Reviewerly/exHarmony huggingface dataset:
Exploring the meaning of identity formation through the experiences of preliminary early childhood teachers

Objectives This study investigated the meaning of identity formation as early childhood teachers through the experiences of preliminary early childhood teachers.&#x0D; Methods Six preliminary early childhood teachers enrolled at universities located in Seoul and Gyeonggi-province were selected as research participants, and in-depth interviews were conducted to understand their experiences without exaggeration. Based thereon, Giorgi's “existential phenomenological analysis” was used to understand the meaning of lived experiences of preliminary early childhood teachers more concretely and reveal the meaning of it.&#x0D; Results Preliminary early childhood teachers were forming their identity through: first, the memories of their own childhood and early childhood teachers, second, self-esteem and values to pursue that are formed based on personal beliefs as early childhood teachers, and third, the experiences of learning and practice encountered during the preparation process of becoming preliminary early childhood teachers.&#x0D; Conclusions Time spent as preliminary early childhood teachers is important in order to form a positive early childhood teacher identity. In addition, it is necessary to provide various opportunities for teachers to talk to each other about the image and meaning of early childhood teachers from their own experiences. Furthermore, it is important to enhance the understanding of the field by strengthening practical training. It is also necessary to bring them to a sense of conviction that they are professionals by striving to improve the low social awareness on early childhood teachers, improving the working environment, and providing appropriate salaries.
DIAGNOSA PENYAKIT SARAF MANUSIA DENGAN METODE FORWARD CHAINING DALAM SISTEM PAKAR

Neurological disease is a disease that attacks the nervous system in the human body. Neurological consist of millions of nerve fibers that have parts of nerve cells that are interconnected and function as growth, brain development, feeling sensation, perception, thoughts and emotions, as well as movement and balance in the human body. Neurological disease is caused by impact of hard objects, exposure to chemicals, viruses or bacteria and inflammation caused by the regeneration of nerve cells themselves. Most people do not know for sure and underestimate the symptoms of neurological disease. An expert system is a knowledge system that provides information or solutions based on the knowledge of an expert to produce information and solutions. The method used in this study is the Forward Chaining method. The forward search process begins with the premise or input information (IF) then leads to conclusions or derivative information (THEN) such as, IF (Input Information) THEN (conclusion) in the form of data, evidence, findings or symptoms. While conclusions can be in the form of goals, hypotheses, explanations or diagnoses. In this study, an expert system will be designed that is used to diagnose human neurological diseases using the Forward Chaining method as a tool system or provide diagnostic results to users based on symptoms and know the types of neurological diseases and know how to treat them.
Strong Network Effects Eliminate Spence Distortions

Strong network effects generate multiple tipping equilibria, in which firms compete for the adoption of all consumers rather than the marginal consumer. In this scenario, a quality distortion—the Spence distortion—should be absent; this contradicts the well-established Spence distortion results in the literature on network economics. This inconsistency stems from the choice of equilibrium selection criterion. This paper shows that all popular selection criteria in that literature lead to Spence distortions, whereas risk dominance and its generalizations do not. This suggests that network market regulations based on Spence distortion arguments may be misguided.
Novel constructions of mutually unbiased tripartite absolutely maximally entangled bases

We develop a new technique to construct mutually unbiased tripartite absolutely maximally entangled bases. We first explore the tripartite absolutely maximally entangled bases and mutually unbiased bases in $\mathbb{C}^{d} \otimes \mathbb{C}^{d} \otimes \mathbb{C}^{d}$ based on mutually orthogonal Latin squares. Then we generalize the approach to the case of $\mathbb{C}^{d_{1}} \otimes \mathbb{C}^{d_{2}} \otimes \mathbb{C}^{d_{1}d_{2}}$ by mutually weak orthogonal Latin squares. The concise direct constructions of mutually unbiased tripartite absolutely maximally entangled bases are remarkably presented with generality. Detailed examples in $\mathbb{C}^{3} \otimes \mathbb{C}^{3} \otimes \mathbb{C}^{3},$ $\mathbb{C}^{2} \otimes \mathbb{C}^{2} \otimes \mathbb{C}^{4}$ and $\mathbb{C}^{2} \otimes \mathbb{C}^{5} \otimes \mathbb{C}^{10}$ are provided to illustrate the advantages of our approach.
Quantification of Predictive Uncertainty via Inference-Time Sampling

