Miracle-dz/UniFAR-FTUs huggingface dataset:
The importance of accountability in IT governance practice in the public sector : a case study of the Kingdom of Bahrain

The term IT Governance (ITG) is widely understood to be inherited from the discipline of corporate governance and represents the way organizations structure and manage IT resources. Researchers in the discipline of ITG have shown a link between effective ITG and organizational performance. However, other research efforts continue to develop models of ITG. Another research area has focused on ITG concepts and dimensions, namely: structure, process and people. Yet, little research has addressed the implementation of ITG stage leaving a clear gap between the theoretical musings, real-life and contemporary practice. A number of researchers have reported that the adoption of ITG is believed to improve organisational accountability, thereby resulting in return on investments. Accountability is an important part of ITG especially since public sector organizations are non-profit and IT projects are considered important. For the purpose of conducting this research, a review of the literature in ITG has been initiated to shape the intended theoretical background of the study. The academic literature conducted formalized a richer view to the ITG concept. The study investigated ITG practice using multiple case studies from public sector organisations based in the Kingdom of Bahrain. The research is also focused on gaining an in-depth insight to evaluate ITG practices. A facilitative framework has been adapted for mapping ITG areas introduced by the ITG Institute using the COBIT framework to structure the research tool. Research results are classified into most significant (mature) and weakest processes to provid a better understanding of the gaps available within ITG practice in public sector organisations covered in this study. The researcher found that IT structure in public sector organisations needs revisting to enable to better disseminating of IT activitities, therefore lead into promoting an accountability culture through definite roles and responsibilities. In addition, the researcher pointed to the enacting forces and the importance of IT-related laws in defining internal controls and protecting the organization’s assets from IT-related risks. In this research, SHIP-ITG model and its maturity criteria represents a contribution of this research study for both practitioners to use and researchers; for instance, this allows the organization to gain a better perspective on ITG processes and provides a clear focus for management. The SHIP-ITG model also forms a basis for further research in ITG adoption models and bridges the gap between conceptual frameworks, real life and functioning governance.
Analysis of bridge failure due to Cyclone Marcia in Central Queensland using fault tree method

Over the past few years Queensland has suffered from a number of severe tropical cyclones, the most recent one being Marcia, that took place on 20th of February 2015. Damage bill of Marcia exceeded $50 million which included cost of repairing a number of damaged bridges. Failure of road infrastructure isolates communities from accessing essential services and commodities. This necessitated an urgent need to develop a systematic method of assessing the failure of the bridge component to improve the resilience of future bridges and provide base knowledge for developing emergency maintenance response. There are several methods available to investigate the bridge failure. Fault tree analysis (FTA) was selected considering its positive attributes over other methods. FTA was used to estimate the probabilities of failure of main components (Super Structure and Sub Structure) and elements of timber and concrete bridges. Secondary data (Level 1 and level 2 bridge inspection reports from the department of transport and main roads) before and after the cyclone Marcia were used in conjunction with expert consultations to construct fault trees for both timber and concrete bridges. Results indicated potential failure mechanisms and the degree of susceptibility of main components of timber and concrete bridges to cyclonic events. However, the extent of the data was not adequate to draw firm conclusions and further studies (i.e. probabilistic models) are recommended to strengthen the understanding of the complete dynamics of the bridge failure under cyclonic event.
Genealogical inference of closely related species based on microsatellites

Despite their unmatched popularity in many research areas, microsatellites have not yet become a major tool for the inference of genealogical relationships of closely related species. Recent studies have successfully extended the repertoire of microsatellite analysis beyond population genetics and demonstrate that phylogenetic relationships of closely related species can be inferred accurately with fewer loci than previously assumed.
Automated Assertion Generation via Information Retrieval and Its Integration with Deep learning

Unit testing could be used to validate the correctness of basic units of the software system under test. To reduce manual efforts in conducting unit testing, the research community has contributed with tools that automatically generate unit test cases, including test inputs and test oracles (e.g., assertions). Recently, ATLAS, a deep learning (DL) based approach, was proposed to generate assertions for a unit test based on other already written unit tests. Despite promising, the effectiveness of ATLAS is still limited. To improve the effectiveness, in this work, we make the first attempt to leverage Information Retrieval (IR) in assertion generation and propose an IR-based approach, including the technique of IR-based assertion retrieval and the technique of retrieved-assertion adaptation. In addition, we propose an integration approach to combine our IR-based approach with a DL-based approach (e.g., ATLAS) to further improve the effectiveness. Our experimental results show that our IR-based approach outperforms the state-of-the-art DL-based ap-proach, and integrating our IR-based approach with the DL-based approach can further achieve higher accuracy. Our results convey an important message that information retrieval could be competitive and worthwhile to pursue for software engineering tasks such as assertion generation, and should be seriously considered by the research community given that in recent years deep learning solutions have been over-popularly adopted by the research community for software engineering tasks.
Methodology to Compare Twitter Reaction Trends between Disinformation Communities, to COVID related Campaign Events at Different Geospatial Granularities

