Reviewerly/exHarmony huggingface dataset:
SMS Security by Elliptic Curve and Chaotic Encryption Algorithms

Short message services (SMS) represent one of the components of the global communications network and are one of the important developments in communication technologies and communications technology. SMS messages without a password are stored in the SMS server. For the purpose of review and dispute resolution. The security of SMS content cannot be protected because it is transmitted in plain text and is accessible to network operators and employees. Therefore, the end-to-end key is based on encryption and decryption technology can provide SMS security. The security protocols used for SMS security on contemporary mobile devices were examined in this study. SMS security system encryption time affects how well mobile devices work. This shows that security technologies take longer to generate keys and encrypt keys as the key size increases. Due to the limited processing power of mobile devices, large-scale algorithms such as DES, AES, RC4, and Blowfish are not suitable for SMS encryption. SMS may be encrypted using the elliptic curve technique because it provides great security with a smaller key on devices with limited resources, such as mobile phones. And chaotic theory, encryption is simple, fast and secure data encryption. As a result, a combination of elliptic curve algorithm and chaotic encryption algorithm is proposed to achieve a high level of security. In this paper, several tests have been done to compare the algorithms in terms of throughput, power consumption, SMS size, encoding time, and decoding time. The results indicate that the proposed method is better than the comparison method.
Canadian research data repositories in re3data: A collaborative effort to increase visibility and foster community engagement

Collaboration has always been a central principle for re3data. This is reflected in the fact that research communities, repository providers, and other relevant stakeholders contribute significantly to the completeness and accuracy of the re3data metadata as well as its further technical and conceptual development.
Co-learning Semantic-Aware Unsupervised Segmentation for Pathological Image Registration

The registration of pathological images plays an important role in medical applications. Despite its significance, most researchers in this field primarily focus on the registration of normal tissue into normal tissue. The negative impact of focal tissue, such as the loss of spatial correspondence information and the abnormal distortion of tissue, are rarely considered. In this paper, we propose a novel unsupervised approach for pathological image registration by incorporating segmentation and inpainting. The registration, segmentation, and inpainting modules are trained simultaneously in a co-learning manner so that the segmentation of the focal area and the registration of inpainted pairs can improve collaboratively. Overall, the registration of pathological images is achieved in a completely unsupervised learning framework. Experimental results on multiple datasets, including Magnetic Resonance Imaging (MRI) of T1 sequences, demonstrate the efficacy of our proposed method. Our results show that our method can accurately achieve the registration of pathological images and identify lesions even in challenging imaging modalities. Our unsupervised approach offers a promising solution for the efficient and cost-effective registration of pathological images. Our code is available at https://github.com/brain-intelligence-lab/GIRNet .
Examining What Causes Perceived Dissonance in Musical Intervals and the Effect of Timbre on Dissonance

Dissonance is perceived when certain musical notes are heard simultaneously and create a harsh, clashing feeling. Consonance, the opposite of dissonance, is perceived as a pleasant harmony of musical notes. Timbre is the defining characteristic of sound which differentiates sounds from different sources. This study attempts to determine the cause of perceived dissonance and the effect of timbre on dissonance. We attempt to corroborate Hermann von Helmholtz’s theory of temporal dissonance, which proposes that dissonance results from beat frequencies created by interference of harmonic overtones. Our procedure examines algebraic and graphical representations of sound waves of the nine standard musical intervals from the minor second to the octave, produced on piano, ranging from dissonant to consonant. No clear correlations were found between different quantitative metrics and dissonance. The study also compares graphs of intervals from a piano and a human voice, which have different timbres. We noted that the graphs of piano notes, which have a “harder”-sounding timbre, had higher ratios of concavity changes to beat period than the graphs of sung notes, which have a “softer” timbre. Further research is planned to study whether graphical characteristics can reveal qualities of timbre.
A Survey on Cloud Model

