Miracle-dz/UniFAR-FTUs huggingface dataset:
Does central bank communication really lead to better forecasts of policy decisions? New evidence based on a Taylor rule model for the ECB

Nowadays, it is widely believed that greater disclosure and clarity over policy may lead to greater predictability of central bank actions. We examine whether communication by the European Central Bank (ECB) adds information compared to the information provided by a Taylor rule model in which real-time expected inflation and output growth are used. We use five indicators of ECB communication that are all based on the ECB President’s introductory statement at the press conference following an ECB policy meeting. Our results suggest that even though the indicators are sometimes quite different from one another, they add information that helps predict the next policy decision of the ECB. Furthermore, also when the interbank rate is included in our Taylor rule model, the ECB communication indicators remain significant.
Some Factors Affecting the Choice of Customers regarding Purchasing Goods, Food & Beverage Service Online

The form of consumer shopping in recent years has had a remarkable change, especially when the number of internet users is increasing not only in urban areas but also in rural areas in Vietnam. Consumers gradually turn to prefer online shopping over traditional shopping because of its convenience and time savings. More and more businesses are participating in and investing more in this form of e-commerce to catch up with current customer trends, making the page edge between businesses extremely fierce to gain market share. Along with the increase in choice, customer requirements are also higher and higher, not only at convenience and saving time. This article is made by FPT university students through comparison and comparison of statistics from reputable sites and references to related documents, focusing on research on the factors affecting choice selection of customers on online shopping sites in Vietnam.
A Systematic Literature Review: Blockchain Based Solutions for IoT’s

--In the modern era, Internet of things brought revolution in the everyday life activities by automation in everyday functions, modifying the approach of people to collaborate with one another or with the devices. Actually, the main feature of IoTs is direct device to device interrelationship. Their benefits range from little domestic devices to heavy industrial systems. Despite of numerous advantages of IoTs, their execution contains different issues mostly due to the severe substance of the functions by implement and because of their narrow computing abilities. Blockchain is the forthcoming annoying technology field that has achieved the attention of IoT sectors to tackle the problems by becoming encountered by themselves. In the article, the relevant issues in IoT field are pointed by us and then examined by the method how these issues can be resolved with the help of Blockchain features. Furthermore, we have also identified issues that appear while allocation of blockchain (BC) in Internet of things (IoTs).
lsirm12pl: An R package for latent space item response modeling

The latent space item response model (LSIRM; Jeon et al., 2021) allows us to show interactions between respondents and items in item response data by embedding both items and respondents in a shared and unobserved metric space. The R package lsirm12pl implements Bayesian estimation of the LSIRM and its extensions for different response types, base model specifications, and missing data. Further, the lsirm12pl offers methods to improve model utilization and interpretation, such as clustering of item positions in an estimated interaction map. lsirm12pl also provides convenient summary and plotting options to assess and process estimated results. In this paper, we give an overview of the methodological basis of LSIRM and describe the LSIRM extensions considered in the package. We then present the utilization of the package lsirm12pl with real data examples that are contained in the package.
Is There a Relationship Between Serum Procalcitonin Level and Mortality and Disease Severity in Patients Diagnosed with COVID-19?

Objective: Coronavirus disease (COVID-19) exhibits a spectrum of clinical conditions, ranging from mild upper respiratory tract disease to acute respiratory distress syndrome (ARDS), with hyperinflammation playing a pivotal role in the development of ARDS. Procalcitonin (PCT) serves as a biomarker indicating hyperinflammation. Our study aimed to assess the correlation between serum PCT levels and disease severity, as well as in-hospital mortality in COVID-19 patients. Material and Methods: The study included patients hospitalized with COVID-19 in our clinic between November 2020 and March 2021. Demographic characteristics, vital signs, comorbidities, and laboratory parameters on the admission day were recorded. Patients were categorized into non-severe and severe COVID-19 based on disease severity. Serum PCT values were compared between these groups. Additionally, patients were further divided into discharged and in-hospital mortality groups, with serum PCT values compared between these subgroups. Results: Out of 137 patients, 78 (57%) had severe COVID-19, and in severe cases, PCT values were significantly higher compared to the non-severe group (p<0.001). Twenty patients (14.6%) died during hospitalization, and in the in-hospital mortality group, PCT values were significantly elevated compared to the discharged group (p=0.014). Using a serum PCT value cut-off >0.12 to predict mortality yielded a sensitivity of 70%, specificity of 56.41%, positive predictive value (PPV) of 21.54%, and negative predictive value (NPV) of 91.67%. For predicting disease severity, a PCT value cut-off >0.124 resulted in a sensitivity of 44.87%, specificity of 27.12%, PPV of 44.87%, and NPV of 27.12%. Conclusion: Our study establishes a connection between elevated PCT levels in COVID-19 patients and increased mortality and disease severity. Monitoring serum PCT levels during hospitalization may aid clinicians in the early identification of potentially severe cases.
Utilization and Optimization of Histogram of Oriented Gradients and Machine Learning in Face Recognition System

