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Motivation Target enrichment strategies generate genomic data from multiple pathogens in a single process, greatly improving sensitivity over metagenomic sequencing and enabling cost-effective, high-throughput surveillance and clinical applications. However, uptake by research and clinical laboratories is constrained by an absence of computational tools that are specifically designed for the analysis of multi-pathogen enrichment sequence data. Here we present an analysis pipeline, Castanet, for use with multi-pathogen enrichment sequencing data. Castanet is designed to work with short-read data produced by existing targeted enrichment strategies, but can be readily deployed on any BAM file generated by another methodology. Also included are an optional graphical interface and installer script. Results In addition to genome reconstruction, Castanet reports method-specific metrics that enable quantification of capture efficiency, estimation of pathogen load, differentiation of low-level positives from contamination, and assessment of sequencing quality. Castanet can be used as a traditional end-to-end pipeline for consensus generation, but its strength lies in the ability to process a flexible, pre-defined set of pathogens of interest directly from multi-pathogen enrichment experiments. In our tests, Castanet consensus sequences were accurate reconstructions of reference sequences, including in instances where multiple strains of the same pathogen were present. Castanet performs effectively on standard computers and can process the entire output of a 96-sample enrichment sequencing run (50M reads) using a single batch process command, in $<$2 h. Availability and implementation Source code freely available under GPL-3 license at https://github.com/MultipathogenGenomics/castanet , implemented in Python 3.10 and supported in Ubuntu Linux 22.04. The data underlying this article are available in Europe Nucleotide Archives, at https://www.ebi.ac.uk/ena/browser/view/PRJEB77004. [ABSTRACT FROM AUTHOR]

As medical sensors undergo expeditious advancements, there is rising interest in the realm of healthcare applications within the Internet of Medical Things (IoMT) because of its broad applicability in monitoring the health of patients. IoMT proves beneficial in monitoring, disease diagnosis, and better treatment recommendations. This emerging technology aggregates real-time patient health data from sensors deployed on their bodies. This data collection mechanism consumes excessive power due to the transmission of data of similar types. It necessitates a deduplication mechanism, but this is complicated by the variable sizes of the data chunks, which may be either very small or larger in size. This reduces the likelihood of efficient chunking and, hence, deduplication. In this study, a deduplication-based data aggregation scheme was presented. It includes a Delimiter-Based Incremental Chunking Algorithm (DICA), which recognizes the breakpoint among two frames. The scheme includes static as well as variable-length windows. The proposed algorithm identifies a variable-length chunk using a terminator that optimizes the windows that are variable in size, with a threshold limit for the window size. To validate the scheme, a simulation was performed by utilizing NS-2.35 with the C language in the Ubuntu operating system. The TCL language was employed to set up networks, as well as for messaging purposes. The results demonstrate that the rise in the number of windows of variable size amounts to 62%, 66.7%, 68%, and 72.1% for DSW, RAM, CWCA, and DICA, respectively. The proposed scheme exhibits superior performance in terms of the probability of the false recognition of breakpoints, the static and dynamic sizes of chunks, the average sizes of chunks, the total attained chunks, and energy utilization. [ABSTRACT FROM AUTHOR]

This paper draws on biographical research among the Akamba and the Luo communities in Eastern and Western Kenya, respectively. Our research explored how practices of adolescence as a process, an institution, and a performance of identity interact with colonial modernities and imaginaries in complex ways. The biographical research was carried out predominantly with women born in the late colonial period in Kenya. We provide critical reflections on the process and affordances of our embodied storytelling approach, which we position as an Africanist methodology and a decolonial research practice. This research and approach provided women with a space to narrate and perform their lived experience, potentially disrupting epistemic inequities that are embedded in the way research on growing up in the past is carried out. The discussions show how colonialism interacted with other factors, including gender and generational power, tradition, girls' agency, and other life characteristics like poverty and family situation, in order to influence the lived experiences of women. Going beyond the narratives of victimhood that characterise coming of age in similar spaces, we present women's emergent, incomplete, and incongruent agency. We position this agency as the diverse ways in which people come to terms with their difficult contexts. The discussion also points to the need for unsettling the settled thinking about girlhood and coming of age in specific historical spaces in the global South. [ABSTRACT FROM AUTHOR]

