Reviewerly/exHarmony huggingface dataset:
Modeling The Number Of Tuberculosis Cases In West Java Using The Negative Binomial Approach

Objective: This study aims to model the number of Tuberculosis cases in West Java Province in 2021 using the Negative Binomial Regression approach. Methods: This study used quantitative analysis uses secondary data from the Central Bureau of Statistics website and the Health Office of West Java Province. 27 West Java districts/cities were studied. The number of tuberculosis cases was assumed to be affected by population density, poverty, sanitation, and health complaints in the past month. Negative Binomial Regression was used to analyse data. Results: The results showed that Poisson Regression caused overdispersion, which was solved using the Negative Binomial Regression approach. The Negative Binomial Regression model passed a detailed test. The partial test showed that only the variable percentage of low-income persons and the variable percentage of people with health concerns significantly affected the model with regression coefficients of 0.8755 and 1.0318, respectively. The final Negative Binomial Regression model with the lowest Akaike Information Criterion value of 491.9 is best for this investigation. Conclusion: The most suitable model for modelling the number of Tuberculosis cases in West Java Province in 2021 is the Negative Binomial Regression model with independent variables that significantly influence the model, namely the percentage of poor people and the percentage of people who have had complaints recently.
EduCardia: an ICT-Enabled Methodology for Assessment and Improvement of Socioemotional Competencies of Students Based on In-Person and Online Activities

Creating a positive school environment in classrooms can enhance the academic and social outcomes of students. In order to promote inclusive education, it is crucial that all students develop the skills to communicate, understand each other, and work together effectively. Although there are numerous research findings and guidelines available for developing social and emotional learning (SEL) materials and implementing SEL training in classrooms, there are limited methodologies and tools openly accessible for assessing SEL competencies. In the current work, we present EduCardia as a methodology to evaluate and enhance the socio-emotional competencies of primary and secondary level students. EduCardia provides both SEL material development and digital tools for teachers to efficiently perform assessment processes in their classrooms and monitor the progress of students' socio-emotional competencies. With access to assessment results, group-based educational activities can be organized to improve inclusion, social cohesion, and promote educational equality. The EduCardia methodology aims to create a supportive school environment that can prevent all forms of discrimination and foster socio-emotional development of students.
Democratization perspectives through strengthening the person's security culture

The democratization of human society is directly related to the need to develop and impose a new model of approach to the security phenomenon. This new approach is an integral part of the security culture. The human society, but also the individual person, possesses a set of values and models regarding the counteracting of the security dangers and risks that form the security culture, among which the safety of the person occupies a central place. We believe that it is necessary to amplify, in the context of the globalization process, the culture of security, especially since the contemporary society is one of risks. Security culture, supported by the concept of human security, which emphasizes the non-military dimensions of security, also involves promoting the protection of human rights and fundamental freedoms. Security culture also requires the establishment of mechanisms to strengthen security education and the ability of citizens to protect themselves against the diversity of security risks.
Multi-Feature Fusion Identity Generation Method for Fixed Location 5G Terminal Devices

While 5G is developing rapidly, it also faces greater security threats, and achieving reliable terminal identification is a prerequisite for ensuring 5G network security. For the identity reliability problems of the accessed fixed location 5G terminal devices, a multi-feature fusion identity generation method is proposed. Firstly, the channel feature sequence that can reflect the location information of 5G terminal devices is obtained at the base station side based on Sounding reference signal; then the envelope information of the intercepted leading signal is extracted at the base station side using the method of envelope detection in communication reconnaissance to obtain the estimated time width of the leading signal; finally, a multi-feature fusion identity generation method is proposed by combining the permanent identifier SUbscription Permanent Identifier that represents the terminal identity. The study shows that the key authentication based on the fusion of multiple features improves the identification security significantly compared with the key authentication based on the single feature identity, and is also more reliable than other terminal authentication techniques.
USE OF BLOCKCHAIN IN VOTING SYSTEM

