Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pattern Similarity Scheme for Medical Image Retrieval

The components of each pattern include a cluster representation and a measure that quantifies the quality of the image content representation achieved by the pattern. The similarity between two patterns is estimated as a function of the similarity between both the structure and the measure components of the patterns. Experiments were performed on a reference set of radiographic images, using standard wavelet domain image features. The results show that the proposed scheme can be more efficient than the common ground schemes for medical image retrieval, as it does not involve exhaustive, nearest neighbor searching over the whole set of the available feature vectors. Keeping the patterns in a unified form facilitates further processing and analysis by mining or visualization algorithms.
A New Heuristic for Improving the Performance of Genetic Algorithm

— The hybridisation of genetic algorithm with heuristics has been shown to be one of an effective way to improve its performance. In this work, genetic algorithm hybridised with four heuristics including a new heuristic called neighbourhood improvement were investigated through the classical travelling salesman problem. The experimental results showed that the proposed heuristic outperformed other heuristics both in terms of quality of the results obtained and the computational time.
Simulation and Comparative Analysis of Proactive and Reactive Routing Protocols in Mobile Ad-Hoc Networks (MANeT)

- An ad-hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any stand-aloneinfrastructure or centralized administration. Mobile Ad-HoC Networks are self-organizing and self-configuring multihop wireless networks where the structure of the network changes dynamically. This is mainly due to the mobility of the nodes. Nodes in these networks utilize the same random access wireless channel cooperating in a friendly manner to engaging themselves in multihop forwarding. The node in the network not only acts as hosts but also as routers that route data to/from other nodes in network. Each device in a MANET is free to move independently in any direction and will therefore change its links to other devices frequently. Each must forward traffic unrelated to its own use and therefore be a router. Routing in Ad-HoC networks has been a challenging task ever since the wireless networks came into existence. The major reason for this is the constant change in network topology because of high degree of node mobility. A number of protocols have been developed for accomplishing this task. Routing is the process of selecting paths in a network along which to send network traffic. In packet switching networks, routing directs packet forwarding, the transit of logically addressed packets from their source toward their ultimate destination through intermediate nodes. An ad hoc routing protocol is a convention, or standard, that controls how nodes decide which way to route packets between computing devices in a mobile ad-hoc network. In ad hoc networks, nodes do not start out familiar with the topology of their networks instead, they have to discover it.The present thesis shows simulation study and comparison of the performance between two categories of routing protocols, table-driven (Proactive) and on-demand (Reactive) routing protocols, this two categories were illustrated by using three different examples of routing protocols. First example is DSDV (Destination Sequenced Distance-Vector) from the Proactive family and the second example is AODV (Ad Hoc On-Demand Distance Vector) and DSR (Dynamic Source Routing Protocol) from the Reactive family. Both protocols were simulated by using NS-2 (network simulator-2) package. Both routing protocols were compared in terms of average throughput (packets delivery ratio), pause time, maximum packets in queue and average delay and Speed. The results are shown in tabular & graphic form.
A Traceability Link Model for the Unified Process

It is presented that extraction of global and local feature based on digital microbial image analysis and fusion local and global features for microbe recognition. The global features are extracted by invariant moments and co-occurrence matrix, in which invariant moments computation is simplified by computing geometric moment and central moment based on the edge of microbe instead of the field of it. Curvature changing detection for microbe is characterized as local features by wavelet transform. Min-max was applied for normalization and after the fusion of normalized global match degree and normalized local match degree, the recognition result is the class that included the template image corresponding to the largest fused match degree. The experimental results show that fusing local and global features is effective for microbe image analysis and recognition.
White-Box Cryptography: Don't Forget About Grey Box Attacks

Despite the fact that all current scientific white-box approaches of standardized cryptographic primitives have been publicly broken, these attacks require knowledge of the internal data representation used by the implementation. In practice, the level of implementation knowledge required is only attainable through significant reverse engineering efforts. In this paper we describe new approaches to assess the security of whitebox implementations which require neither knowledge about the look-up tables used nor expensive reverse engineering effort. We introduce the differential computation analysis (DCA) attack which is the software counterpart of the differential power analysis attack as applied by the cryptographic hardware community. Similarly, the differential fault analysis (DFA) attack is the software counterpart of fault-injection attacks on cryptographic hardware. For DCA, we developed plugins to widely available dynamic binary instrumentation (DBI) frameworks to produce software execution traces which contain information about the memory addresses being accessed. For the DFA attack, we developed modified emulators and plugins for DBI frameworks that allow injecting faults at selected moments within the execution of the encryption or decryption process as well as a framework to automate static fault injection. To illustrate the effectiveness, we show how DCA and DFA can extract the secret key from numerous publicly available non-commercial whitebox implementations of standardized cryptographic algorithms. These approaches allow one to extract the secret key material from white-box implementations significantly faster and without specific knowledge of the white-box design in an automated or semi-automated manner.
The assessment of transboundary and regional air pollution due to particles