The number of cyber incidents in which the Internet of Things (IoT) device or system is present is increasing every day, requiring the opening of forensic investigations that can shed light on what has occurred. In order to be able to provide investigators with proper solutions for performing complete and efficient examinations in this new environment, IoT systems and devices are being studied from a forensic perspective so that tools and procedures can be designed accordingly. In this article, besides reviewing the proposals from the community on this matter, the multi-purpose IoT operating system Ubuntu Core is studied to determine in what way a forensic investigation of this system should be performed, detailing how to approach the acquisition and analysis phases. In addition, both the volatile and non-volatile artifacts that might hold useful information are listed and described, and a forensic tool is presented for their recovery as well as for the acquisition of the non-volatile memory. [ABSTRACT FROM AUTHOR]

The alienation of labour is both classical and contemporary. In its classical form, it speaks to the potential dehumanisation of workers in capitalist societies. In its contemporary form, it manifests itself in the disenfranchisement of the individual because of changes in organised global workplaces. Over the years, Africa’s labour transition from traditional spirituality to contemporary organised global workplaces has fuelled new forms of public labour alienation. Civil servants, in some African countries, experience labour alienation reminiscent of work under capitalism. This is in contradiction to the pre-colonial and traditional view of work as a vocation. Zimbabwe is undergoing negative economic, social, and political growth that has resulted in the alienation of civil servants. The government reneges on its public role of providing space for individual growth and well-being in preference for ‘public capitalism’ and cultural alienation. The potential for an effective public service lies in changing the work culture. Contribution: This article interrogates the impacts of the work culture within the public service in Zimbabwe in an attempt to proffer a return to the African traditional spirituality of work that was founded on the principles of ubuntu. It recognises the traditional symbiotic relationship between being and doing among the indigenous African communities as the panacea for the continent’s human capital development. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is a critical tool in the detection and characterization of brain tumors, offering precise information on tumor location, size, and properties. This information is vital for healthcare professionals to devise personalized treatment plans and select suitable therapeutic approaches while preserving neurological functions. Presented here is an innovative AI-based MRI tumor diagnosis system, accessible to a wide range of medical professionals including radiologists, specialists, and registrars. The system integrates advanced web service applications, providing an improved user experience. The diagnosis system is built utilizindg a Y-Net architecture with transfer learning, demonstrating a classification loss of 0.2152 and an accuracy rate of 0.9075. Remarkably, for segmentation tasks, the system achieves an accuracy of 0.9983, a dice coefficient of 0.9036, and an Intersection over Union (IoU) of 0.8225. Hosted on Amazon's cloud servers, the system is readily available to medical center personnel, ensuring widespread accessibility. For deployment on the Amazon cloud, a virtual machine running the Ubuntu Linux distribution was established, and the training model was successfully deployed online. To bolster the reliability of interactions among various users, the deployment of HTTP/2 for internet connections is recommended. This protocol significantly enhances the handling of substantial MRI data, fostering improved transfer speeds and effective collaboration in the diagnosis and treatment of brain tumors. This implementation culminates in a sophisticated Picture Archiving and Communication System (PACS) with cloud-based functionalities for the storage, retrieval, management, and distribution of medical images and associated patient data. The MRI PACS system guarantees precise and error-minimized interactions among users, excelling in data interchange speed and accuracy. Simultaneously, the system's core AI functionalities, including segmentation, classification, localization, and tumor area calculation, are executed flawlessly, ensuring integrity and no data loss or corruption. [ABSTRACT FROM AUTHOR]

We provide artifacts to reproduce the evaluation results of our article: “NSFuzz: Towards Efficient and StateAware Network Service Fuzzing”. The provided artifacts can be downloaded from https://zenodo.org/record/ 7134490. It includes 14 docker containers, several scripts for execution and analysis, one additional proof for the crash results, and six related documents for the running of experiments. We claim for all three badges, i.e., Available, Functional, and Reusable. This report gives instructions on how to reproduce the answers which mainly involve basic operations on the Ubuntu operating system [ABSTRACT FROM AUTHOR]

The article discusses the future of European Union (EU) data protection law following its fifth anniversary in May 2023. It argues for the expansion of the concept of personal data to cater for collectives and not just the individual. It discusses the interests of collectives in data protection that are overlooked by current laws and highlights the importance of extending data protection to collectives. In this regard, the article argues that EU data protection law should undergo a reverse Brussels effect which allows the EU to look outward and learn from cultures that privilege communities and groups to identify a collective cultural value that can inform a theoretical framework that recognises collective rights. It proposes Ubuntu as one such value that the EU can learn from and transpose into the current data protection system without making drastic changes. It proposes an Ubuntu Framework and the principles accompanying such framework that can be incorporated into the GDPR. [ABSTRACT FROM AUTHOR]