&lt;p&gt;To tackle the uncertainties in life, a model that can efficiently convert qualitative concepts and quantitative values is essential. This model is referred to as a qualitative-quantitative uncertainty model. The conventional membership function provides a fixed membership degree that is incompatible with the fuzziness and randomness of qualitative concepts when a certain element of the theoretical domain is inputted. To address this issue, Academician Li introduced the cloud model, which is a qualitative-quantitative uncertainty model created for converting between qualitative and quantitative values. Unlike the traditional membership function, the cloud model generates a set of random numbers with a stable tendency that better captures the fuzziness and randomness of the qualitative concept when an element of the theoretical domain is inputted. In this paper, the background and fundamental concepts of cloud models are initially presented. Afterwards, we delve into the advancements of cloud models in various fields such as controller, data mining, and reliability. Through these discussions, the paper showcases the significant role that cloud models can play in resolving qualitative and quantitative conversion issues across different domains. The three numerical characteristics of cloud models are then described in detail, as well as cloud generator, virtual cloud and other cloud model related algorithms. Finally, some statistical properties of cloud models are discussed, as well as the current problems and future research directions.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Automatic Essay Grading System Using Deep Neural Network

Essays are important for testing students’ academic scores, creativity, and being able to remember what they studied, but grading them manually is really expensive and time-consuming for a large number of essays. This project aims to implement and train neural networks to assess and grade essays automatically. The human grades given to the essays should be matched with grades generated from our automatic essay grading system consistently with minimum error. Automated essay grading can be used for evaluating essays written according to specific prompts or specific topics. It is the process of automating scoring system of essays without any human intervention and using computer programs. This system is most beneficial for educators since it helps reducing manual work and saves a lot of time. It not only saves a lot of time but also speeds up the process of learning feedback. We used deep neural networks in our system instead of traditional machine learning models.
Spiking information processing in a single fabricated photonic spiking neuron with double integrated electronic dendrites

Dendrites, branches of neurons that transmit signals between synapses and soma, play a vital role in spiking information processing, such as nonlinear integration of excitatory and inhibitory stimuli. However, the investigation of nonlinear integration of dendrites in photonic neurons and the fabrication of photonic neurons including dendritic nonlinear integration in photonic spiking neural networks (SNNs) remain open problems. Here, we fabricate and integrate two dendrites and one soma in a single Fabry–Perot laser with an embedded saturable absorber (FP-SA) neuron to achieve nonlinear integration of excitatory and inhibitory stimuli. Note that the two intrinsic electrodes of the gain section and saturable absorber (SA) section in the FP-SA neuron are defined as two dendrites for two ports of stimuli reception, with one electronic dendrite receiving excitatory stimulus and the other receiving inhibitory stimulus. The stimuli received by two electronic dendrites are integrated nonlinearly in a single FP-SA neuron, which generates spikes for photonic SNNs. The properties of frequency encoding and spatiotemporal encoding are investigated experimentally in a single FP-SA neuron with two electronic dendrites. For SNNs equipped with FP-SA neurons, the range of weights between presynaptic neurons and postsynaptic neurons is varied from negative to positive values by biasing the gain and SA sections of FP-SA neurons. Compared with SNN with all-positive weights realized by only biasing the gain section of photonic neurons, the recognition accuracy of Iris flower data is improved numerically in SNN consisting of FP-SA neurons. The results show great potential for multi-functional integrated photonic SNN chips.
FAIR Data Digest #2

How to deal with author pseudonyms in data integration? How does fundamental research benefits Knowledge Graphs and why do Wikidata identifiers actually start with a Q? Here you get the answers!
FAIR Data Digest #1

Conference spring 2023, GPT vs Knowledge Graphs (KGs), and money in KG reasoning
FAIR Data Digest #3

On the benefits of ISNI identifiers, what ELSA is and how FAIR data can support legislation
The Use of Natural Light for Educational Purposes in the Formation of Natural Scientific Literacy of Students in Primary School

The study presents lessons learned in regular Science and Physics and Astronomy classes. The latter are related to an area of content – natural sunlight. An interdisciplinarity training is applied through STEAM–based learning approaches and methods: exploratory approach, experiential learning, learning by doing, small group work, etc. The opinion of the students with respect to the training was examined and presented in the study. Application of methods such as "Learning by doing", "Learning by experience" in science education is presented for: formation of natural scientific literacy, development of creativity related to making a solar device, applicable in direct teaching process. Attention is paid to the application of the above-mentioned approaches and methods as the main motivating tool responsible for building teamwork attitudes, support as well as cooperation among the students in primary school. The main purpose of the presented innovative learning approach is to make the teaching of natural sciences more fascinating for students which leads to better assimilation and durability of the new knowledge.
A Comprehensive Analysis of The Varied Interpretations among Islamic Scholars Concerning Bitcoin Transactions