Elevated atmospheric particulate matter concentrations are associated with significant adverse health effects, affect ecosystems, influence visibility and cloud formation in the atmosphere and play an important role in climate change. Air quality modelling makes the connection between the design of effective mitigation strategies and knowledge of air pollutant sources. Transboundary and regional air quality models are one of the main scientific and policy tools used for the air quality assessment including particulate matter (PM). As sources outside cities often contribute significantly to local air pollution, many European cities will be unable to meet WHO guideline levels for air pollutants only by local action. Regional models resolve complex chemical processes of formation of secondary PM providing the possibility to identify the key mechanisms in their formation. Modelling results are primarily used to identify the contribution of the regional anthropogenic and natural emission sources of air pollution to the local levels, and to ensure high quality data for decision making that would jointly with the adequate measurements allow for the implementation of effective measures for air pollution reduction. The aim of this chapter is to provide an overview of transboundary and regional modelling of air pollution due to PM as well as the assessment of rural background PM measurements. Within this chapter regional model capabilities including input data (emissions, meteorology etc.) are discussed with focus on two widely applied atmospheric chemistry models EMEP and WRF/Chem as well as trajectory model Hysplit. Finally, the systematic review of some recent scientific papers focused on PM simulation and intercomparison of different models enabled the identification of unresolved scientific issues in regional PM simulations with atmospheric chemistry models.
Passing the Data Baton : A Retrospective Analysis on Data Science Work and Workers

Data science is a rapidly growing discipline and organizations increasingly depend on data science work. Yet the ambiguity around data science, what it is, and who data scientists are can make it difficult for visualization researchers to identify impactful research trajectories. We have conducted a retrospective analysis of data science work and workers as described within the data visualization, human computer interaction, and data science literature. From this analysis we synthesis a comprehensive model that describes data science work and breakdown to data scientists into nine distinct roles. We summarise and reflect on the role that visualization has throughout data science work and the varied needs of data scientists themselves for tooling support. Our findings are intended to arm visualization researchers with a more concrete framing of data science with the hope that it will help them surface innovative opportunities for impacting data science work. Data availability: https://osf.io/z2xpd/? view_only=87fa24be486a473884adb9ffbe8db4ec
Two Strategies to Speed up Connected Component Labeling Algorithms

This paper presents two new strategies to speed up connected component labeling algorithms. The first strategy employs a decision tree to minimize the work performed in the scanning phase of connected component labeling algorithms. The second strategy uses a simplified union-find data structure to represent the equivalence information among the labels. For 8-connected components in a two-dimensional (2D) image, the first strategy reduces the number of neighboring pixels visited from 4 to 7/3 on average. In various tests, using a decision tree decreases the scanning time by a factor of about 2. The second strategy uses a compact representation of the union-find data structure. This strategy significantly speeds up the labeling algorithms. We prove analytically that a labeling algorithm with our simplified union-find structure has the same optimal theoretical time complexity as do the best labeling algorithms. By extensive experimental measurements, we confirm the expected performance characteristics of the new labeling algorithms and demonstrate that they are faster than other optimal labeling algorithms.
Image Compression using self-Organizing Maps

The self-organizing Kohonen map is a reliable and efficient way to achieve vector quantization. Typical application of such algorithm is image compression. Moreover, Kohonen networks realize a mapping between an input and an output space that preserves topology. This feature can be used to build new compression schemes which allow to obtain better compression rate than with classical method as JPEG without reducing the image quality. Compared to JPEG, our lossy compression scheme shows better performances (in terms of PSNR) for compression rates higher than 30 [C. Amerijckx, M. Verleysen, P. Thissen and J.-D. Legat (1998). Image compression by self-organized Kohonen map. IEEE Transactions on Neural Networks, 9(3), 503-507. ]. For lossless compression, this rate is about 2.7 for standard images.
The Euro goes East: Implications of the 2000–2002 Economic Slowdown for Synchronisation of Business Cycles between the Euro area and CEECs

