Miracle-dz/UniFAR-FTUs huggingface dataset:
Bottom-up Discovery of Context-aware Quality Constraints for Heterogeneous Knowledge Graphs

As knowledge graphs are getting increasingly adopted, the question of how to maintain the validity and accuracy of our knowledge becomes ever more relevant. We introduce context-aware constraints as a means to help preserve knowledge integrity. Context-aware constraints offer a more fine-grained control of the domain onto which we impose restrictions. We also introduce a bottom-up anytime algorithm to discover contextaware constraint directly from heterogeneous knowledge graphs—graphs made up from entities and literals of various (data) types which are linked using various relations. Our method is embarrassingly parallel and can exploit prior knowledge in the form of schemas to reduce computation time. We demonstrate our method on three different datasets and evaluate its effectiveness by letting experts on knowledge validation and management assess candidate constraints in a real-world knowledge validation use case. Our results show that overall, context-aware constraints are to an extent useful for knowledge validation tasks, and that the majority of the generated constraints are well balanced with respect to complexity.
Detecting IoT Botnet Formation using Data Stream Clustering Algorithms

The Internet of Things has gained much importance nowadays due to its applicability to many ecosystems on day-to-day use. However, these embedded systems have several hardware constraints, and theses device’s security has been neglected. Consequently, botnets malwares have taken advantage of poor security schemas on these devices. This paper proposes unsupervised machine learning using data streams to detect the botnet formation on the edge of the network. The results obtained by the algorithm includes an average of 98.43% accuracy and taking about 20.07 ms to evaluate each sample from the stream, making it reliable and fast, even in a more constrained device, such as Raspberry Pi 3 B+.
Molecular epidemiology and the clinical impact of carbapenemase-producing Enterobacterales isolates among adult patients: aspects from a Romanian non-teaching hospital

Abstract Introduction: A dramatic increase of infections induced by carbapenemase-producing Enterobacterales (CPE) has been registered worldwide. The aim of this study was to evaluate the molecular epidemiology and the clinical impact of CPE strains isolated from adult inpatients. Material and methods: A one-year, single-center, retrospective observational study including 34 consecutive patients with 37 non-duplicate CPE strains recovered from clinical specimens was accomplished. The Vitek 2 Compact, M.I. C.Evaluator strips, the modified carbapenem inactivation method (mCIM), and the combination disks test (KPC, MBL, OXA-48 Confirm kit, Rosco Diagnostica) were applied as phenotypic tests. A multiplex polymerase chain reaction (PCR) assay was used for detection of blaKPC, blaNDM, and blaOXA-48-like genes. The clonality was assessed with pulsed-field gel electrophoresis (PFGE). Results: Klebsiella pneumoniae (n=25) was the most frequent CPE encountered. The carbapenemase types were NDM (n=13), KPC (n=12), and OXA-48-like (n=12). Two distinct clonal clusters were identified among the 12 KPC positive strains. All CPE isolates exhibited non-susceptibility to carbapenems, cephalosporins, ciprofloxacin. Respiratory tract infections (n=16) and hospitalization in the intensive care unit (ICU) (n=14) were dominant. The most common comorbidity was congestive heart failure (n=11). Monotherapy was the main strategy adopted (n=15). Death occurred in 18 patients. Conclusions: Our analysis underscores the scarcity of antibiotic solutions and high mortality. Monotherapy for urinary tract infections (UTIs) is beneficial. Inter- or intrahospital dissemination of successful epidemic clones is proved. The adequate CPE infections control programs and antimicrobial policies are essential.
A Robust Optimization Approach for Designing FTTx Networks Integrating Free Space Optics under Weather Uncertainty

We address the problem of designing a 3-architecture telecommunication access networks that combines optical fiber- and copper-based access technologies with Free Space Optics (FSO) technology. We propose a mixed integer linear programming model for representing the optimal design problem, which can be traced back to a multi-architecture variant of the well-known Connected Facility Location problem. In order to protect design solutions against reduction in capacity that may occur in FSO connections due to adverse weather conditions, we adopt a robust cutting plane procedure. Preliminary computational tests are reported to show the ability of our robust optimization approach to produce weather-resistant design solutions.
What's in a Definition? An Investigation of Semantic Features in Lexical Dictionaries

: Encoding and generating word meanings as short deﬁnitions for user- and machine-consumption dictionaries is still a usually adopted strategy within interpretable lexical-semantic resources. Deﬁnitions have the property of being natural to be created by humans, while several techniques have been proposed for the automatic extraction from large corpora. However, the reversed process of going back to the words (i.e., onomasiological search) is all but simple for both humans and machines. Indeed, deﬁnitions show context- and conceptual-based properties which inﬂuence their quality. In this contribution, I want to draw the attention to this problem, through a simple content-to-form experimentation with humans in the loop. The results give some ﬁrst insight on the relevance of the problem from a computational perspective. In addition, I analyzed (both quantitatively and qualitatively) a set of 1,901 word deﬁnitions taken from different sources, towards the modeling of features for their generation and automatic extraction.
Relationships of mass properties and body proportions to locomotor habit in terrestrial Archosauria