At present, there is a notable divergence in viewpoints among Islamic scholars pertaining to the legality of Bitcoin transactions.A subset of these scholars categorically prohibits its use, while another endorses it.This disparity has precipitated confusion within the Muslim community, thereby complicating the task of adopting one stance over the other.The primary aim of this study is to meticulously examine and analyze the arguments presented by both subsets, with the objective of shedding light on the underlying causes of this divergence of viewpoints.To achieve this, the study utilizes a blend of descriptive and inductive analytical methodologies.The findings indicate that the scholars who prohibit Bitcoin largely base their
Lego ditches plan to use recycled PET

Lego says that after 2 years of testing, it has dropped any plans to use recycled polyethylene terephthalate (PET) as the main material for its famous Lego toy bricks. The company currently makes its bricks from the fossil fuel–derived polymer acrylonitrile-butadiene-styrene but since 2015 has been seeking a more sustainable material. Recycled PET “wouldn’t have helped us reduce carbon emissions,” the firm says in a press release. The firm has so far tested more than 300 different materials.
FAIR Data Digest #16

how important is the social component for Research Data Infrastructures and how can you explore existing Wikibase cloud instances?
FAIR Data Digest #15

why you should care about a single trustworthy data source for Belgian heritage metadata?
FAIR Data Digest #14

The European Open Science Cloud, as FAIR as it can be and a Greek tragedy
FAIR Data Digest #13

supporting many use cases with FAIR publication metadata of more than 249 million publications
FAIR Data Digest #12

FAIR proceedings: semantification of the open access publishing service CEUR-WS
FAIR Data Digest #10

On persistent identifiers for research and DCAT data catalogs
FAIR Data Digest #9

Podcast Pick: Jimmy Wales about Wikipedia | Data Integration with TheFuzz (Python) | Introducing ESFRI
FAIR Data Digest #7

What is CLARIN, the Common Language Resources and Technology Infrastructure?
FAIR Data Digest #5

What are Europeana and the Cultural Heritage Cloud? And Hello MetaBelgica!
FAIR Data Digest #4

This week: trying to fix Wikidata issues with its ontology and using named graphs as an advanced data management technique to manage context in RDF
Uncontrollable GOLLEM-AI and the total decoding and synthesizing of reality

You have read that I am criticizing the discourse on AI here in the last weeks and I have made a call for regulation of AI in education.
Analysing weather conditions using Firebase

Forecasting Weather to a specific time and location via the use of science and technology for its specific location from the 19th century, millions of humans have started predicting weather forecasting. This technology uses meteorology so that it predicts the weather for the future so that the response of the atmosphere can be recorded for the future purpose. Technology for predicting weather we use barometric pressure, in that which show the current weather condition and cloud cover for Sky condition this prediction model is based on the Computer Based model show the records of a wide range of atmospheric will be recorded by having this technology for model prediction is pattern recognition, telecommunication model, performance knowledge, but its skill required the more in this technology for forecasting the chaotic character of the weather which is overpowered by the measurement error by this less accuracy the atmospheric dynamic model field to predicting the model which is already exist for forecasting. As a result, as the time gap between today and the predicted model for the forecast has a time gap so it leads to less accuracy, the range of forecasting prediction for selecting the most likely and accurate outcome in the model contest aid narrows free error in it. The most difficult challenge in the world is to predict the forecasting using the Meteorology virtual application for the most significant difficult Technology. In this paper, we are going to investigate data mining techniques and artificial neural networks, my research paper presents the service-oriented model that the architecture for the weather information system will be linked with the GPS system. Algorithms such as Artificial neural networks and Decision tree algorithms are going to use for prediction model for forecasting by gathering information on meteorological data collection of specific time can use for achieving accuracy therefore, this paper includes of classification of different rules for predicting the model, which generated result by the technique of data mining Technology for weather forecasting prediction.
Improving Reliability of Magnetic Localization Using Input Space Transformation

