Miracle-dz/UniFAR-FTUs huggingface dataset:
Is trust a belief, observation, or state: Results from an active inference analysis of driver-automation transitions of control

Transitions of control are an important safety concern for human-automation teams and automated vehicle safety. While trust and situation awareness have been observed to influence transitions of control in automated vehicles, there are few objective measurements, making these concepts difficult to operationalize in increasingly automated decision systems. In this study, we take a step towards quantifying trust by mapping latent driver beliefs extracted from an active inference-factor analysis model of driver behavior and cognitive dynamics to subjective responses to trust questionnaires. Our results show that subjective trust is primarily correlated with model parameters affecting perceptual evidence accumulation rate, and the same parameters are significantly correlated with driver age.
Mycobacterium tuberculosis EspR modulates Th1-Th2 shift by transcriptionally regulating IL-4, steering increased mycobacterial persistence and HIV propagation during co-infection

Mycobacterium tuberculosis (Mtb) and HIV are known to mutually support each other during co-infection by multiple mechanisms. This synergistic influence could be either by direct interactions or indirectly through secreted host or pathogen factors that work in trans. Mtb secretes several virulence factors to modulate the host cellular environment for its persistence and escaping cell-intrinsic immune responses. We hypothesized that secreted Mtb transcription factors that target the host nucleus can directly interact with host DNA element(s) or HIV LTR during co-infection, thereby modulating immune gene expression, or driving HIV transcription, helping the synergistic existence of Mtb and HIV. Here, we show that the Mtb-secreted protein, EspR, a transcription regulator, increased mycobacterial persistence and HIV propagation during co-infection. Mechanistically, EspR localizes to the nucleus of the host cells during infection, binds to its putative cognate motif on the promoter region of the host IL-4 gene, activating IL-4 gene expression, causing high IL-4 titers that induce a Th2-type microenvironment, shifting the macrophage polarization to an M2 state as evident from CD206 dominant population over CD64. This compromised the clearance of the intracellular mycobacteria and enhanced HIV propagation. It was interesting to note that EspR did not bind to HIV LTR, although its transient expression increased viral propagation. This is the first report of an Mtb transcription factor directly regulating a host cytokine gene. This augments our understanding of the evolution of Mtb immune evasion strategies and unveils how Mtb aggravates comorbidities, such as HIV co-infection, by modulating the immune microenvironment.
The Falsificationist View of Machine Learning

Machine learning pushes the frontiers of algorithmic achievements, though the striving for state-of-the-art performance often obscures the fragility of enforcing decisions amid uncertainty. This paper interprets machine learning within Karl Popper’s epistemology. We assess machine learning paradigms’ fit for falsificationism and argue that the new interpretation can improve robustness. Though the price is to accept unambiguous decisions, the restriction of the hypothesis space still adds value. The context for our work is established by comparison with similar techniques and highlighting its limitations.
An Effective Data Mining Technique For Classification Of Mammograms To Diagnose Breast Cancer