Predictive variability due to data ambiguities has typically been addressed via construction of dedicated models with built-in probabilistic capabilities that are trained to predict uncertainty estimates as variables of interest. These approaches require distinct architectural components and training mechanisms, may include restrictive assumptions and exhibit overconfidence, i.e., high confidence in imprecise predictions. In this work, we propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions. It is architecture agnostic and can be applied to any feed-forward deterministic network without changes to the architecture or training procedure. Experiments on regression tasks on imaging and non-imaging input data show the method's ability to generate diverse and multi-modal predictive distributions, and a desirable correlation of the estimated uncertainty with the prediction error.
Bitcoin Investors' Style, Skill, Sentiment, Seasonal Trading, and Anchoring Bias

I examine the style and skill of Bitcoin traders in a U.S. based exchange, Coinbase. Bitcoin traders are contrarian. Their order flow displays daily and weekly seasonality and some evidence of market timing skills, especially among large and medium size traders. The positive returns to contrarian trades are consistent with liquidity provision. Additionally, large Bitcoin traders anchor to the 30-, 90-, and 120-day highs. Investor sentiment and attention intensify buying among smaller and selling among larger traders, but these trades are unprofitable. Analysis of trades from an alternative exchange shows that the order flow of traders in Asia positively correlates with concurrent Bitcoin returns, and do not reveal market timing skills.
Research on Automatic Data Collection Method of Electronic Countermeasure Training

Based on the actual demand of electronic countermeasure(ECM) training, in order to overcome the disadvantages of manual training data collection at present, a software automatic collection technology is proposed, and the corresponding algorithm is given. Compared with the traditional manual collection method, this technology has the advantages of high efficiency, accuracy, easy transplantation, and easy application of collected data. It can be used in the future electronic countermeasures training tasks to improve the efficiency and quality of data collection, thus improving the effectiveness of training evaluation and achieving the training purpose.
Artificial Intelligence Tagging Algorithm Coupling University Legal Intelligence Perception Framework

Based on the artificial intelligence tagging algorithm, user scenario theory and qualitative research theory, this paper discusses the elements of intelligent data services for think tanks, and attempts to establish an accurate and usable network public opinion evaluation system to monitor the heat of emergencies. The theoretical foundation of the legal service platform is to integrate the theories of grassroots social governance, the modernization of the rule of law, the concept of "Internet +", and the business management system of judicial and administrative organs into the construction system of the public legal service platform. evaluation mechanism.
Tracking SQL Server Configuration Changes

This chapter will outline the ways you can view configuration changes in SQL Server. You will want and need to capture these changes since they can have a large impact on your database server. Configuration changes can include enabling SQL Agent or Database Mail. They can also include things like changing max and min memory. There are a lot of different configuration settings. Most server configuration options are available via SSMS. Some you can only access with sp_configure.
Cryptographic Solutions for Cloud-Based Storage System

Nowadays cloud computing is a driving force which has a large impact on each aspect of our lives and widely used in today's business structure. With its capacity and capability, it is widely accepted by many organizations and users. Cloud computing provides numerous benefits to end users and companies in terms of cost, maintenance, management due to which many organizations prefer to use its services with open hands. With the increasing demand, day-by-day service providers also increased and the user has a choice to choose the best one based on their demand Cloud Storage is one such service that provides cost effective storage solution to the users. They provide unlimited storage to the users based on the requirement and charge according to that only. User can rely on them for the storage but apart from the numerous benefits security and privacy remains the biggest concern whenever a user moves to cloud services. Security triad comprises of authentication, Integrity, and availability remains the concern for every user while moving towards cloud-based services. Almost everyday industry and academician working on finding an effective and efficient way, which could provide secure migration of user data in the cloud. One of the solutions could be the use of cryptographic techniques to provide data security. Cryptography is accepted largely to ensure the privacy and security of data in cloud computing. In this paper, several cryptographic technique discussed which are expected to provide the solution to the user's problem when they tend to move towards cloud computing.
A deep feature-level fusion model for masked face identity recommendation system

The widespread occurrences of airborne outbreaks (e.g., COVID-19) and pollution (e.g., PM2.5) have urged people in the affected regions to protect themselves by wearing face masks. In certain areas, wearing masks amidst such health-endangering times is even enforced by law. While most people wear masks to guard themselves against airborne substances, some exploit such excuses and use face masks to conceal their identity for criminal purposes such as shoplifting, robbery, drug transport, and assault. While automatic face recognition models have been proposed, most of these models aim to identify clear, unobstructed faces for authentication purposes and cannot effectively handle cases where masks cover most facial areas. To mitigate such a problem, this paper proposes a deep-learning-based feature-fusion framework, FIREC, that combines additional demographic-estimated features such as age, gender, and race into the underlying facial representation to compensate for the information lost due to mask obstruction. Given an image of a masked face, our system recommends a ranked list of potential identities of the person behind the mask. Empirical results show that the best configuration of our proposed framework can recognize bare faces and masked faces with the accuracy of 99.34% and 97.65% in terms of Hit@10, respectively. The proposed framework could greatly benefit high-recall facial identity recognition applications such as identifying potential suspects from CCTV or passers-by’s cameras, especially during crisis times when people commonly cover their faces with protective masks.
Synthesis of Cost-Optimal Multi-Agent Systems for Resource Allocation