Network robustness in wireless sensor network (WSN) is an emerging area of research as sensor nodes (SNs) often fail due to limited energy resources, harsh environment, and proliferation of cyber-attacks. Therefore, the energy efficiency and robustness of SNs are the two critical issues that need to be addressed in WSNs. In this paper, we address the above mentioned issues in two phases. In the first phase, we propose an energy efficient SN deployment scheme for WSNs using non-uniform SN deployment scheme. In the second phase, we generate scale-free network topology in order to improve the robustness of WSN so that they can withstand failure issues due to cyber-attacks. For the construction of scale-free topology, we have considered various constraints of SNs viz., communication range, maximum degree, and network growth. This topology construction was proved to be very effective in providing robustness to WSN because of their heterogeneous degree distribution. The performance of the proposed work has been evaluated through simulation using Python 3.7.1 on Spyder 3.3.2 on Ubuntu 19.10 with Linux Kernel 5.0.0.13-generic, X86_64 operating system. The simulation results show that the proposed work significantly outperforms existing state-of-the-art related approaches in terms of robustness of the WSN along with the increase in network lifetime. Also, it produces characteristic at tailed distribution with maximum SNs having fewer degree count. [ABSTRACT FROM AUTHOR]

The article reports on the findings of a qualitative inquiry involving a sample of nine (9) teachers (three participants per school) drawn from three schools within the locality of three education circuits of Emalahleni in Mpumalanga Province (South Africa). The primary objective of the article was informed by the paucity of literature that establishes an intersection between Indigenous epistemologies of Ubuntu philosophy, instructional leadership and the sharing process of knowledge management within the domain of primary and secondary education. By eliciting teachers' views about heads of departments' (HODs') curriculum leadership practices, the article attempts to narrow down the knowledge gap on the topic of instructional (herein referred to as curriculum) leadership-- a domain whose preoccupation often slants towards the principal's role at the exclusion of other key stakeholders within the school ecology. In terms of the findings, democratic (participative), autocratic, transactional, transformational and managerial leadership styles were found to have been used by individual HODs alongside instructional leadership style to strengthen their curriculum leadership role. It however, became apparent that both participative and transformational leadership styles sufficiently promoted the ethos of Ubuntu in HODs' curriculum leadership role and thus enhanced curriculum delivery processes and knowledge sharing behavior among teachers as well as between HODs and teachers. The findings of the article demonstrate how a nonadversarial intersection between indigenous and mainstream leadership practices, might add an impetus to HODs' curriculum delivery and knowledge sharing leadership role in under-resourced schooling contexts. [ABSTRACT FROM AUTHOR]

The purpose of the study discussed in the present article is to demonstrate how to leverage the resource description framework to integrate static and dynamic data sources to speed-up data integration, unification, analytics, and sharing in any database. People who use the knowledge graph approach to make apps, integrate data, and examine it depend on its ability to connect scattered data and match complex patterns of long links in the data set. This research looked at several methods for creating database knowledge graphs. The strategy and technical mechanism are observed on command prompts of Ubuntu Operating System. This project runs using machine learning and template-based technology. Other approaches allowed for a semi-automated data integrity assessment. In graph databases, data is stored as graphs. This level of maturity enables failback and clustering in real-time. The information is stored in columns and rows rather than tables. As a result, complex relationships may be swiftly encoded and detected. To understand the significance of this research, we must examine it from a practical standpoint to create a database's knowledge graph. A Knowledge Graph, which determines the authority of a given topic, is one strategy of presenting a subject directly to the user based on real-time sharing of data. [ABSTRACT FROM AUTHOR]