:- Breast cancer is the leading cause of cancer death among women. Screening x-ray mammography is the first and regular method currently available for the reliable early detection and treatment can be started for hopeful cure of breast cancer. Research indicates that the mortality rate could decrease by 35% if women age around 45-50years and older have regular mammograms. The detection rate can be confirmed 15-30% by providing the radiologist with results from a computer-aided diagnosis (CAD) system acting as a second opinion. However, among screening mammograms routinely interpreted by radiologists, very few (approximately 10%) cases actually have breast cancer. It would be beneficial if an accurate CAD system existed to identify normal mammograms and thus allowing the radiologist to focus on suspicious cases. This strategy could reduce the radiologist's workload and improve screening performance. Since mammography is considered as the most effective means for breast cancer diagnosis, this paper introduces two separate techniques for mass and micro-calcification segmentation in digital mammograms. There are two different segmentation techniques used for masses and microcalcifications. For masses there are three steps-background subtraction, fuzzy texture representation and entropic theresholding. Similarly micro-calcifications are also segmented in three stages – background subtraction, Laplacian of Gaussian filtering and contrast estimation followed by thresholding. GLCM texture feature extractions are known to be the most common and powerful techniques for texture analysis including histogram statistical features. Experiments have been taken for a data set of 322 images taken from MIAS of different types with the aim of improving the accuracy of Association Rule classifier by generating minimum no. of rules to cover more patterns. In this study, an incremental updating technique is applied to associative classification for constructing classification system when a new training dataset is appended to an old training dataset. The proposed algorithm, called Incremental Classification Based on Association Rules (ICBA). ICBA has 2 phases which are rule generator phase (ICBA-RG) and classifier building phase (ICBA-CB). In order to reduce the execution time, we applied the concept of Fast Update algorithm (FUP) algorithm to both phases of our algorithm. The experiment results show that the proposed algorithm has execution time better than CBA algorithm.
COMPARATIVE STUDY AND PROPOSAL TO USE ENHANCED BELLMAN-FORD ALGORITHM FOR FASTER PATH COMPUTATION ON SDWAN CONTROLLER

Software-Defined Wide Area Network (SDWAN) technology has revolutionized network management by providing efficient and reliable communication across geographically dispersed locations. One critical aspect of SDWAN is path computation, which determines the optimal routes for data transmission between network nodes. Traditional shortest path algorithms like Bellman-Ford, Dijkstra, and SPFA are commonly used for path computation. However, the increasing scale and complexity of SDWAN networks demand more efficient algorithms. In this research paper, we propose an enhanced shortest path algorithm called SCBF (Shortest Path Computation Based on Bellman-Ford) specifically designed for SDWAN controllers. SCBF incorporates optimization techniques to reduce computational overhead and improve runtime efficiency. By building upon the principles of the Bellman-Ford algorithm, SCBF introduces novel optimizations that expedite path computation. To evaluate SCBF's performance, we conduct a comparative study against traditional shortest path algorithms. Through extensive simulations using various network topologies and traffic scenarios, we demonstrate that SCBF outperforms the traditional algorithms in terms of runtime efficiency. SCBF achieves faster path computation on the SDWAN controller, reducing computational complexity and improving scalability. The comparative study showcases the advantages of SCBF in terms of reduced network latency, improved throughput, and enhanced scalability. The findings contribute to the development of more efficient path computation algorithms for SDWAN controllers, enabling faster decision-making in network routing and resource allocation. These improvements lead to enhanced performance and reliability in SDWAN deployments. Future research and real-world implementations can explore the practical implications of SCBF in SDWAN environments, further validating its effectiveness. SCBF has the potential to provide significant benefits in terms of reduced network overhead and improved network management, ultimately enhancing the overall SDWAN experience.
An optimal control framework for adaptive neural ODEs

In recent years, the notion of neural ODEs has connected deep learning with the field of ODEs and optimal control. In this setting, neural networks are defined as solutions of a given ODE which is solved with a certain time discretization. The learning task consists in finding the ODE parameters as the optimal values of a sampled loss minimization problem. In the limit of infinite time steps, and data samples, we obtain a notion of continuous formulation of the problem. The practical implementation involves two discretization errors: a sampling error, and a time-discretization error. In this work, we develop a general optimal control framework to analyse the interplay between the above two errors. We prove that to approximate the solution of the fully continuous problem at a certain accuracy, we not only need a minimal number of training samples, but we also need to solve the control problem on the sampled loss function with some minimal accuracy. The theoretical analysis allows us to develop rigorous adaptive schemes in time and sampling, and give rise to a notion of adaptive neural ODEs. The performance of the approach is illustrated in several numerical examples.
A SYSTEMATIC STUDY ON SUGGESTION MINING FROM OPINION REVIEWS