Abstract. Throughout their 250 Myr history, archosaurian reptiles have exhibited a wide array of body sizes, shapes, and locomotor habits, especially in regard to terrestriality. These features make Archosauria a useful clade with which to study the interplay between body size, shape, and locomotor behavior, and how this interplay may have influenced locomotor evolution. Here, digital volumetric models of 80 taxa are used to explore how mass properties and body proportions relate to each other and locomotor posture in archosaurs. One-way, nonparametric, multivariate analysis of variance, based on the results of principal components analysis, shows that bipedal and quadrupedal archosaurs are largely distinguished from each other on the basis of just four anatomical parameters (p < 0.001): mass, center of mass position, and relative forelimb and hindlimb lengths. This facilitates the development of a quantitative predictive framework that can help assess gross locomotor posture in understudied or controversial taxa, such as the crocodile-line Batrachotomus (predicted quadruped) and Postosuchus (predicted biped). Compared with quadrupedal archosaurs, bipedal species tend to have relatively longer hindlimbs and a more caudally positioned whole-body center of mass, and collectively exhibit greater variance in forelimb lengths. These patterns are interpreted to reflect differing biomechanical constraints acting on the archosaurian Bauplan in bipedal versus quadrupedal groups, which may have shaped the evolutionary histories of their respective members.
Rapid Development of a Low-cost Web-based 360 Virtual Tour

The use of 360-degrees Virtual Tour (VT) is a common practice in the education and tourism sector. It has recently gained popularity given the benefits of bringing physical spaces into a 360-degrees experience that can be explored in a simple and intuitive manner. Due to Covid-19, many Organisations wish to utilise immersive 360 technologies but many cannot afford it. In this paper, a 360-degree VT pipeline is proposed to allow Organisations to develop a VT that can compete in functionality with other sophisticated VT. Users with minimal coding experience are able to develop a low-cost web-based VT using a 360 camera, the Open Source tool Marzipano, the developed software framework and documentation from the GitHub repository. The contribution of this work is both the software framework, which complies with the Web Content Accessibility Guidelines (WCAG), for use with the Marzipano tool and a University case study along with a user evaluation to demonstrate the effectiveness of the approach. The usability evaluation run with the stakeholders demonstrates the acceptance of this 360 experience to allow new students to get a 360-degrees view of the GCU Glasgow Campus.
Incidence of Wohlbachia, cardinium, spiroplasma and Phage Wo in Different Geographical Populations of Chilo suppressalis (Lepidoptera: Pyralidae) from China

ABSTRACT: Using specific primers, we measured the infection status of Wolbachia, WO, Cardinium, and Spiroplasma in Chilo suppressalis (Walker) (Lepidoptera: Crambidae) collected from 14 locations in China, and conducted phylogenetic analysis of the three endosymbionts. The results showed that eight geographical populations of C. suppressalis were infected by Wolbachia. The infection rates of Wolbachia in four geographical populations had no significant difference between 2011 and 2012 (P>0.05). There were five geographical populations infected with Cardinium and seven geographical populations infected with Spiroplasma. Seven populations of C. suppressalis carrying Wolbachia were also infected with WO. The maximum parsimony tree shows that Wolbachia from C. suppressalis, Pieris rapae, Coliaserate poliographus and Tetranychus urticae fall into the same clade. Cardinium from C. suppressalis joins together with Cardinium from Oppiella nova. Spiroplasma in C. suppressalis has a close relationship with that of Fannia manicata. In conclusion, infections of three endosymbionts showed regional differences, and there were eight geographical populations infected with more than one endosymbiont. Our study found, for the first time, double infection with Wolbachia and Spiroplasma in one sample from Xinzhou. Phylogenetic analysis of the three endosymbionts proved that the endosymbionts can transfer horizontally between different individuals of the same or different hosts.
Development and Test of a New Concept of Interactive Front Counter Designed to Enhance User Experience

: The front counter is the physical contact point between customers and service providers. By embedding technology in this piece of furniture in an aesthetic and an ergonomic design, surprising forms of interaction can be achieved. The combination of smart communicating devices and wood as the main material for integrated interfaces creates a unique atmosphere that can enhance user experience. This paper presents the design of an innovative, modular, low cost, interactive, smart front counter. This system provides customers with personalized information according to their profile, needs and tastes. At the same time, service and/or product providers can collect and visualize data concerning customers in real-time. With the contribution of professionals in woodcraft manufacturing and industrial design, a prototype of interactive front counter integrating a tactile wooden board, a gestural interface, and a wood screen has been developed and tested in the enotourism context with a group of 75 people. The counter was positively evaluated. Results show that the interaction with such a system can provide an attractive and valuable support for both customers and service providers.
Identifying Botnets by Analysing Twitter Traffic during the Super Bowl