What is blockchain technology?since its invention in 2008. The first decentralized by Satoshi Nakamoto. He improved the overall design using a Hashcash like method. The design was implemented as a core component of the cryptocurrency bitcoin where its serves as the public ledger for all transaction on the entire network. It is a digital ledger technology that allows for secure and transparent record-keeping of transactions.In a blockchain, every participant has a copy of the ledger, and all transactions are verified and recorded in a decentralized and transparent manner. The security of the blockchain comes from the fact that once a block is added to the chain, it cannot be altered or deleted without the consensus of the network.Besides its original use case in digital currencies, blockchain technol- ogy has been applied to various industries, such as supply chain management, healthcare, real estate, and voting systems. Its po- tential benefits include increased security, transparency, effi- ciency, and decentralization. One of the key benefits of using blockchain technology in voting is the ability to create a trans- parent and decentralized system. This means that anyone can verify the accuracy of the results, and there is no need for a centralized authority to oversee the process. The blockchain acts as a distributed ledger, with each node on the network hold- ing a copy of the transaction history. Key Words: Blockchain, Voting
Novelty detection for multi-label stream classification under extreme verification latency

Multi-Label Stream Classification (MLSC) is the classification streaming examples into multiple classes simultaneously. Since new classes may emerge during the streaming process (concept evolution) and known classes may change over time (concept drift) it is challenging task. In real situations, concept drift and concept evolution occur in scenarios where the actual labels of arriving examples are never available; hence it is impractical to update decision models in a supervised fashion. This is known as Extreme Verification Latency, a topic that has not been well investigated in MLSC literature. This paper proposes a new method called MultI-label learNing Algorithm for Data Streams with Binary Relevance transformation (MINAS-BR), integrated with a Novelty Detection (ND) procedure for detecting concept evolution and concept drift, updating the model in an unsupervised fashion. Furthermore, since the label space is not static, we propose a new evaluation methodology for MLSC under extreme verification latency. Experiments over synthetic and real-world data sets with different concept drift and concept evolution scenarios confirmed the strategies employed in the MINAS-BR and presented relevant advances for handling streaming multi-label data.
FPGA Implementation of Medical Image Fusion

obtain clear images 3) To maintain the accuracy -PCA II.
The Effect of National Education Policy 2020 on Teacher Education

Guru Brahma, Guru Vishnu, Guru Devo Maheshwaraha&#x0D; Guru Saakshat Para Brahma, Tasmai Sree Gurave Namah&#x0D; India enlightened the country in all periods of its history. The history of teacher education in India is the oldest history of the Indian education system. Saint Kabir explains the eminent personality teacher as a "Guru."Guru Govind Dau Kadhe, Kake Lagoo PaiBalihari Guru Aapne, Govind Deyo Bathai.The responsibility of passing on the knowledge of the 'Vedas,' 'Upanishad,' and other holy writings, as well as the teaching itself, was assigned to Brahmins, who were known as 'Gurus' and passed down from one generation to the next. Only those with the greatest experience and knowledge served in teaching roles. "Enjoying the same social standing and reputation as all individuals who excellently serve society," the World Encyclopedia of Teaching and Teacher Education (1987) said, "Educational programmes and levels should be more and more equivalent with the physician's education. "The National Education Strategy 2020 will characterize society as one in which prominent people are referred to as "gurus" for how they impart their knowledge, skills, and ethics to pupils in the most beneficial and optimum way. There have to be initiatives to encourage the most talented individuals to choose careers in teaching. People who have a high regard for educators and a high regard for the teaching profession as a whole. The most recent developments in teacher education should be more forcefully implemented in training institutions, both in terms of the material they teach and the methodology they use with local languages. Service conditions and the empowerment of teachers should be brought up to the necessary levels in conjunction with the quality of instructors' motivation. The level of education that a teacher needs to have is a factor in both selection and placement decisions.
Review of the national experience of ecological education in sustainable development goals (the case study of RNPLS&amp;T’s contests)

The experience of Russian libraries in ecological education for the sake of sustainable development goals (SDGs) is examined. The winner projects submitted for RNPLS&amp;T’s contests in 2018–2022 are reviewed in brief. The main trends in ecological education and SDG advancement are discussed.
The Effect of Service Information System Quality on the Intensity of Use of Digital Learning Service Systems

Information systems are used by organizations to help the organization's operations become more efficient in their role as a tool to win the competition. Data processing has become an important part of the Management Information System that will affect every organization's activities. Universitas Hasanuddin's management information system has developed an online information system through the website, namely the Learning Management System. The Universitas Hasanuddin Learning Management System is prepared to support and expedite the learning process. However, the current learning management system has not been understood by lecturers and students due to poorly understood features, slow system access, and a lack of socialization of the use of the learning management system at the department or study program level, so that it does not encourage lecturers and students to use it more often. Learning Management System. This study aims to determine how the system's quality significantly affects the intensity of use. This type of quantitative descriptive research uses a survey approach in this study. The respondents in this study were adjusted for lecturers with a sample of 100 people and students with a sample of 100 people. Data collection techniques included Questionnaires and Document Studies. Data analysis techniques using Structural Equation Modeling will be processed using AMOS software version 24.00. This study found that the effect of system quality on the intensity of use resulted in a cr value of 0.328 &lt; 1.96 and a p-value of 0.74 &gt; 0.05, so there was no significant effect of system quality on the intensity of use. This answers the hypothesis in the study. Namely, H0 is rejected, and H1 is accepted.
MODERATING MENTAL HEALTH: ARE AUTOMATED SYSTEMS TOO RISK AVERSE?