Web Browser Private mode is a feature hide activities carried out by users, user activities not stored on the hard disk or SSD, so they cannot be analyzed with standard forensic techniques (static forensics). This study aimed to acquire web browser artefacts from Random Access Memory (RAM) and Network Traffic on Windows 10 and Ubuntu Linux operating systems. The problem of this study is how to find evidence of a crime in a web browser that utilizes the private mode feature. The web browser in private mode does not leave any traces on the User’s computer, so it becomes an obstacle in the process of searching for digital evidence of a crime. This article proposed parallel data acquisition data through two different sources to obtain digital evidence using private mode web browsers, from RAM and Network Traffic. All website log on Windows 10 and Ubuntu 20.04 are founded with RAM analysis. Analysis network traffic also revealed (1) source IP address, (2) destination IP address, (3) protocol, (4) source port, (5) destination port, (6) timestamp (communication time done), (7) length packet (number of packets transmitted), (8) operating system host client, (9) hostname server and (10) browser client. [ABSTRACT FROM AUTHOR]

In this study, a new dataset was created for use to estimate the state of charge (SOC) of lithium polymer batteries. A new experimental system was created to obtain the dataset by measuring the current, voltage, and temperature parameters of lithium polymer batteries. A convolutional neural network (CNN)-based deep learning model was used as the SOC prediction method. The effect of both batch size and dense network hyperparameter value on total parameter and deep learning error metric values for CNN-based lithium polymer battery SOC estimation is discussed. The proposed method, unlike deep learning models that require a high processing load in electronic cards, has provided remarkable results by being determined according to four different dense networks and two different batch size values. The proposed model has been obtained by performing experiments on optimizer, learning rate, dense network, and batch size values while determining the appropriate parameters to make successful predictions. The success of the CNN models was compared by conducting deep learning training on a computer with an Nvidia Gtx 1060 graphics card running the Ubuntu operating system. Adadelta optimizer achieved R2 0.977262 prediction success with learning rate 10–2, batch size 5 × 102, dense 105 hyperparameter values. According to the results of the experiment, it was concluded that in the CNN deep learning method, large dense layers and small batch size values created less error in SOC estimation. [ABSTRACT FROM AUTHOR]

Quicksort is an important algorithm that uses the divide and conquer concept, and it can be run to solve any problem. The performance of the algorithm can be improved by implementing this algorithm in parallel. In this paper, the parallel sorting algorithm named the Multi-Deque Partition Dual-Deque Merge Sorting algorithm (MPDMSort) is proposed and run on a shared memory system. This algorithm contains the Multi-Deque Partitioning phase, which is a block-based parallel partitioning algorithm, and the Dual-Deque Merging phase, which is a merging algorithm without compare-and-swap operations and sorts the small data with the sorting function of the standard template library. The OpenMP library, which is an application programming interface used to develop the parallel implementation of this algorithm, is implemented in MPDMSort. Two computers (one with an Intel Xeon Gold 6142 CPU and the other with an Intel Core i7-11700 CPU) running Ubuntu Linux are used in this experiment. The results show that MPDMSort is faster than parallel balanced quicksort and multiway merge sort on the large random distribution data. A speedup of 13.81 × and speedup per thread of 0.86 can be obtained. Thus, developers can use these parallel partitioning and merging algorithms to improve the performance of related algorithms. [ABSTRACT FROM AUTHOR]

The article contributes to the ongoing scholarly exploration of Ubuntu as an indigenous Southern African research paradigm. Building on an understanding of Ubuntu as humanness that embraces the interconnectedness not only of humans, but of all creation, the article emphasises that how we research is inseparable from what is researched. Being human in the sense of Ubuntu is not passive but depends on our continued enacting of our humanness through relating positively to others. On this basis, being/becoming human is understood as an inherent (research) agenda in the Ubuntu paradigm. It is proposed that Ubuntu accommodates research agendas that recognise the interdependence of humans with other humans, other species and our shared planet and that aim at balancing these relationships in search of humble togetherness. Ubuntu research agendas seek to contribute to the healing of our planet’s human made colonial and ecological conditions locally or globally. [ABSTRACT FROM AUTHOR]