Computer science and technology development in recent years has experienced great developments. This time, some types of technology digitise almost everything related to human life, including facial recognition. In recent years, various methods for recognising human faces have developed. One of them is using the Histogram of Oriented Gradients (HOG). On this occasion, an image processing system will be designed to recognise human faces using Histograms of Oriented Gradients (HOG) and machine learning such as Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). This system detects the winking of the face by using computer-recognisable points in the eye area from 68 facial landmarks. From these results, the distance between the upper and lower eyelids can be measured. If the distance (in pixels) is small enough, it can be interpreted as a wink. In addition, it is also limited by the distance of faces that can be detected to blink. In the end, if a recognised face blinks are detected, the time and date will be recorded. It will then open a solenoid lock using serial communication via Arduino Uno to become a security system. From 100 facial photos and 207 blink tests, 89.86% found that the computer could detect a "True Positive" wink. Besides, the recommended tolerance parameter value for this facial recognition system is between 0.42 and 0.48.
Blockchain and BlockDAG protocols Chains to DAGs Master Thesis

BlockDAGs have garnered significant attention in the realm of distributed ledgers as a potential solution to address the low transaction throughput of the longest-chain rule, as introduced in the ground-breaking Bitcoin whitepaper by Satoshi Nakamoto. We introduce the intricacies of BlockDAG protocols and highlight the utilization of BlockDAG protocols to achieve higher throughput compared to more traditional blockchain consensus protocols. The core objectives are (i) the assessment of inefficiencies stemming from the longest-chain selection rule, (ii) the implementation of a modified longest-chain protocol transformed into a BlockDAG protocol, and (iii) the analysis of the implementation under various parameters. This paper outlines the transformation of the N AKAMOTO consensus protocol into a DAG-based version, referred to as DAG-N AKAMOTO . The transformation is based upon a generic construct to transform a blockchain into a BlockDAG protocol, the transformation leads to increased throughput. Furthermore, the DAG-N AKAMOTO protocol is implemented in Python in the form of a simulation. In conclusion, we present a detailed analysis of the DAG-N AKAMOTO protocol’s performance through simulations, indicating that lowering the mempool similarity and optimizing the transaction selection strategy leads to increased throughput. The thesis suggests future work involving additional messaging mechanisms to further optimize transaction selection. In summary, this thesis offers a comprehensive exploration of a BlockDAG protocol and the potential to enhance throughput in distributed ledger technologies.
Reprogrammable and reconfigurable mechanical computing metastructures with stable and high-density memory

Previous mechanical meta-structures used for mechanical memory storage, computing and information processing are severely constrained by low information density and/or non-robust structural stiffness to stably protect the maintained information. To address these challenges, we proposed a novel reprogrammable multifunctional mechanical metastructure made by an unprecedented building block based on kinematic mechanism. The proposed meta-structure can achieve all abovementioned functionalities accompanying with high information density and promising structural stability. We attribute all these merits to the intrinsic kinematic bifurcations of structural units, which enable the periodic meta-structure with additional and independently deformable bi-stable structural segments, and multi-layered deformed configurations to significantly enlarge the available information bits. We validate the stable information storage are originated from the compatible deformations of local structural segments before and after bifurcations. We illustrated the stored information can be feasibly reprogrammed by magnetic poles. Our design strategy paves new way for creating novel functional mechanical metastuctures.
Stochastic approximation in infinite dimensions

Stochastic Approximation (SA) was introduced in the early 1950's and has been an active area of research for several decades. While the initial focus was on statistical questions, it was seen to have applications to signal processing, convex optimisation. %Over the last decade, there has been a revival of interest in SA as In later years SA has found application in Reinforced Learning (RL) and led to revival of interest. While bulk of the literature is on SA for the case when the observations are from a finite dimensional Euclidian space, there has been interest in extending the same to infinite dimension. Extension to Hilbert spaces is relatively easier to do, but this is not so when we come to a Banach space - since in the case of a Banach space, even {\em law of large numbers} is not true in general. We consider some cases where approximation works in a Banach space. Our framework includes case when the Banach space $\Bb$ is $\Cb([0,1],\R^d)$, as well as $\L^1([0,1],\R^d)$, the two cases which do not even have the Radon-Nikodym property.
Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey

Several adaptations of Transformers models have been developed in various domains since its breakthrough in Natural Language Processing (NLP). This trend has spread into the field of Music Information Retrieval (MIR), including studies processing music data. However, the practice of leveraging NLP tools for symbolic music data is not novel in MIR. Music has been frequently compared to language, as they share several similarities, including sequential representations of text and music. These analogies are also reflected through similar tasks in MIR and NLP. This survey reviews NLP methods applied to symbolic music generation and information retrieval studies following two axes. We first propose an overview of representations of symbolic music adapted from natural language sequential representations. Such representations are designed by considering the specificities of symbolic music. These representations are then processed by models. Such models, possibly originally developed for text and adapted for symbolic music, are trained on various tasks. We describe these models, in particular deep learning models, through different prisms, highlighting music-specialized mechanisms. We finally present a discussion surrounding the effective use of NLP tools for symbolic music data. This includes technical issues regarding NLP methods and fundamental differences between text and music, which may open several doors for further research into more effectively adapting NLP tools to symbolic MIR.
Ventajas competitivas de un centro comercial a partir de sus recursos

Due to the existing real estate offer, people can currently choose where to set up their business; as a result, shopping centers need to develop strategies to generate sustainable competitive advantages. Given the commercial activity in the city of Merida, Yucatan, in Mexico, and the competitive environment, it is advisable to analyze the operation strategies of shopping centers, as well as the resources involved. This paper analyzes the opinion of six tenants of a shopping center regarding the resources used and those available, as well as the strategies developed based on those resources, and the competitive advantages generated. A case study was elaborated that analyzed a shopping center in the city of Merida. Results show that the strategies of the shopping center studied are based on its tangible resources, as they are valuable, rare and inimitable, as well as potential creators of sustainable competitive advantages.
Journal of Ideas in Health Evaluation of salivary IL-10 and IL-8 as predictive biomarker in patient with oral and oropharyngeal squamous cell carcinoma

Background: The incidence and mortality rates of oral squamous cell carcinoma (OSCC) vary globally, yet the cancer continues to pose significant morbidity and mortality risks. This study sought to assess salivary IL-8 and IL-10 as biomarkers for identifying malignant oral lesions. Methods: Between March 2022 and May 2023, a case-control study at the Dental Center of New Baquba, Diyala, Iraq, collected saliva for IL-8 and IL-10 measurement via ELISA. An independent t-test compared IL-8 and IL-10 means based on age and gender, while ROC analysis gauged specificity and sensitivity. Results: The prevalence of OSCC was evenly distributed across genders, with a higher occurrence among patients aged over 55 years (87.5%). OSCC distribution in the oral cavity indicated the lips as the most affected area (40.0%), followed by the tongue (37.0%), and the hard and soft palate (12.50%). Statistically significant differences were noted in IL-10 and IL-8 levels between the study and control groups across all age brackets, except for IL-8 in the age ≤55 (p ≤ 0.05). Elevated mean levels of IL-10 and IL-8 were observed in the older age group (>55), with IL-10 showing a greater increase in males (71.99±3.6), while IL-8 levels were higher in females. IL-10 demonstrated higher sensitivity (90%) and specificity (60%) with an optimal cutoff point value of 43.29, whereas IL-8 exhibited a sensitivity of 80% and a specificity of 47.5% with an optimal cutoff point value of 44.48. Conclusion: Individuals aged over 55 showed higher IL-10 and IL-8 levels, particularly in males for IL-10 and in females for IL-8. Salivary biomarkers hold promise for early OSCC detection
Developing non-viral or viral vectors for efficient and targeted delivery of genetic material, such as DNA or RNA, for gene therapy applications

Gene therapy has emerged as a promising approach for treating a wide range of genetic and acquired diseases by introducing or modifying genetic material within cells. To achieve successful gene therapy, efficient and precise delivery of genetic material, such as DNA or RNA, to target cells is essential. This abstract explores the development of both non-viral and viral vectors for the delivery of genetic material in gene therapy applications. Non-viral vectors, including lipid nanoparticles (LNPs), polymer-based carriers, and cell-penetrating peptides, have gained significant attention due to their safety profile and ease of production. These vectors are designed to protect genetic material from degradation, facilitate cellular uptake, and release the cargo at the desired location. Recent advancements in nanotechnology have enabled the design of customizable non-viral vectors with enhanced delivery efficiency and reduced offtarget effects. Viral vectors, on the other hand, harness the natural infectivity of viruses to transport genetic material into target cells. Retroviruses, lentiviruses, adenoviruses, and adeno-associated viruses (AAVs) are commonly used viral vectors in gene therapy. Viral vectors offer high transduction efficiency but may trigger immune responses or pose risks of insertional mutagenesis. Efforts in vector engineering have led to the development of safer viral vectors with improved targeting capabilities and reduced immunogenicity. AAVs, in particular, have gained prominence due to their ability to achieve long-lasting gene expression with minimal adverse effects. Targeted delivery strategies aim to enhance vector specificity, ensuring that genetic material reaches the intended cell type or tissue. These strategies include modifying vector surface proteins, employing tissue-specific promoters, or utilizing ligand-receptor interactions. In conclusion, the successful application of gene therapy relies on the development of efficient and targeted delivery systems for genetic material. Non-viral and viral vectors offer distinct advantages and continue to evolve to meet the demands of gene therapy applications. Advances in vector design, safety, and targeting strategies hold promise for the continued progress of gene therapy as a transformative medical intervention.
Inertial Sensor Based Human Activity Identification System Using CNN- LSTM Deep Learning Technique