We assess the correlation of supply and demand shocks between current countries in the euro area and European Union (EU) accession candidates from 1993/1995 to 2002. Supply and demand shocks are recovered from estimated structural vector autoregressive models of output growth and inflation. Notably, the economic slowdown between 2000 and 2002 increased heterogeneity of business cycles between the euro area and acceding counties. We find that several acceding countries have a quite high correlation of underlying shocks with the euro area and conclude that continuing integration within the EU is likely to align the business cycles of these countries in a manner similar to the synchronisation of supply and demand shocks that we document for the EU in the 1990s.
Do Efficient Banking Sectors Accelerate Economic Growth in Transition Countries

The relationship between financial sector and economic growth in transition countries has been largely ignored in the earlier empirical literature. In this paper, we analyse the finance-growth nexus using a fixed-effects panel model and unbalanced panel data from 25 transition countries during the period 1993-2000. We measure the qualitative development in the banking sectors using the margin between lending and deposit interest rates. Our second variable for the level of financial sector development is the amount of bank credit allocated to the private sector as a share of GDP. According to our results, the interest rate margin is significantly and negatively related to economic growth. This outcome is in line with theoretical models and has important policy implications. On the other hand, a rise in the amount of credit does not seem to accelerate economic growth. The main reasons behind this result could be the numerous banking crises the transition countries have experienced and the soft budget constraints that are still prevalent in many transition countries. Due to these specific characteristics the growth in credit has not always been sustainable and in some cases it may have led to a decline in growth rates.
Digital Commons

Mycobacterium tuberculosis (Mtb) , the causative agent of tuberculosis (TB), infects one third of the world’s population. Among these infections, clinical isolates belonging to the W-Beijing appear to be emerging, representing about 50% of Mtb isolates in East Asia, and about 13% of all Mtb isolates worldwide. In animal models, infection with W-Beijing strain, Mtb HN878, is considered ‘‘hypervirulent’’ as it results in increased mortality and causes exacerbated immunopathology in infected animals. We had previously shown the Interleukin (IL) -17 pathway is dispensable for primary immunity against infection with the lab adapted Mtb H37Rv strain. However, it is not known whether IL-17 has any role to play in protective immunity against infection with clinical Mtb isolates. We report here that lab adapted Mtb strains, such as H37Rv, or less virulent Mtb clinical isolates, such as Mtb CDC1551, do not require IL-17 for protective immunity against infection while infection with Mtb HN878 requires IL-17 for early protective immunity. Unexpectedly, Mtb HN878 induces robust production of IL-1 b through a TLR-2-dependent mechanism, which supports potent IL-17 responses. We also show that the role for IL-17 in mediating protective immunity against Mtb HN878 is through IL-17 Receptor signaling in non-hematopoietic cells, mediating the induction of the chemokine, CXCL-13, which is required for localization of T cells within lung lymphoid follicles. Correct T cell localization within lymphoid follicles in the lung is required for maximal macrophage activation and Mtb control. Since IL-17 has a critical role in vaccine-induced immunity against TB, our results have far reaching implications for the design of vaccines and therapies to prevent and treat emerging Mtb strains. In addition, our data changes the existing paradigm that IL-17 is dispensable for primary immunity against Mtb infection, and instead suggests a differential role for IL-17 in early protective immunity against emerging Mtb strains.
Contextualizing the information space in Federated Digital Libraries

Rapid growth in the volume of documents, their diversity, and terminologicalvariationsrender federated digitallibrariesincreas-ingly di(cid:14)cult to manage. Suitable abstraction mechanisms are required to construct meaningful and scalable document clusters, forming a cross-digital library information space for browsing and semantic searching. This paper addresses the above issues, pro-poses a distributed semantic framework that achieves a logical partitioning of the information space according to topic areas, and provides facilities to contextualize and landscape the available document sets in subject-speci(cid:12)c categories.
Laboratory validation of a clinical metagenomic next-generation sequencing assay for respiratory virus detection and discovery