Multi-agent systems for resource allocation (MRAs) have been introduced as a concept for modelling competitive resource allocation problems in distributed computing.An MRA is composed of a set of agents and a set of resources.Each agent has goals in terms of allocating certain resources.For MRAs it is typically of importance that they are designed in a way such that there exists a strategy that guarantees that all agents will achieve their goals.The corresponding model checking problem is to determine whether such a winning strategy exists or not, and the synthesis problem is to actually build the strategy.While winning strategies ensure that all goals will be achieved, following such strategies does not necessarily involve an optimal use of resources.In this paper, we present a technique that allows to synthesise cost-optimal solutions to distributed resource allocation problems.We consider a scenario where system components such as agents and resources involve costs.A multi-agent system shall be designed that is cost-minimal but still capable of accomplishing a given set of goals.Our approach synthesises a winning strategy that minimises the cumulative costs of the components that are required for achieving the goals.The technique is based on a propositional logic encoding and a reduction of the synthesis problem to the maximum satisfiability problem (Max-SAT).Hence, a Max-SAT solver can be used to perform the synthesis.From a truth assignment that maximises the number of satisfied clauses of the encoding a costoptimal winning strategy as well as a cost-optimal system can be immediately derived.
Meta-learning from Learning Curves: Challenge Design and Baseline Results

Meta-Iearning has been widely studied and implemented in many Automated Machine Learning systems to improve the process of selecting and training Machine Learning models for new tasks, by leveraging expertise acquired on previously observed tasks. We design a novel meta-learning challenge aiming at learning-to-learn from one of the most essential model evaluation data, the learning curve. It consists of multiple model evaluations collected during the process of training. A meta-learner is expected to apply a learned policy to learning curves of partially trained models on the task at hand, to rapidly find the best task solution, without training all potential models to convergence. This implies learning the exploration-exploitation trade-off. Our challenge is split into two phases: a development phase and a final test phase. In each phase, a meta-learner is meta-trained and meta-tested on validation learning curves (development phase) or test learning curves (final test phase). During meta-training, the meta-learner is allowed to learn from the provided learning curves in any possible way. In meta-testing, we borrowed the common Reinforcement Learning setting in which an agent (a meta-learner) learns by interacting with an environment storing pre-computed learning curves. A meta-learner must pay a cost (corresponding to the actual training and testing time) to reveal learning curve information progressively. The meta-learner is evaluated and ranked based on the average area under its learning curves. This challenge was accepted as part of the official selection of WCCI 2022 competitions.
Urban Transportation Monitoring Based on Concept of Complex Adaptive System

In the face of the dual pressures from rapid urbanization and motorization, confidence and decision-making in solving transportation problems should stem from understanding instead of courage and determination. The greatest value of transportation big data lies in helping understand the research objects with complex adaptive system characteristics, and intervene in the evolution of the system through “response in a timely manner”, with a view to steering urban transportation on the track of sustainable development.
Reconfiguration Design and Implementation of DPRAP-Based Multi-viewpoint Image Coding Algorithm

Aiming at the problem that the existing texture map and depth map coding algorithm software in Three Dimensional-High Efficiency Video Coding (3D-HEVC) takes a long time to code, and the utilization of special hardware is high, this paper proposes a multi-view image coding framework based on reconfigurable hardware. By reusing the same module algorithm in the coding loop, different module algorithms in the coding loop are reconstructed. Finally, four coding loops of the independent view texture map, depth map, dependent-view texture map, and depth map in multi-view coding are realized by using Dynamic Programmable Reconfigurable Array Processor (DPRAP). Experimental results show that the hardware resources required by the reconfigurable multi-view image coding scheme based on DPRAP are reduced by 7.79% LUTs and 7.88% Registers compared with those before reconstruction, thus achieving the purpose of reducing the use of hardware resources. Compared with the test results of HTM16.0, the PSNR loss of reconstructed images after the implementation of the reconfigurable multi-view coding proposed in this paper is only 0.99%. Therefore, this research has a certain reference value for real-time video coding of the 3D-HEVC algorithm.
Bidding Price Forecast of Construction Projects Based on Machine Learning Algorithm