Online product reviews have become eminent in the purchase decision-making process. With progress in web 2.0 technologies, huge volumes of unstructured text data are generated as reviews on e-commerce platforms and third-party web portals. Opinion review mining has become a critical area of research in language processing and applied machine learning. Opinion reviews available across various portals are perceived primarily to understand the sentiment polarity expressed by the reviewer at multiple granularities. The opinion review may also contain suggestions or tips for manufacturers and peer customers. Suggestion Mining refers to the automatic extraction of suggestions from opinionated text. The applications include product quality improvement, peer customer suggestions, summarizing collected surveys and feedback, a recommender system, and enhancing sentiment polarity classification. Suggestion mining is considered a sentence classification task, such as classifying a given review as suggestive intent or not. Various linguistic, syntactic, and semantic features with core machine learning and neural network approaches are used for suggestion mining. This paper presents a comprehensive and systematic review of suggestion mining from opinion reviews and their facets in the literature.
Visualization analysis of research hotspots and frontier development of knowledge graph construction

Knowledge graph construction is one of the hot issues in arti�cial intelligence, and it is the core and key to knowledge graph research. Through Citespace (a statistical software) and bibliometric analysis, we draw a visual knowledge graph based on relevant literature published from 2012 to 2021, and analyze it from multiple dimensions including annual published articles, institutions, research hotspots, and research frontiers. The following conclusions are obtained. First, the number of research results is gradually increasing, but the research institutions are scattered. Second, knowledge graph construction has been studied from diversi�ed perspectives, but the concept connotation and technical boundary are vague. Third, the construction technology of each link is constantly developing, but its accuracy, basic methods and theories need to be further improved. Fourth, large-scale universal multilingual knowledge extraction and application, together with natural language processing, has become the most concerned issue in the current knowledge graph construction. However, in large-scale application in special �elds, the research on knowledge graph evaluation technology is inadequate, and the development of educational technology based on knowledge graph is slightly insu�cient. In the future, priority should be given to developing the application of knowledge graph in large-scale domestic �elds, strengthening international cooperation and exchange in technology research & development, and developing knowledge graph system engineering conception. It is also important to strengthen the research on multi-domain application technology and the humanistic research of knowledge graph construction.
INTERNET OF THINGS (IOT) FOR SMART CITY, AGRICULTURE AND HEALTHCARE

The Internet of Things (IoT) technology has revolutionized all areas of human life, making it more comfortable. IoT refers to the current trend of The Internet of Things (IoT) technology that has revolutionized all areas of human life, making it more comfortable. IoT refers to the current trend of connecting all kinds of physical objects to the Internet, even the most unexpected ones, without human intervention, which constitutes a self-configurable network. The Internet of Things (IoT) enables organizations to automate the process and improve service delivery via Internet technology and data transfer to the cloud. Nowadays, the Internet of Things (IoT) is becoming a widely discussed topic among researchers, specialists, and experts. It is seen as the next step in the evolution of the Internet. This paper covers the application of (IoT) technology in three different areas: smart cities, health, and agriculture.
Resolvability of Visual-Inertial Structure from Motion in Closed-form

: This paper investigates the visual-inertial structure from motion problem. A simple closed form solution to this problem is introduced. Special attention is devoted to identify the conditions under which the problem has a ﬁnite number of solutions. Speciﬁcally, it is shown that the problem can have a unique solution, two distinct solutions and inﬁnite solutions depending on the trajectory, on the number of point-features and on their layout and on the number of camera images. The investigation is also performed in the case when the inertial data are biased, showing that, in this latter case, more images and more restrictive conditions on the trajectory are required for the problem resolvability.
Design and Stability Analysis of an Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Pacemaker controller in MATLAB Simulink