With still ongoing COVID pandemic, there is an immediate need for a deeper understanding of how Twitter discussions (or chatters) in disinformation spreading communities get triggered. More specifically, the value is in monitoring how such trigger events in Twitter discussion do align with the timelines of relevant influencing events in the society (indicated in this work as campaign events). For campaign events in regards to COVID pandemic, we consider both NPI (Nonpharmaceutical Interventions) campaigns and disinformation spreading campaigns together. In this short paper we have presented a novel methodology to quantify, compare and relate two Twitter disinformation communities, in terms of their reaction patterns to the timelines of major campaign events. We have also analyzed these campaigns at their three geospatial granularity contexts: local county, state, and country/ federal. We have conducted a novel dataset collection on campaigns (NPI + Disinformation) at these different geospatial granularities. Then, with collected dataset on Twitter disinformation communities, we have performed a case study to validate our proposed methodology.
Conformation traits associated with production and milk composition of Holstein cows

The association between type conformation systems and milk (PL), fat (PG) and protein (PP) production was evaluated through the correlation between conformation traits (CC) and genetic value predictions for milk (HTPL), fat (HTPGK) and protein (HTPPK) of Holstein cattle, to determine the extent to which CC influence animal production. The study included 31,711 CC, 87,871 MP, 65,593 PG and 43,717 PP records. The CC were grouped into four general systems: structure and capacity (SEC), feet and hooves (SP), udder (SU) and haunch (SA). The effect of end point (FP) classes on MP, PG and PP levels was also evaluated through analysis of variance. HTPL had a correlation of 0.28 with SU, 0.17 with SA, 0.08 with SP and -0.05 with SEC (P<0.001). The correlations of HTPGK and HTPPK with the different systems were low, highlighting those found with SU, 0.15 and 0.21 (P<0.001), respectively. The phenotypic associations and correlations of genetic values reported in this study suggest that selection of cows by CC can modify milk, fat and protein production.
Do Not Read the Same News! Enhancing Diversity and Personalization of News Recommendation

Personalized news recommendation by machine is one of the widely studied areas. As the production of news articles increases and topics are diversified, it is impractical to read all the articles available to users. Therefore, the purpose of the news recommendation system should be to provide relevant news based on the user’s interest. Unlike other recommendation systems, explicit feedback from users on each item such as ratings is rarely provided in news recommendation systems. Most news recommendation systems use implicit feedback such as click histories to profile user interest, which leads to biased recommendation results towards generally popular articles. In this paper, we suggest a novel news recommendation model for more personalized recommendations. If a user reads news not widely clicked by others, the news reflects the user’s personal interest rather than other popular news clicked. We implement two user encoders, one to encode the general interest of the set of users and another one to encode the user’s individual interest. We also propose regularization methods that induce two encoders to encode different types of user interest. The experiment on real-world data shows that our proposed method improves the diversity and the quality of recommendations for different click histories without any significant performance drops.
ScholarWorks at WMU ScholarWorks at WMU Outcomes of Music Improvisation Experiences: A Scoping Review Outcomes of Music Improvisation Experiences: A Scoping Review

Music improvisation is a therapeutic music intervention utilized frequently by board-certified music therapists in various clinical settings. While music therapists frequently use music improvisation as a treatment intervention, research on music improvisation outcomes is limited. The purpose of this scoping review is to summarize current knowledge about the outcomes of music improvisation experiences in music therapy and non-music therapy contexts. This review aims to identify and address gaps in the literature to provide suggestions for future research. The findings of this review will inform clinical music therapy methods. To conduct the scoping review, the researcher used the following five-stage framework: identifying the research question, identifying relevant studies, charting the data, summarizing results, and discussing results. The analysis of twenty-nine studies on music improvisation resulted in a summary and discussion of various music improvisation outcomes, including neurologic, physiological, social and relational, emotional, communicative, phenomenological findings, additional unique outcomes, implications for music therapy, and suggestions for future research.
A Sampling Approach based on Set Coverage Algorithm