Body motion tracking for medical applications has the potential to improve quality of life for people with physical or speech motor disorders. Current solutions available in the market are either inaccurate, not affordable, or are impractical for a medical setting or at home. Magnetic localization can address these issues thanks to its high accuracy, simplicity of use, wearability, and use of inexpensive sensors such as magnetometers. However, sources of unreliability affect magnetometers to such an extent that the localization model trained in a controlled environment might exhibit poor tracking accuracy when deployed to end users. Traditional magnetic calibration methods, such as ellipsoid fit (EF), do not sufficiently attenuate the effect of these sources of unreliability to reach a positional accuracy that is both consistent and satisfactory for our target applications. To improve reliability, we developed a calibration method called post-deployment input space transformation (PDIST) that reduces the distribution shift in the magnetic measurements between model training and deployment. In this article, we focused on change in magnetization or magnetometer as sources of unreliability. Our results show that PDIST performs better than EF in decreasing positional errors by a factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula> 3 when magnetization is distorted, and up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula> 7 when our localization model is tested on a different magnetometer than the one it was trained with. Furthermore, PDIST is shown to perform reliably by providing consistent results across all our data collection that tested various combinations of the sources of unreliability.
Hierarchical relation and entity rotation-based multi-feature knowledge graph embedding

Knowledge graphs are knowledge bases that represent entities and relations in the objective world through graph structure, and they have a boosting effect on many artificial intelligence tasks. To facilitate the development of downstream artificial intelligence tasks, knowledge graph embedding (KGE) is proposed. It aims to express semantic information for each entity and relation in the knowledge graph within a low-dimensional space. However, when it comes to semantic hierarchy, multiple relation patterns and multi-fold relational structures in knowledge graphs, most of the existing models tend to focus on only one or two aspects, often neglecting the importance of considering all three simultaneously. Therefore, we propose a new knowledge graph embedding model, Hierarchical relation and Entity Rotation-based Multi-Feature knowledge graph Embedding (HERotMFE). Concerning hierarchical relation rotation and entity rotation, it can represent semantic hierarchy, multiple relation patterns and multi-fold relations simultaneously. Self-attention mechanism is used to learn the weights of the two-part rotation to further enhance the model’s performance. According to the findings of the experiments, HERotMFE outperforms existing models on most metrics and achieves state-of-the-art results.
Enhancing Permit Status Checking for the Integrated Public Service Information and Reporting System Application

The district government of Sanggau already has a public service mall (MPP), which is used by all agencies as a place of service. SIPADU (Integrated Public Service Information and Reporting System) is the name of this application, which has various features to meet the needs of MPPs (Public Service Mall). Currently, users of the SIPADU application find it difficult to check the status of permissions. Users must first complete the authentication procedure in order to obtain the desired permission status. This research was conducted to implement a solution to make it easier for users to check the status of the submitted licensing by building a web-based system that will search for the data without requiring login authentication. The analytical methods used include Literature Study and Interview. The design method used is the waterfall method with 6 stages of the process, namely Planning, Analysis, Design, Implementation, Testing, and Verification. The result of this system is a website that can check the status of the permit being submitted. The results of the User Interface evaluation are following the provisions of the Eight Golden Rules method and the results of the evaluation of system testing using the BlackBox testing method with the results showing 100% no errors occur so that it is very suitable for use by users. This study applies the Web Scraping method using PHP scripts and using the Apache Web Server to obtain all licensing data using authentication data provided by PT Media Visual Group so that all users of the SIPADU application can track licensing.
Web-Based Document Workflow Management Information System

In business process and document approval characteristics, approval workflow is a process for automatically flowing business between approval providers with predetermined rules. When viewed from the process of requesting goods still uses e-mail. With the increasing number of documents that must be approved and still using e-mail communication media, obstacles were found that caused delays in the document approval process. In this study, the authors aim to create a computerized workflow document, which will be implemented in the procedure for purchasing requests for goods and requests for promotional items from branches and assisting document recipients in sorting out documents that have not/have been approved which are expected to provide benefits to users in carrying out the process. Document approval. The information system the author has developed can only be run for internal users who already have login access to the system and can only be accessed using the internal network. The development process uses the waterfall method, and data collection methods in this study include observation, interviews, and literature study. System testing is carried out using blackbox testing evaluation and interface design evaluation using the eight golden rules. From the implementation and evaluation results, it was concluded that the system had achieved the desired results, namely, the branch admin page for sending information on approval documents and the approval party page for responding to documents.
Noise quantization simulation analysis of optical convolutional networks

Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.