Footnotes 1 https://assistant.google.com/platforms/tv/ 2 The Ginga-NCL extension implementation presented in this work can be accessed at https://github.com/marinaivanov/ginga-mulsemedia/tree/multimodal 3 https://github.com/TeleMidia/ginga 4 https://gaming.tobii.com/tobii-eye-tracker-4c/ 5 https://help.tobii.com/hc/en-us/articles/213414285-Specifications-for-the-Tobii-Eye-Tracker-4C 6 Video of the application running at: https://1drv.ms/u/s!AvKXFJ6ipz46hqgcEWo39TPNhFC4JQ?e=xyetJ1 7 NCL applications and the script can be accessed at http://bit.do/fHiw4 8 https://forumsbtvd.org.br/tv3 0/ We experimented with each application to evaluate and compare the NCL extension proposal response time and an NCL 3.0 application with Lua scripts. Keywords: NCL; Ginga-NCL; Multimodal interaction; Multi-user interaction; Digital tv applications EN NCL Ginga-NCL Multimodal interaction Multi-user interaction Digital tv applications 4821 4846 26 01/30/23 20230215 NES 230215 Introduction Multimedia applications allow the synchronization of different media types such as text, image, video, and audio. Batista et al. [[7]] propose a Ginga module that enables NCL applications to use resources from other secondary devices to control content display, interaction, and event capture. Extended Ginga-NCL implementation Ginga-NCL is the DTV Ginga middleware subsystem, which allows the execution of multimedia applications specified in NCL [[1]]. [Extracted from the article]

Distance learning today allows you to create a system of mass continuous selflearning, universal exchange of information, regardless of the presence of time and space zones. For more effective online learning, it is necessary to introduce artificial intelligence technologies and methods for implementing learning systems. In this paper, the objects of research were: 1) the capabilities of the OSTIS open semantic technology, the capabilities of the IMS.OSTIS metasystem for organizing online learning; 2) machine learning method – classification. Results of the study: 1) an online course was organized using the IMS.OSTIS metasystem of the OSTIS open semantic technology; 2) for the analysis and visualization of training data, a machine learning method is implemented – classification. The results of the implementation of the online course were obtained using the semantic technology for designing an intelligent learning system: the IMS.OSTIS metasystem using the graphical semantic code SCg. The OSTIS kernel requires a machine with the Ubuntu operating system installed, which is a GNU/Linux distribution based on Debian GNU/Linux, an operating system based on the Linux kernel. The paper also shows an example of using the machine learning method – classification. This method allows you to classify data. Intelligent processing and visualization of data were carried out based on the results of testing students in order to classify them into letter categories A, B, C, D according to a set of features: scores and points of the average score. The high-level Python library Pandas was used, this is a library for data analysis. To visualize the results of data processing, the Matplotlib library in Python was used [ABSTRACT FROM AUTHOR]

This study applies an Afrocentric theory (Relationship-Resourced Resilience [RRR]) to analyze teacher resilience in a less-researched context in the Global South. The Isithebe-intervention study in South African schools investigated how time together to strengthen relationships promotes teacher resilience despite structural disparities. Teachers were conveniently sampled, and South African schools were purposively sampled using concurrent mixed-methods triangulation. Based on Ubuntu social-connectedness principles, the intervention gave teachers monthly art-based time to communicate and build relationships. Pre- and post-intervention measurements included teacher-reported surveys (ENTREE and REPSSI SC subscales) and participatory reflection and action conversations (verbatim transcriptions and visual data). Inferential statistics were used to analyze quantitative data and showed that time together increases resilience, social connectedness, and trust. Qualitative results show time spent together promoted a sense of belonging, safety, and trust in supporting one another by sharing ideas for informal professional development or caring for children, families, and friends who depend on such help to withstand ongoing challenges. Few teacher resilience studies exist in Global South and South Africa. Structured time to build relationships capitalizes on dominant but marginalized Afrocentric belief systems favoring interdependent, collective resilience values, beliefs, and practices and encourages instructors to teach countering deficit notions of structurally disparate contexts. [ABSTRACT FROM AUTHOR]

The literature on Ubuntu is as prolific, as it is diverse. Over the past two decades the increasing attention has been devoted to Ubuntu in relation to African education. In recognising the richness of attention, which has thus been afforded to Ubuntu, this article attempts to offer something else. I consider the concept of Ubuntu as an African notion for education, in the same way that some Europeans describe bildung as education, and ta'dib, for some Muslims, means education. Hence, in this article, I firstly analyse the concept Ubuntu. Secondly, I show how Ubuntu relates to at least three meanings of education, namely, interdependent human action, deliberative inquiry, and socially responsive action. Finally, I make an argument for Ubuntu as a cosmopolitan practice, thus further enhancing its educational impetus before concluding with the rationale for Ubuntu, namely a cultivation of dignity. [ABSTRACT FROM AUTHOR]