: Massive amounts of business process event logs are collected and stored by modern information systems. Model discovery aims to discover a process model from such event logs, however, most of the existing approaches still suffer from low efficiency when facing large-scale event logs. Event log sampling techniques provide an effective scheme to improve the efficiency of process discovery, but the existing techniques still cannot guarantee the quality of model mining. Therefore, a sampling approach based on set coverage algorithm named set coverage sampling approach is proposed. The proposed sampling approach has been implemented in the open-source process mining toolkit ProM . Furthermore, experiments using a real event log data set from conformance checking and time performance analysis show that the proposed event log sampling approach can greatly improve the efficiency of log sampling on the premise of ensuring the quality of model mining.
Consideraciones de diseño de un control doble lazo de un convertidor boost para la corrección del factor de potencia

This work presents an analytical study with its verification by simulation of a double-loop control strategy on boost converter for power factor correction. This strategy is based on a scheme with an outer loop for regulating the output voltage and an inner loop for input current control. Both control loops have certain specifications transient and a good level of attenuation to disturbances. Some PSIM simulations illustrate and verify the dynamic behavior of the proposed methodology.
Decoding Brain Signals to Assess the Perception of Music in the Psychedelic State

Psychedelics are a class of drugs which are known to induce an altered state of consciousness called the "psychedelic state". While often taken for recreational usage (despite their typically illegal status), much research has been conducted on their therapeutic uses. Research in psychedelic therapy has shown a lot of promise in the treatment of mental illnesses such as anxiety and depression, and music has shown to be an important contextual factor in psychedelic therapy. Therefore, a better understanding of the relationship between music and psychedelics could impact the way that psychedelic therapy is carried out. In this project, we assess how the perception of music changes in the psychedelic state. More speciﬁcally, under the eﬀect of LSD - the prototypical psychedelic. We will analyse a dataset with data from 13 test subjects. For each test subject, we have recordings of their brain signals both in and out of the psychedelic state. We also have the music the subjects were listening to while these brain signals were recorded. we we have the limitations methods
A Graph Temporal Information Learning Framework for Popularity Prediction

Effectively predicting the future popularity of online content has important implications in a wide range of areas, including online advertising, user recommendation, and fake news detection. Existing approaches mainly consider the popularity prediction task via path modeling or discrete graph modeling. However, most of them heavily exploit underlying diffusion structural and sequential information, while ignoring the temporal evolution information among different snapshots of cascades. In this paper, we propose a graph temporal information learning framework based on an improved graph convolutional network (GTGCN), which can capture both the temporal information governing the spread of information in a snapshot, and the inherent temporal dependencies among different snapshots. We validate the effectiveness of the GTGCN by applying it on a Sina Weibo dataset in the scenario of predicting retweet cascades. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art approaches.
Uncovering the specificities of CAD tools for industrial design with design theory – style models for generic singularity

Abstract According to some casual observers, computer-aided design (CAD) tools are very similar. These tools are used to design new artifacts in a digital environment; hence, they share typical software components, such as a computing engine and human–machine interface. However, CAD software is dedicated to specific professionals – such as engineers, three-dimensional (3D) artists, and industrial designers (IDs) – who claim that, despite their apparent similarities, CAD tools are so different that they are not substitutable. Moreover, CAD tools do not fully meet the needs of IDs. This paper aims at better characterizing CAD tools by taking into account their underlying design logic, which involves relying on recent advances in design theory. We show that engineering CAD tools are actually modeling tools that design a generic variety of products; 3D artist CAD tools not only design but immediately produce single digital artifacts; and ID CAD tools are neither a mix nor an hybridization of engineering CAD and 3D artist CAD tools but have their own logic, namely to create new conceptual models for a large variety of products, that is, the creation of a unique original style that leads to a generic singularity. Such tools are useful for many creative designers beyond IDs.
Spatial Rigid/Flexible Dynamic Model of Biopsy and Brachytherapy Needles Under a General Force Field

—Computer-aided simulation of percutaneous nee- dle insertion, as a training tool for junior surgeons, is expected to signiﬁcantly increase targeting accuracy during min- imally invasive operations. An essential requirement for the development of these simulation solutions, is the complete and accurate characterisation of the underlying dynamics of needle insertion. In this regard, this work presents a novel mathematical model, based on the theory of ﬂexible multibody dynamics, that captures the spatial dynamics of needles used for brachytherapy and local anaesthetic transperineal prostate (LATP) biopsy, under a general three-dimensional force ﬁeld. Due to its accuracy and computational efﬁciency, the proposed model is expected to constitute a valuable tool for both real-time visual/haptic simulation and control of percutaneous needle insertion.
Multi-view Omics Translation with Multiplex Graph Neural Networks