This paper presents the design and stability analysis of an Adaptive neuro-fuzzy inference system-based controller of a pacemaker in MATLAB Simulink. ANFIS uses Learning and Speed properties of Fuzzy and Neural Networks. Based on body states and prepro-grammed situations of patients (age and sex, etc.),heart rate and amplitude of pacing pulse are changed. Output signal that is fed backed from heart is compared to the reference fuzzy bases ANFIS signals . After designing ANFIS based controller, the stability of the proposed system has been tested in both Time (Step response) and Frequency domains(Bode Diagram and Nichols chart). In our previous paper Step response analyzed and compared with other works. For frequency domain, all the possible frequency analysis methods have been tested but because of nonlinear properties of ANFIS, after linearization, just the Bode diagram achieved good results. The step response results in time domain is compared with previous work's results including optimum heart pulse rate for each particular patient. In frequency-domain the Bode diagram stability analysis showed Gain and phase margin as follows:
Establishment of hairy roots culture of Perilla frutescens L. and production of phenolic acids

Perilla frutescens L. is a kind of phenolic acids riches plant. Rosmarinic acid (RA) is the main phenolic acid component in perilla. It has various pharmacological activities such as anti-allergic, anti-inammatory, and antioxidant. In this study, the hairy roots culture of Perilla was established from leaves explants using Agrobacterium rhizogenes C58C1, A4, R1000, and the content of phenolic acids was also determined. The highest RA content in the �ve obtained fast-growing lines reached 19.08mg/g (DW). The effects of salicylic acid (SA) and methyl jasmonate (MeJA) treatment on phenolic acids (Protocatechuic aldehyde, caffeic acid, ferulic acid, RA, and ethyl caffeic acid ) accumulation in hairy roots were also investigated. SA can promote phenolic acid accumulation; the content of RA reached 20.92 mg/g (DW) on day nine after treatment. MeJA can upregulate the RA content to 17.36mg/g (DW) on day three, and then the RA content declined to 4.27mg/g (DW) on day 9. These results showed the high potential of perilla hairy roots to produce phenolic acids. It would be an e�cient method for the large-scale production of RA in P. frutescens.
Health economic assessment tools (HEAT) for walking and for cycling

Physical inactivity is a significant public health problem in most regions of the world, which is unlikely to be solved by classical health promotion approaches alone. The promotion of active transport (cycling and walking) for everyday physical activity is a win-win approach; it not only promotes health but can also lead to positive environmental effects, especially if cycling and walking replace short car trips. Cycling and walking can also be more readily integrated into people’s busy schedules than, for example, leisure-time exercise. These forms of physical activity are also more practicable for groups of the population for which sport is either not feasible because of physical limitations or is not an accessible leisure activity for economic, social or cultural reasons. There is a large potential for active travel in European urban transport, as many trips are short and would be amenable to being undertaken on foot or by bicycle. This, however, requires effective partnerships with the transport and urban planning sectors, whose policies are key driving forces in providing appropriate conditions for such behavioural changes to take place. This has been recognized by a number of international policy frameworks, such as the Action Plan for implementation of the European Strategy for the Prevention and Control of Noncommunicable Diseases 2012–2016, adopted by the WHO Regional Committee for Europe (1). The strategy identifies the promotion of active mobility as one of the supporting interventions endorsed by WHO Member States to address this highpriority topic in the European Region, as do other international policy frameworks such as the Toronto Charter for Physical Activity launched in May 2010 as a global call for action (2).
A hard molecular nanomagnet from con�ned paramagnetic 3d-4f spins inside a fullerene cage

Reducing inter-spin distance can enable effective magnetic interactions to realize outstanding magnetic properties, which, however, remains rather challenging. Here, an unprecedented 3d-4f metal cluster (Dy2VN) was constructed inside a C80 cage, affording a heretofore unseen metallofullerene containing both paramagnetic 3d and 4f metal ions. Signi�cantly suppressed 3d-4f (Dy-V) distances, due to the unique cage con�nement effect, are directly recorded by crystallographic and theoretical analysis of Dy2VN@Ih(7)-C80. Consequently, superior magnetic coupling (Jtotal, Dy-V = 53.30 cm-1; Jtotal, Dy-Dy = -18.75 cm-1) is obtained, leading to a record-high magnetic blocking temperature among all reported 3d-4f single-molecule magnets and huge coercive �eld of 2.73 Tesla. Our work puts forth a new class of single-molecule magnets with both paramagnetic 3d and 4f metals con�ned in a fullerene cage, of which superior and tunable magnetic properties are envisioned thanks to the unique cage con�nement effect and the diverse composition of the entrapped magnetic core.
Low-temperature phase diagrams of quantum lattice systems. III, Examples