Inertial sensors embedded within smart devices are emerging research propel that gather information about people's actions. This valuable information can be further used to predict and analyses human behaviors using the latest techniques like machine learning and deep learning. The predictions made through these models have great importance in the field of medical medicine, like caring about elderly and mentally retarded patients and, they can also be used in security surveillance applications. In this proposed approach, a daily living Activity Recognition dataset, MHEALTH from Kaggle repository, which is built from the recordings of ten volunteers through the use of four inertial sensors of mobile devices placed at various body positions of volunteers. A hybrid CNN-LSTM deep learning model is implemented using Python libraries such as keras, pandas, tensorflow, numpy, etc. this model is a layered architecture of Convolutional Neural Network (CNN) to extract prime features from input data and to predict human activity in the long short-term memory (LSTM). The experimental results of the proposed model after hypertunning achieved accuracy of 98% over the MHEALTH dataset
Performance Comparison of Road Traffic Sign Recognition System Based on CNN and Yolo v5

Exemplary drivers prioritize adherence to traffic rules, and implementing traffic sign detection technology effectively supports such behavior, enhancing road safety for all road users. This paper brings a comparative analysis of road traffic sign recognition systems based on Convolutional Neural Network (CNN), and You Only Look Once version 5 (Yolo v5) on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The proposed recognition system consists of four stages: 1) dataset annotation; 2) data pre-processing to lower noise; 3) sign detection and classification; 4) performance analysis. The developed models can detect and recognize forty-three classes of traffic signs covering all existing traffic signs under varying light and noise conditions as available in the dataset. Libraries and tools like Keras, TensorFlow, SKlearn, OpenCV2, Google Colab, etc., have been used to pre-process the data in Python. The processed images will serve as the CNN and Yolo v5 training datasets. According to experimental findings, the CNN model outperforms the YOLO v5 model in successfully identifying the majority of traffic signals with a precision of 96.231%. The proposed system would be helpful for the development of autonomous vehicles and provide effective driving assistance messages.
Grid Construction of 5G Beam-Space via Matrix Completion and Principal Component Analysis

Deriving the spatial features of user equipment (UE) and traffic distribution is crucial for optimizing real-world wireless network performance, such as load balancing and improving cell-edge user experience. In this paper, we creatively construct the beam-space grid with high-dimensional beam reference signal receiving power (RSRP), serving as spatial reference system for 5G mobile networks. However, this is a challenging task due to i) access to 3D UE geographic information is limited by privacy protection and device settings, ii) beam-RSRP with up to 75% loss in measurement reports (MRs), iii) large computation and storage requirements of grid information caused by the exploding grid number in high-dimensional beam-space. A novel approach is designed to tackle these challenges, that coherently integrate low-rank matrix completion and dimension reduction techniques. Using telco big data, specifically MRs from live networks, we recover the beam-RSRP with high-precision and construct the beam-space grid even without geographic information. This research ensures stable derivation of spatial distribution features in the beam-space grid while efficiently processing grid information.
Enhancing Electrocardiogram Identity Recognition Using Convolutional Neural Networks With a Multisimilarity Loss Model

Identity recognition plays an important role in human civilization. With people’s further cognition of physiological signals, research on electrocardiogram (ECG)-based identity recognition is gradually carried out at home and abroad. At present, the accuracy of the ECG-based identity recognition is not high, and the generalization ability of the model is weak; to solve this problem, we propose a solution to identity recognition based on multisimilarity loss (MS Loss). First, we filter the signal from ECG identity (ECG-ID) database and feed it into the designed convolutional neural networks (CNNs) for training. After that, the extracted embeddings are then trained with K-nearest neighbor (KNN), support vector machines (SVMs), and Siamese neural networks (Siamese NNs) for recognition. To verify the effectiveness of the embeddings obtained by CNN, we also compared the MS Loss method with the direct training method. To verify the generalization ability of the mode, we collected ECG data from 20 members of the laboratory and established self-built database. Results show that the proposed model has advanced generalization ability than the state-of-the-art works and achieves 97.13% accuracy in the 90-person-scale database and 99.86% recognition accuracy in the 20-person-scale self-built database. This article uses a deep CNN to extract features via MS Loss from ECG signals and realizes quickly distinguishing different individuals, which provides a feasible and natural method for small-scale identification and reduces the difficulty of engineering generalization.