Across commercial social media platforms and dedicated support forums alike, mental health content raises important questions about what constitutes risk and harm online, and how automated and human moderation practices can be re-configured to accommodate resilient behaviours and social support. In work with three Australian mental health organisations that provide successful discussion and support forums, this paper identifies moderation practices that can help to re-think how mental health content is managed. The work aims to improve safety and resilience in these spaces, drawing insights from successful practices to inform algorithmic and moderator treatment of mental health content more widely across social media. Through an analysis of interviews and workshops with forum managers and moderators, I paper argue that platforms must incorporate strengths-based context (resilience indicators) into their moderation systems and practices, challenging simplistic assessments of mental health content as risk and harm.
Simultaneous in-and-out-of-plane deformation measurements using speckle projection and fluorescent digital image correlation

A new method for measuring in-and-out-of-plane deformation using a single 3CCD color camera is proposed. First, print a layer of black primer on the surface of the tested object. Then use the spray gun to make red fluorescence on the surface of the tested object. Finally, use the projector to project the computer-generated blue speckle pattern with black primer onto the surface of the tested object, and capture the reference pattern and the deformed pattern with a color camera at the same time. This method uses three independent red-green-blue chips on the 3CCD color camera sensor to separate the captured patterns, the in-plane deformation is measured by Fluorescent Digital Image Correlation (FDIC), and the out-of-plane deformation is measured by Speckle Projection Profilometry (SPP). Compared with the traditional 3-Dimensional Digital Image Correlation (3D-DIC), this method only uses one color image to contain in-and-out-of-plane deformation information , and does not need complex stereo calibration, which greatly reduces calculation workload. Experimental results of step rigid body translation, disc center loading and free end loading of cantilever beam demonstrate that the proposed technique is easy to implement and can be applied to practical in-and-out-of-plane deformation measurement with high accuracy in engineering field.
DR-UNet: dynamic residual U-Net for blind correction of optical degradation

Due to the size limitation of mobile devices, their optical design is difficult to reach the level of professional equipment. Corresponding restoration methods are then needed to compensate for the shortage. However, most of the models are still static, which leads to their limited representation ability of images. To tackle this problem, we propose a plug-and-play deformable residual block for efficiently sampling the spatially related features at different scales. Moreover, considering that the optical degradation is closely correlated with the field-of-view (FOV), we introduce a FOV attention block based on omni-dimensional dynamic convolution to integrate spatial features. On this basis, we further propose a novel optical degradation correction model called DR-UNet. It is constructed on an encoder-decoder structure to capture multiscale information, along with several context blocks. By correcting the optical degradation in images from coarse to fine, we finally obtain high-quality and degradation-free images. Extensive results demonstrate that our method can compete favorably with some state-of-the-art methods.
Hate Speech and its Impact on Albanian Youth

Background: The main topic of this scientific paper is based on what hate speech is, and how it affects young Albanians. The main problem raised in this paper is how hate speech is not much talked about in our everyday lives, and how young people do not have enough information about different examples of hate speech in their everyday lives and therefore not being aware how this can affect their mental health and emotional well – being. Purpose: The hypothesis that this scientific paper is based on is how today’s society faces a current situation of co-existence, where there is a lack of information on hate speech, and how young people (students) do not know how to identify what can be considered hate speech or not. Method: The theoretical basis of this scientific paper focuses on relevant literatures, studies conducted on this topic from all over the world, as well as previous studies focused on Albania, with relevant references. This paper contains the main study conducted by the author, a survey named “Hate speech and its effect on emotional Wellbeing” with open and closed questions, the data of which have been analyzed and interpreted. Conclusion: The creation of new platforms where young people can be informed about hate speech, report experiences is a necessary need.
A Hybrid User Model for Virtual Stochastic Sensors