Detecting accounts broadcasting illegal contents at sporting events in social networks is an important problem of difficult solution, since the traditional intrusion detection systems are not effective in online social networks due to the speed with which these kind of messages and contents spread. Thus, there is an increasing need for an adequate and efficient detection system of the so-called botnets used for the distribution of illegal contents on online social networks. In this paper we propose using well-known classification methods to analyse the activity of Twitter accounts in order to identify botnets. We have analysed the Twitter conversations that include hashtags related to the Super Bowl LIII (February 3, 2019). The objective is to identify the behaviour of various types of profiles with automatic and non-standard spamming activities. In order to do so, a dataset from public data available on Twitter that includes content published by human-managed accounts and also by bots that used hashtags related to the event has been collected. This dataset has been manually labelled to create a training set with tweets posted by humans and bots active in Twitter. As a result, several types of profiles with non standard activities have been identified. Also, some groups of accounts have been identified as botnets that were activated during the Super Bowl LIII (2019).
Comparative Analysis between the k-means and Fuzzy c-means Algorithms to Detect UDP Flood DDoS Attack on a SDN/NFV Environment

Distributed Denial of Service (DDoS) attacks are a growing issue for computer networks security and have become a serious network security problem. Environments based on Software Defined Networking (SDN) and Network Function Virtualization (NFV) offers the ability to program a network and allows dynamic creation of flow policies. Allied to that, clustering algorithms can be used to classify and detect DDoS. This paper presents a study and an analysis of two unsupervised machine learning algorithms used to detect DDoS attacks in an SDN/NFV simulated environment. The results obtained by the two algorithms include an accuracy rate of 99% and the k-means algorithm was 33% faster than fuzzy c-means, which demonstrates its effectiveness and
Supporting Named Entity Recognition and Document Classification in a Knowledge Management System for Applied Gaming

: In this research paper, we present a system for named entity recognition and automatic document classiﬁcation in an innovative knowledge management system for Applied Gaming. The objective of this project is to facilitate the management of machine learning-based named entity recognition models, that can be used for both: extracting different types of named entities and classifying textual documents from heterogeneous knowledge sources on the Web. We present real-world use case scenarios and derive features for training and managing NER models with the Stanford NLP machine learning API. Then, the integration of our developed NER system with an expert rule-based system is presented, which allows an automatic classiﬁcation of textual documents into different taxonomy categories available in the knowledge management system. Finally, we present the results of a qualitative evaluation that was conducted to optimize the system user interface and enable a suitable integration into the target system.
Neural Semantic Pointers in Context

: Resolving linguistic ambiguities is a task frequently called for in human communication. In many cases, such task cannot be solved without additional information about an associated context, which can be often captured from the visual scene referred by the sentence. This type of inference is crucial in several aspects of language, communication in the ﬁrst place, and in the grounding of language in perception. This paper focuses on the contextual effects of visual scenes on semantics, investigated using neural computational simulation. Speciﬁcally, here we address the problem of selecting the interpretation of sentences with an ambiguous prepositional phrase, matching the context provided by visual perception. More formally, provided with a sentence, admitting two or more candidate resolutions for a prepositional phrase attachment, and an image that depicts the content of the sentence, it is required to choose the correct resolution depending on the image’s content. From the neuro-computational point of view, our model is based on Nengo, the implementation of Neural Engineering Framework (NEF), whose basic semantic component is the so-called Semantic Pointer Architecture (SPA), a biologically plausible way of representing concepts by dynamic neural assemblies. We evaluated the ability of our model in resolving linguistic ambiguities on the LAVA (Language and Vision Ambiguities) dataset, a corpus of sentences with a wide range of ambiguities, associated with visual scenes.
Internal and External Factor Analysis in Bottleneck Detection in Shop Sales: The Case of Grocery Shops in Lithuania

: The optimization of supermarket processes as well as the increase in productivity and proﬁtability of shop sales requires extensive knowledge of bottlenecks within the sales processes as bottlenecks limit the capacity of shop sales. Bottlenecks refer to bursty processes in analogy to the occurrence of bit-errors in data transmission systems. The aim of the paper is to analyse external and internal factors in shop sales underpinning the examination of external and internal factors in shop sales based on the collected data of two supermarkets in Lithuania. In this context, concentrated arrival of customers is identiﬁed as an external factor. By internal factors, the buyers’ waiting time in the queue to the cash register as well as the payment processing time at the cash register are meant. In this work the internal factors of the payment process are modelled by gap processes where the obtained parameters such as the buyers’ concentration and the buyers’ probability allow a good comparison of the payment related processes. This work aims at achieving customer quality improvement through prevention of queuing. The obtained results show that the waiting time in the queue to the cash register is quite bursty whereas the payment processing time at the cash register is quite regularly distributed. Therefore, the conclusion can be drawn that at the cash register short periods of high activities are followed by longer periods of inactivity.
Toward Efficient Block Replication Management in Distributed Storage

