multiple challenges in the field of early drug discovery, such as over-training and generalization to unseen data, due to the inherently unbalanced and small datasets. In this work, the TranScreen pipeline is proposed, which utilizes transfer learning and a collection of weight initializations to overcome these challenges. An amount of 182 graph convolutional neural networks are trained on molecular source datasets and the learned knowledge is transferred to the target task for fine-tuning. The target task of p53-based bioactivity prediction, an important factor for anti-cancer discovery, is chosen to showcase the capability of the pipeline. Having trained a collection of source models, three different approaches are implemented to compare and rank them for a given task before fine-tuning. The results show improvement in performance of the model in multiple cases, with the best model increasing the area under receiver operating curve ROC-AUC from 0.75 to 0.91 and the recall from 0.25 to 1. This improvement is vital for practical virtual screening via lowering the false negatives and demonstrates the potential of transfer learning. The code and pre-trained models are made accessible online. Full article

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Big Data Cogn. Comput., Volume 4, Issue 3 (September 2020) – 8 articles

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