In this study, we focus on the need for the parameters of the Support Vector Machine (SVM) which is optimized by the Particle Swarm Optimization (PSO) algorithm. SVM has higher recognition advantages in solving nonlinear problems for small samples and high dimension pattern problems. By choosing the Gauss longitudinal kernel function as the kernel function of the nonlinear decision function, the nonlinear problem is converted to a linear problem by using SVM. A detailed presentation of the required kernel function is defined. Under the premise of guaranteeing the parameter optimization of SVM, PSO algorithm for the optimal speed improves the computing speed of SVM. Through Matlab2014b, the results show that the bidding price prediction strategies based on the PSO-SVM algorithm have significant feasibility.
Object Detection Algorithm Based on Global Information Fusion

The output channel of the Fully Convolutional One-Stage Object Detection (FCOS) feature extraction network drops sharply before input FPN. Feature information is severely lost. This paper proposes an object detection algorithm based on global information fusion. First, a multi-scale global aggregation module is designed. This module extracts the information of multi-scale receptive fields, aggregates global features, and enhances local features. The last layer features of the feature backbone network are downsampled, and fused with feature downsampling enhanced using a multi-scale global aggregation module. The enhanced feature upsampling and shallow feature fusion is output through FPN. The proposed algorithm uses Generalized Intersection over Union loss instead of Intersection over Union loss, which can make the target localization more accurate. The detection accuracy of the algorithm in this paper on the PASCAL VOC dataset reaches 82.8%, which is 2.0% higher than FCOS. The accuracy on the KITTI dataset reaches 82.4%, which is 4.2% higher than FCOS. And the detection speed meets the real-time requirements.
Processamento de Linguagem Natural

This book of the XXXVII Brazilian Symposium on Databases (SBBD 2022) includes three book chapters written by the authors of the selected short courses and presented in the edition of the event held from September 19 to 23, 2022. The chapters address issues related to Natural Language Processing, NoSQL Database Design, and Using Meta-Learning in Deep Learning tasks. The short course program committee was composed of professors Ticiana L. Coelho da Silva (UFC), Damires Yluska de Souza Fernandes (IFPB), and Anne Magály de Paula Canuto (UFRN), under the coordination of the former. The richness of this issue can be mainly credited to the authors and reviewers. We thank them a lot for their insightful contributions and discussions during SBBD 2022.
ATTACKS IN INTERNET OF THINGS (IOT): BLOCKCHAIN METHOD FOR SECURITY

The Physical devices or objects along with the sensors, controller and actuator connected to the Internet together called as Internet of Things (IoT).The IoT devices are increasing day by day.This drastic demand of IoT may be the most unsecure type of network which can be encountered nowadays.The Internet of Things becoming the reason for the vast development of the associated technologies such as Cloud Computing, Wireless mobile networks, near-field communications, etc. IoT is changing the direction of communication around the world.Since, IoT devices are typically gathering sensitive information from unattended or hostile environments, they are exposed for security attacks, which are strongly affecting the user's privacy and the network performance.The deployment of efficient security and privacy protocols in IoT networks is extremely needed to ensure or secure confidentiality, authentication, access control and integrity among others.In this paper, we are analyzing the security problem of IoT, various attacks that is seen in IoT devices.This paper proposed the Blockchain method, by which we can secure our IoT system.
NFT tokens in the context of copyright on the works

The article attempts to consider legal issues arising in connection with the use of blockchain technologies in the field of intellectual property rights to works of art. The article explores the essence of NFT tokens associated with copyright objects, analyzes alternative options for attributing them to the institute of real rights or intellectual property rights. The legal regime of certain types of tokens and their legal regulation in foreign legislation is considered. The conclusion is formulated that the NFT token is a digital confirmation of copyright related to a digital work. The current copyright regulations provide protection to the original work associated with the token, to the extent that protection is granted to works that exist in the real world. It is proposed to consider the NFT token registered in the blockchain registry as proof of the legitimacy of the copyright holder&amp;apos;s title, the authenticity of the copy of the work and its origin (provenance). It is proved that the illegal issue of a token associated with a work, without the consent of the copyright holder of this work, should be recognized as the manufacture of a counterfeit copy of the work, and the person who illegally issued such a token is liable for creating a counterfeit copy of the work if it is possible to identify the subject of responsibility and there is evidence of his bad faith.