We use the low-temperature expansion and the extension of Pirogov-Sinai theory developed in [1], and the perturbation theory of [2] to describe the phase diagrams of two families of fermionic lattice systems at low-temperature: the balanced model and a variant of the t — J model.
PRACTICAL FRAMEWORK FOR SOFTWARE INTEGRITY MEASUREMENT BASED ON STATIC ANALYSIS

Quality metrics technology is a disciplined approach used to evaluate, predict, and specify the software quality in terms of software integrity. The obtained metrics interprets the criteria influencing the implementation of software integrity. The primary goal of software integrity regarding security is ensuring the commendable and basic data around the organizations and makes it effectively achievable. In this study, we proposed two methods for finding the integrity bugs in software implementation. The first methods involve determining and consolidating the criteria of evaluation while in the second method we employed Chidamber and Kemerer Java Metrics (CKJM) to compute the software quality metrics. Furthermore, three open-source Java frameworks are utilized to analyze the effect of quality metrics on the implementation of software integrity. The proposed framework enabled to examine the most extensively employed metrics to test software integrity i.e the number of children, the coupling between objects, and response for class respectively. The empirical results suggested that the metrics calculated by the proposed framework have a significant effect on software integrity. The benefits of the proposed system to the absolute use of measurements at steady appearances of software products help in the early identification of software quality-related issues. Specific appraisal of quality levels gives better administration permeability and empowers auspicious dynamics.
RESEARCH ON STRATEGIES FOR WALKING UNDER CONTEXT OF GLOBAL PEDESTRIAN CULTURE:A CASE STUDY ON ZURICH

Based on in-field surveys and interviews,this paper conducts a thorough analysis on the strategies for pedestrian system development in Zurich by four sections:constituting and implementing policies for city pedestrian system,improving the land use mode to integrate public transportation and the pedestrian system,restricting the use of cars in city transportation system,and further improving the conditions of the pedestrian system.It is expected to provide useful references for the development of pedestrian system in China.
Effect of storage conditions on peripheral leukocytes transcriptome

Introduction Peripheral blood leukocytes are essential components of the innate and adaptive immune responses. Their transcriptome reflects an individual’s physiological and pathological state, consequently bulk and single-cell RNA sequencing have been used to assess health and disease. As RNA is dynamic and could be affected by ex vivo conditions before RNA stabilization, we have assessed the influence of temporary storage on the transcriptome. Methods We collected peripheral blood from six healthy donors and processed it immediately or stored it either at 4□ or room temperature (RT, 18–22□) for 2h, 6h and 24h. Total cellular RNA was extracted from leukocytes after red blood cells lysis and the transcriptome analyzed using RNA sequencing. Results We identified 152 up-regulated and 12 down-regulated coding genes in samples stored at 4□ for 24h with most of the up-regulated genes related to nucleosome assembly. More coding genes changed expression at RT, with 1218 increased and 1480 decreased. The increased genes were particularly related to mRNA processing and apoptosis, while the decreased genes were associated with neutrophil activation and cytokine production, implying a reduced proportion of neutrophils, which was confirmed by leukocyte subsets analysis. Most house-keeping genes changed expression during storage, but genes such as TUBB and C1orf43 were relatively stable and could serve as reference genes. Conclusion Temporary storage conditions profoundly affect leukocyte gene expression profiles and change leukocyte subset proportions. Blood samples stored at 4□ for 6h largely maintain their original transcriptome.