The core question in this article is whether, or not, uBuntu and Humanism can simultaneously inform a pursuit of an equitably infused global epistemological orientation and, consequently, an equitably infused global (philosophy of) education. I argue that uBuntu and Humanism are not compatible at the very core of their ontological, axiological, and epistemological nature, and thus, might present an epistemological challenge to any attempt to develop an equitably infused global epistemological orientation and an equitably infused global (philosophy of) education. Moreover, I assume the broad etymological definition of philosophy as the 'love of wisdom' and that wisdom is a manifestation in uBuntu world, and consequently the love of such, and the resulting need to inquire into the compatibility, while recognizing potential incommensurability, between how wisdom is thought of by Western humanist-derived epistemologies and by uBuntu-derived epistemologies. At the center of the analysis is the nature of 'human' or 'person' since both Humanism and uBuntu are intrinsically bound to conceptualizations of personhood. Framed within the historical backdrop of the European Renaissance and the African Renaissance, the article comprises a critical historical outlook and analysis of primary and secondary sources of discourses on Humanism and uBuntu. Primary focus is on works of so-called classical thinkers such as Heidegger, Husserl, Cheikh Anta Diop, René Descartes, Frederick Nietzsche, Julius Nyerere, and John Mbiti. The key aim is to challenge scholars to work toward the equitable infusion of epistemologies inherent in these two terms, instead of continuing to see these terms as interchangeable, which essentially authenticates and perpetuates the imposition of humanism on non-humanist contexts. The African Renaissance might be the adequate space for such equitable placement of uBuntu as a source of global epistemologies alongside those emanating from Humanism. [ABSTRACT FROM AUTHOR]

Considering the debate on preference between digitalization and postdigitalization, this study which is a concept note is undertaken. This is in attempt to explore the roles of education leaders if postdigitalization is to be preferred and promoted. Digitalization which is characterised by high technology use is regarded as tool for repair and solution to prevailing challenges such as inequality, unemployment, poverty, among others tends not to be experienced optimally as envisaged. Meanwhile, one of its major drivers remains the education sector. Conversely, the concept of Ubuntu which is used to express brotherhood remains a major phenomenon in the African continent. Thus, the need to consider postdigitalization as a remedy towards harnessing some of the benefits of digitalization together with those of Ubuntu. This is majorly because in postdigitalization more of the focus remains on humans which is the case with Ubuntu compared to digitalization which considers technology more than people. [ABSTRACT FROM AUTHOR]

The multi-controller placement problem (MCPP) represents one of the most challenging issues in software-defined networks (SDNs). High-efficiency and scalable optimized solutions can be achieved for a given position in such networks, thereby enhancing various aspects of programmability, configuration, and construction. In this paper, we propose a model called simulated annealing for multi-controllers in SDN (SA-MCSDN) to solve the problem of placing multiple controllers in appropriate locations by considering estimated distances and distribution times among the controllers, as well as between controllers and switches (C2S). We simulated the proposed mathematical model using Network Simulator NS3 in the Linux Ubuntu environment to extract the performance results. We then compared the results of this single-solution algorithm with those obtained by our previously proposed multi-solution harmony search particle swarm optimization (HS-PSO) algorithm. The results reveal interesting aspects of each type of solution. We found that the proposed model works better than previously proposed models, according to some of the metrics upon which the network relies to achieve optimal performance. The metrics considered in this work are propagation delay, round-trip time (RTT), matrix of time session (TS), average delay, reliability, throughput, cost, and fitness value. The simulation results presented herein reveal that the proposed model achieves high reliability and satisfactory throughput with a short access time standard, addressing the issues of scalability and flexibility and achieving high performance to support network efficiency. [ABSTRACT FROM AUTHOR]

In this article, we describe the principles and advantages of using the Number-Theoretic Transform (NTT) in post-quantum cryptography. We deal with usages of NTT in post-quantum algorithms included in the competition announced by the National Institute of Standards and Technology. Attention is paid to the fast multiplication of polynomials using NTT and negacyclic convolution. We also focus on the existing implementation of NTT and its modifications to analyze the effectiveness of individual modifications. Separate attention is paid to the Constant Time implementation of NTT because the constant computation time of the transformation decreases a possibility of side channel attack. We describe measurements performed on OS Linux Ubuntu 20.04 LTS environment in Linux kernel mode, with the highest attention to the measurement executed on a microcontroller with an ARM 32-bit core. Measurements performed on microcontroller units are done using 32-bit and 16-bit arithmetic to demonstrate how to achieve constant computation time of the transformation. We present the results and analysis of measurements performed using modified implementations. [ABSTRACT FROM AUTHOR]

The article focuses on the significance of the Fourth Industrial Revolution (4IR) in South Africa, examining its distinct features and potential to contribute to the achievement of the Sustainable Development Goals (SDGs). It delves into the challenges and prospects presented by 4IR technologies, particularly in relation to addressing poverty, enhancing education, and tackling water-related crises.