The rapid development of high-throughput experimental technologies for biological sampling has made the collection of omics data (e.g., genomics, epigenomics, transcriptomics and metabolomics) possible at a small cost. While multi-view approaches to omics data have a long history, omics-to-omics translation is a relatively new strand of research with useful applications such as recovering missing or censored data and finding new correlations between samples. As the relations between omics can be non-linear and exhibit long-range dependencies between parts of the genome, deep neural networks can be an effective tool. Graph neural networks have been applied successfully in many different areas of research, especially in problems where annotated data is sparse, and have recently been extended to the heterogeneous graph case, allowing for the modelling of multiple kinds of similarities and entities. Here, we propose a meso-scale approach to construct multiplex graphs from multi-omics data, which can construct several graphs per omics and cross-omics graphs. We also propose a neural network architecture for omics-to-omics translation from these multiplex graphs, featuring a graph neural network encoder, coupled with an attention layer. We evaluate the approach on the open The Cancer Genome Atlas dataset (N=3023), showing that for MicroRNA expression prediction our approach has lower prediction error than regularized linear regression or modern generative adversarial networks.
Leveraging Intra and Inter Modality Relationship for Multimodal Fake News Detection

Recent years have witnessed a massive growth in the proliferation of fake news online. User-generated content is a blend of text and visual information leading to producing different variants of fake news. As a result, researchers started targeting multimodal methods for fake news detection. Existing methods capture high-level information from different modalities and jointly model them to decide. Given multiple input modalities, we hypothesize that not all modalities may be equally responsible for decision-making. Hence, this paper presents a novel architecture that effectively identifies and suppresses information from weaker modalities and extracts relevant information from the strong modality on a per-sample basis. We also establish intra-modality relationship by extracting fine-grained image and text features. We conduct extensive experiments on real-world datasets to show that our approach outperforms the state-of-the-art by an average of 3.05% and 4.525% on accuracy and F1-score, respectively. We also release the code, implementation details, and model checkpoints for the community’s interest.1
Long‐term effects of glucocorticoid excess on the brain

The metabolic and cardiovascular clinical manifestations in patients with Cushing's syndrome (CS) are generally well known. However, recent studies have broadened the perspective of the effects of hypercortisolism, showing that both endogenous and exogenous glucocorticoid excess alter brain functioning on several time scales. Consequently, cognitive deficits and neuropsychological symptoms are highly prevalent during both active CS and CS in remission, as well as during glucocorticoid treatment. In this review, we discuss the effects of endogenous hypercortisolism and exogenously induced glucocorticoid excess on the brain, as well as the prevalence of cognitive and neuropsychological deficits and their course after biochemical remission. Furthermore, we propose possible mechanisms that may underly neuronal changes, based on experimental models and in vitro studies. Finally, we offer recommendations for future studies.
A model can describe large networks

In this article, we focus on Bernoulli percolation and mainly investigate bounds of the probability of the connectivity of 0 to the distance n. At first, we give a rough bound of the probability, and then refine our result by the high-dimensional RSW theory, which gives a nontrivial bound for a short crossing in ℤ𝒅, as well as the renormalization method. We finish the last step of this section by coupling. Next, we give a more refined bound in ℤ𝟐 using the dual graph. At last, we investigate the behaviors of some subgraphs of ℤ𝟐. The work done and conclusions made in this article are fundamental in percolation theory. The purpose of this article is to introduce Bernoulli percolation in a relatively thorough way to the beginners of percolation theory and provide some insights into different proofs of basic conclusions.
Comparison of the effectiveness of savlon antiseptic with povidone-iodine for acinetobacter baumannii infected wounds in white rats (Rattus Norvegicus)

Wound washing with antiseptic liquid is one step in the management of chronic wounds. In infected wounds, washing with antiseptic is expected to reduce bacterial colonies and help eradicate infection. One of the important pathogenic bacteria is Acinetobacter Baumannii. These opportunistic germs can form colonies, especially in patients receiving broad-spectrum antibiotic therapy, treatment in the ICU, and on skin affected by burns. Antiseptic commonly used to wash wound are Savlon and Povidone-iodine 10%. Both antiseptic are broad spectrum antisepctic, kill gram negative and gram positive bacteria. However Savlon could caused skin irritation. Therefore dilution is needed to prevent this effect. This study was intended to determine the difference and comparison of the effectiveness of Savlon and Povidone iodine antiseptic to bacterial growth on wound infected by Acinetobacter Baumannii. This study is an experimental study with a randomized controlled trial without blinding. The wound research procedure was made on the right and left sides of the rat's back with a size of 1x1 cm each. Wounds that have been contaminated with Acinetobacter Baumannii are left for 4 hours. After 4 hours the wound will be washed and irrigated. The wound that has been made is closed with a transparent dressing.