Virtual stochastic sensors (VSS) enable the reconstruction of unobserved behavior of discrete or hybrid stochastic systems from observable output.Augmented stochastic Petri nets (ASPN) are user models for VSS and describe discrete stochastic models that produce discrete output symbols based on the transitions or system states.Hybrid ASPN (H-ASPN) can describe hybrid stochastic systems with continuous quantities that are influenced by and interact with the discrete system parts, producing samples of these continuous system quantities as observable output.Real-world systems often contain both of these types of observable output.In household energy models, the total consumption is a continuous quantity that can be sampled regularly.Additionally, the usage behavior of some appliances might be known in advance or can be monitored easily, resulting in observable discrete symbols.Being able to utilize both of these for behavior reconstruction, promises better results than using either one alone.In this paper, we describe an extended H-ASPN paradigm for modeling symbol output as well as sample measurement for partially observable hybrid stochastic systems.We demonstrate the paradigm on a small non-intrusive appliance load monitoring (NIALM) example and test the behavior reconstruction.The extended H-ASPN modeling paradigm enables faster and more reliable behavior reconstruction results, when using both observable symbols and samples.The experimental results indicate that extended H-ASPN could lead the way to practically feasible VSS for hybrid systems.
Adaptive Precision Training (AdaPT): A dynamic quantized training approach for DNNs

Quantization is a technique for reducing deep neural networks (DNNs) training and inference times, which is crucial for training in resource constrained environments or applications where inference is time critical. State-of-the-art (SOTA) quantization approaches focus on post-training quantization, i.e., quantization of pre-trained DNNs for speeding up inference. While work on quantized training exists, most approaches require refinement in full precision (usually single precision) in the final training phase or enforce a global word length across the entire DNN. This leads to suboptimal assignments of bit-widths to layers and, consequently, suboptimal resource usage. In an attempt to overcome such limitations, we introduce AdaPT, a new fixed-point quantized sparsifying training strategy. AdaPT decides about precision switches between training epochs based on information theoretic conditions. The goal is to determine on a per-layer basis the lowest precision that causes no quantization-induced information loss while keeping the precision high enough such that future learning steps do not suffer from vanishing gradients. The benefits of the resulting fully quantized DNN are evaluated based on an analytical performance model which we develop. We illustrate that an average speedup of 1.27 compared to standard training in float32 with an average accuracy increase of 0.98% can be achieved for AlexNet/ResNet on CIFAR10/100 and we further demonstrate these AdaPT trained models achieve an average inference speedup of 2.33 with a model size reduction of 0.52.
Secure and Privacy-Based Data Sharing Approaches in Cloud Computing for Healthcare Applications

The cloud framework is extensively employed in the medical industry for a broad range of applications including medical information storage, distribution, as well as administration. Given the advantages of cloud computing, several medical companies are exploring implementing such techniques to address various difficulties inside the medical sector. It evolved into an essential component of healthcare delivery. It may help medical companies concentrate on their activities, and medical assistance, including clinical management. This provides a safer approach for sharing confidential material with hospitals as well as third-party studies and medical institutes. Nevertheless, because the structure of cloud technology emerges as well as develops fast, especially theoretical and practical implications, significant legal/contractual, economical, customer satisfaction, connectivity, cybersecurity, and confidentiality problems remain in the research phase. In this article, we explain several cloud-based computing services, as well as implementation strategies, also highlight important problems. This study also focuses on safe and secure information exchange options within cloud technology for medical domains.
Cache-Based Side-Channel Attack Mitigation for Many-Core Distributed Systems via Dynamic Task Migration

Side-channel attacks (SCA) are a serious threat to cryptographic systems due to mostly unavoidable information leakage. Cache-based SCAs take advantage of cache inherent timing properties on shared memory systems to extract security-critical information. In this paper, we present a novel approach to mitigate cache-based SCAs on distributed many-core systems, based on a resource management technique. Our solution leverages dynamic task migration as a mechanism to ensure a secure execution scenario for security-critical applications. Additionally, we propose a resource-management-based mechanism to ensure a secure execution when migration is not possible due to a lack of available resources. We evaluate our solution in terms of gained security and performance impact using the Sniper simulator for different configurations. Results show that our technique effectively develops resilience against SCAs, while causing a low performance slowdown (1.6% on average, 9% worst case). For all tested benchmarks, our worst-case performance slowdown is 20% less than a state-of-the-art countermeasure. Moreover, our solution utilizes less than 1 ms of system run-time overhead for a 64 core platform with 100% utilization.