Single-cell RNA-sequencing analysis to quantify the RNA molecules in individual cells has become popular, as it can obtain a large amount of information from each experiment. We introduce UniverSC (https://github.com/minoda-lab/universc), a universal single-cell RNA-seq data processing tool that supports any unique molecular identifier-based platform. Our command-line tool, docker image, and containerised graphical application enables consistent and comprehensive integration, comparison, and evaluation across data generated from a wide range of platforms. We also provide a cross-platform application to run UniverSC via a graphical user interface, available for macOS, Windows, and Linux Ubuntu, negating one of the bottlenecks with single-cell RNA-seq analysis that is data processing for researchers who are not bioinformatically proficient. As the throughput of single-cell RNA-seq studies increases, there is a need for tools that can make the data analysis steps more streamlined and convenient. Here, the authors develop UniverSC, a tool that unifies single-cell RNA-seq analysis workflows and also facilitates their use for non-experts. [ABSTRACT FROM AUTHOR]

Deep learning has been a promising technology for epidemic tree detection in recent years. However, the expensive data annotation has posed a great challenge to the discolored epidemic wood detection in the pine forest. Particularly, some difficulties are still found in the dataset expansion, model generalization ability, and the presence of samples with obscured or small objects during detection. In this study, target detection was proposed for the discolored epidemic wood using semi-supervised learning and Unmanned Aerial Vehicle (UAV) image analysis. The specific procedure mainly included the dataset for the pine forest epidemic wood, semi-supervised model training, and algorithm detection. The dataset was collected from three provinces in China, especially with a total of six epidemic wood forms. Two datasets were randomly and equally divided for the training and testing in the supervised and semi-supervised learning stages, respectively. 2 160 training and 240 testing sets were available after data augmentation. The anti-noise loss (SoftFocalLoss and L1Loss) was classified with the uncertainty indicator for the data distillation. Among them, combating Noise was the most advanced semi-supervised target detection model in the same period. As such, the quality of pseudo labeling was improved effectively. The following improvements were achieved in the Cascade Noise-Resistant Semi-supervised (CNRS) object detection model, compared with the Combating Noise. 1) A cascade network was added to balance the distribution of positive and negative samples during training, in order to equalize the accuracy and over-fitting. 2) The FocalLoss was used to mine the difficult samples in the phase of supervised learning. The improved learning of the model was achieved in the difficult objects, such as the edge objects and early epidemic wood. 3) SmoothL1Loss was used to ensure a relatively stable gradient, particularly for the large difference between the true and the predicted value. 4) Soft-Non-Maximum Suppression (Soft-NMS) was used to soften the rejection process of the detection frame in the RCNN stage, in order to prevent the near targets from being filtered. The experiments were conducted on Ubuntu 18.04 operating system using NVIDIA Tesla P100 graphics processor. The experimental results show that the Average Precision (AP) values were 64.2%, and 85.4%, respectively, for the single-stage detector SSD300 and the two-stage detector Faster RCNN using fully labeled data. 50% of the labeled data was also selected in the semi-supervised model. Both AP values were higher than the fully supervised model, indicating that the anti-noise learning effectively extracted the semantic information from the pseudo labels. The ablation model of CNRS Model1 improved the AP by 0.3 percentage points, where a cascade network was added with the RoI Pooling. Model2 further improved the AP by 0.3 percentage points after the RoI Align. The AP of the optimal model on the test set was 87.7% with an F-Score of 0.669. A comparison was also made on the detection of the four models using 24 test images. The error detection rate of CNRS was compared with the fully supervised network model, and Faster RCNN using fully labeled data. The CNRS presented a 50% reduction in the labeling, and a 2.3 percentage point increase in the AP, which was 1.6 percentage points higher than that of the semi-supervised network Combating Noise. This improved model can also provide reliable data support for pest control in agriculture and forestry. An accurate detection can be achieved in many different forms of epidemic trees and a significant reduction in the data labeling cost. [ABSTRACT FROM AUTHOR]