Distributed/parallel file systems commonly suffer from load imbalance and resource contention due to the bursty characteristic exhibited in scientific applications. This article presents an adaptive scheme supporting dynamic block data replication and an efficient replica placement policy to improve the I/O performance of a distributed file system. Our goal is not only to yield a balanced data replication among storage servers but also a high degree of data access parallelism for the applications. We first present mathematical cost models to formulate the cost of data block replication by considering both the overhead and reduced data access time to the replicated data. To verify the validity and feasibility of the proposed cost model, we implement our proposal in a prototype distributed file system and evaluate it using a set of representative database-relevant application benchmarks. Our results demonstrate that the proposed approach can boost the usage efficiency of the data replicas with acceptable overhead of data replication management. Consequently, the overall data throughput of storage system can be noticeably improved. In summary, the proposed replication management scheme works well, especially for the database-relevant applications that exhibit an uneven access frequency and pattern to different parts of files.
Organizational Capabilities in Data-driven Value Creation: A Literature Review

: Prior research has shown that organizational capabilities can help enhance competitive advantages. However, organizational capabilities are also required to develop in order to keep up with the dynamic environment or changing business environment. Over the last decade, data has been introduced as a new business resource in organizations, which brings both new opportunities and challenges in data-driven value creation in organizations. There is a need to understand what organizational capabilities are needed in achieving data-driven value creation for organizations. This study conducts an exploratory literature review to understand what organizational capabilities are related to data-driven value creation. Based on the selected 14 articles from the literature between years 2016 and 2020, the literature review shows that analytics capabilities are the most important capabilities in data-driven value creation together with other different capabilities. There is more room for research with regards to organizational capabilities in data-driven value creation, such as to examining what are the components of analytics capabilities and how organizations should combine different organizational capabilities for data-driven value creation.
On Improving Toll Accuracy for COVID-like Epidemics in Underserved Communities Using User-generated Data

This paper envisions using user-generated data as a cheap way to improve accuracy of epidemic tolls in underserved communities. The global widespread of COVID-19 pandemic has imposed several unprecedented challenges. One of these challenges is constantly monitoring the unprecedented epidemic widespread at a fine-granular spatial scale, so experts can model, understand, and prevent disease transmission and field personnel can reach and treat infected people. Unfortunately, the limited resources compared to the pandemic widespread has led to a significant number of unreported cases in underserved communities and developing countries, including a large number of severe cases. We propose in this paper enhancing epidemic case reporting in underserved communities through exploiting the power of data that are posted by people on web. Our vision is building a data analysis pipeline that filters and categories use-generated data objects to provide informal estimates for tolls in unreachable regions and enhance estimates in other regions. The pipeline consist of five stages, that starts with filtering epidemic-specific data to visualize advanced aggregates to end users. We also discuss several technical challenges that face different stages of the pipeline.
On the Relationship between Seasons of the Year and Disaggregation Performance

This paper pursues the question of how seasons of the year affect disaggregation performance in Non-Intrusive Load Monitoring. To this end, we select the dishwasher, a common household appliance that may exhibit usage cycles depending on the user. We utilize an auto-correlation function to detect usage patterns of dishwashers in each season. Then, we examine the dissimilarity across each season with the help of the Keogh Lower Bound measure. Finally, we conduct a disaggregation study using the REFIT dataset and relate the outcome to the dissimilarity across seasons. Our findings indicate that in cases where energy consumption shows similarity throughout seasons, the performance of load disaggregation approaches can be positively affected.
Target Evaluation for Neural Language Model using Japanese Case Frame

: Automatic text generation are widely used in various type of natural language processing systems. It is crucial to capture correct grammar for these systems to work. According to the recent studies, neural language models successfully acquire English grammar. However, it’s not thoroughly investigated why the neural language models work. Therefore, fine-grained grammatical or syntactic analysis is important to assess neural language models. In this paper, we constructed grammatical evaluation methods to assess Japanese grammatical ability in neural language models by adopting a target evaluation approach. We especially focus on case marker and verb match in Japanese case grammar. In experiments, we report the grammatical ability of neural language model by comparing n-gram models. Neural language model performed better even some information lacks, while n-gram performs poorly. Also, Neural language model exhibited more robust performance for low frequency terms.