Tools for rapid identification of novel and/or emerging viruses are urgently needed for clinical diagnosis of unexplained infections and pandemic preparedness. Here we developed and clinically validated a largely automated metagenomic next-generation sequencing (mNGS) assay for agnostic detection of respiratory viral pathogens from nasopharyngeal swab, bronchoalveolar lavage and tracheal aspirate samples in <24 hours. The mNGS assay achieved mean limits of detection of 543 copies/mL, viral load quantification with 100% linearity, and 93.6% sensitivity, 93.8% specificity, and 93.7% accuracy compared to gold-standard clinical multiplex RT-PCR. Performance increased to 97.9% overall predictive agreement after discrepancy testing and clinical adjudication, which was superior to that of RT-PCR (95.0% agreement). To enable discovery of novel, sequence-divergent human viruses with pandemic potential, de novo assembly and translated nucleotide algorithms were incorporated into the automated SURPI+ computational pipeline used by the mNGS assay for pathogen detection. Using in silico analysis, we showed after removal of all human viral sequences from the reference database that 70 (100%) of 70 representative human viral pathogens could still be identified based on homology to related animal or plant viruses. Our assay, which was granted breakthrough device designation from the US Food and Drug Administration (FDA) in August of 2023, demonstrates the feasibility of routine mNGS testing in clinical and public health laboratories, thus enabling a robust and rapid response to the next viral respiratory pandemic.
A Qualitative Descriptive Study Exploring Caregivers' Information Needs and Experience Caring for a Child with Chronic Heart Failure

Background: Chronic phenotypes of pediatric heart failure pose life-long burdensome symptoms for the healthcare system and families. Treatment involves complex medical therapies with few surgical options until more advanced, refractory stages. Caregivers must become proficient in providing care to these vulnerable children in the home environment, which imposes a high amount of stress. Despite caregiver demands, little is known about caregiver information needs and experiences caring for a child with chronic heart failure. Therefore, a qualitative approach employing semi-structured interviews aimed to fill this knowledge gap. Methods: A qualitative descriptive methodology guided our study. Participants were recruited from a tertiary cardiac centre in Edmonton, Alberta, Canada. Data collection and analysis occurred concurrently. Semi-structured interviews were conducted until data redundancy was achieved. Inductive content analysis was used to uncover categories. Results: Eleven interviews identified five main categories. Three categories related to information needs: 1) sources of information, 2) profound stress steepens the learning curve, and 3) acknowledging that learning heart failure takes time. Two categories related to experience: 4) the emotional rollercoaster, feelings of emotional distress, and 5) the hard reality of caring for a child with heart failure: always on the clock. Conclusions: To our knowledge, this is the first North American situated qualitative study to provide key insights about caregivers? information needs and experiences caring for a child with chronic heart failure. This knowledge provides insight to enhance and optimize clinical care and outcomes in this population and will inform the design of an arts-based information tool targeting caregivers.
Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights.

In the healthcare industry, many artificial intelligence (AI) models have attempted to overcome bias from class imbalances while also maintaining high results. Firstly, when utilizing a large number of unbalanced samples, current AI models and related research have failed to balance specificity and sensitivity a problem that can undermine the reliability of medical research. Secondly, no reliable method for obtaining detailed interpretability has been put forth when addressing large numbers of input features. The present research addresses these two critical research gaps with a proposed lightweight Artificial Neural Network (ANN) model. Using 43 input features from the 2021 Behavioral Risk Factor Surveillance System (BRFSS) dataset, the proposed model outperforms prior models in producing balanced outcomes from markedly unbalanced large survey data. The efficacy of this proposed ANN model is attributed to its simplified design, which reduces processing demands, and its resilience in identifying the probability of myocardial infarction (MI). This is demonstrated by its 80% specificity and 77% sensitivity and is substantiated by a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.87. The outcomes across the scopes of each specified data domain were also separately represented, thus demonstrating the proposed models' robust sensitivity. The interpretability of the model, as measured by Shapley values, reveals substantial correlations between myocardial infarction (MI) and its risk factors, including long-term medical conditions, socio-demographic factors, personal health habits, economic and social status, healthcare availability and affordability, as well as impairment statuses, providing valuable insights for improved cardiovascular risk assessment and personalized healthcare strategies.
Quantifying the Relative Importance of Genetics and Environment on the Comorbidity between Mental- and Cardiometabolic Disorders: A Comprehensive Analysis of National Register Data from 17 million Scandinavians

Mental disorders (MDs) are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders (CMDs). Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and complete genealogies of Denmark and Sweden (n=17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six MDs and 14 CMDs. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with CMDs, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with CMDs was mainly or fully driven by environmental factors. These findings provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.