Reviewerly/exHarmony huggingface dataset:
The Study of the Farmer Financial Institutions Survey AI Model in Papua New Guinea：Numerical Analysis by Cloud Computing

The questionnaire survey method is the research method and the structural equation model (SEM) as a statistical analysis.The expected result will be the establishment of the import model for the first farmer financial institution in PNG and the successful strategy for providing the first farmer financial institution in PNG and the concept of research results can be applied to third-world countries.
Ontology-based Knowledge Modeling for Emergency Response to Shipboard Hazardous Cargo Accidents

The ontology-based knowledge modeling is introduced into the knowledge management and service of shipboard hazardous cargo accident on the water, the ontology model layer and data layer of shipboard hazardous cargo accident emergency relief are established, the structured multi-source knowledge of the non-structured expression of shipboard hazardous cargo accident emergency is realized, the ontology mapping algorithm of shipboard hazardous cargo accident emergency is designed, and the multisource heterogeneous knowledge fusion of shipboard hazardous cargo accident emergency is realized to further integrate and transform the existing accident information advantages into decision-making advantages, effectively enhance the emergency response capability and improve the emergency management work.
Research on Virtual Power Plant Trading Matching Mechanism Based on Blockchain Technology

As an energy management and control technology, virtual power plant technology is the key technology to achieve the dual-carbon goal.In the future new power system, the multi-agent trusted transaction in virtual power plant will be more complex and challenging.The features of blockchain technology, such as information security, distributed decision-making, smart contract and tamper-proof, provide a new idea for multi-subject trusted transactions of virtual power plants.This paper focuses on the benefits maximization formed by multiple aggregators of virtual power plants and the trustworthy transaction matching efficiency.First of all, based on blockchain technology and multi-agent system integration to establish a master multi-slave hierarchical interactive control architecture; Then, based on the non-cooperative static game model, we design a P2P trusted trade matching mechanism under the demand of peer-to-peer trade.Finally, the rationality and feasibility of the strategy proposed in this paper are verified by the example analysis, which effectively enhances the matching efficiency of multi-agent transactions, improves the VPP optimization allocation ability and the overall economy.
Performance of Product Review Prediction based on CNN and SVM Techniques

Sentiment analysis and opinion mining are two of the most well-known fields that use text data from places like Facebook, Twitter, Amazon, and other places to evaluate and learn about people.It is important because it lets businesses actively work on improving their business plans and fully understand what customers say about their goods.It is also important because it is important.In this process, you need to do both computational analysis of a person's buying habits and opinion mining about a company's business structure.This thing could be a person, a place, a blog post, or the experience of using a tool.Reviews, ratings, and comments left by users can be examined to provide more insightful data for business usage.Understanding the consumer's needs and foretelling their future intents towards the service are made possible through the analysis of such consumer behaviour.This cognitive research on e-commerce Businesses can monitor how their items are used and how customers feel about them, then use the information to develop a personalised shopping experience for their customers and boost organisational profit.The dataset for this study's product reviews was obtained from a dataset source.The pre-processing processes must then be put into practise.Next, NLP approaches are used to construct the system.The machine learning SVM and CNN-based customer review prediction system offers a strong method for analysing and forecasting the sentiment of customer reviews.This system uses SVM and CNN algorithms for classification, data pre-processing, data splitting, and result creation.In the pre-processing stage, the text input is cleaned up and transformed using NLP techniques, while missing values are handled and labels are encoded.To train the models and assess their effectiveness, the data is then divided into training and testing sets.The classification process uses the SVM and CNN algorithms, with CNN utilising convolutional layers for feature extraction and SVM using hyperplanes for data separation.The trained models are assessed by the system using metrics like recall, precision, and accuracy to produce results.
VR-driven museum opportunities: digitized archives in the age of the metaverse

VR technology has brought new prospects and energy to art museums, prompting the advancement of new technologies that can tackle future challenges while catering to the demands of the metaverse era. Today’s digital archiving and guided tours, which rely on VR and more portable information retrieval, provide optimism for museum development. VR technology overcomes the limitations of traditional museums constrained by space and time as digital archiving gradually becomes a reality. This study highlights the benefits of metaverse-driven online archiving from the users’ perspective, followed by a review of recent examples of using VR for media art archiving. We emphasize the importance of further research into unexplored emerging technologies in media art archiving and propose that VR technologies in the metaverse are essential enablers for online archiving purposes. Our work aims to help art researchers, archivists, professionals, and artists to comprehend the challenges and future needs of archiving media art in the developing metaverse. Furthermore, we discuss present cases of employing VR for media art preservation after outlining the advantages of metaverse-driven online archiving from the users’ viewpoint. We stress the need for additional studies on new technologies in media art preservation. Moreover, metaverse VR technologies play a crucial role in achieving online archiving goals. This research contributes to a better understanding of the challenges and future requirements of archiving media art in the evolving metaverse for art researchers, archivists, and artists.
The Role of the School Library in Improving Students' Literacy

This study discusses the literacy activities of students at school, which aims to determine the literacy activities carried out and the library's role in increasing the literacy of SDI Al-Azhar 4 students. In addition, to find out the obstacles and solutions they face. The research method used for this study is a qualitative approach and this type of case study research. Data collection techniques in this study were interviews, observation, and literature review. The results of this study include 1). Although SDI Al-Azhar 4 has not implemented the School Literacy Movement (GLS) in the school curriculum, it has indirectly implemented GLS in their schools through reading activities in class, storytelling, book reviews, and library classes. 2) The library has a role as a facilitator in increasing the literacy of SDI Al-Azhar 4 students by facilitating reading materials, library class hours, and library activities. In addition, the library (librarian) also plays the role of educator in literacy activities through storytelling, watching films, and reading in classes organized by the library. 3). Obstacles libraries face in providing literacy activities, namely reading materials that are easily damaged because they are often read by students, which are then resolved with a solution given a budget for repairs and procurement of new books. Besides that, another obstacle faced is the Covid-19 pandemic which has changed the learning system by not being face-to-face. This problem is given a solution by carrying out literacy activities online.
An Approach to the Classification of Finite Semifields by Quantum Computing

Finite semifields Rúa, I.are finite nonassociative rings with an identity element, such that the set of nonzero elements is a loop under the product. Their number of elements is a prime power, known as order. They were considered first by Dickson [2], and studied by Albert [1] and Knuth [3]. Finite semifields of order 16 have been classified by Kleinfeld in [7], of order 32 by Knuth in [3], and by Walker in [8]. The classification of finite semifields can be rephrased as a problem of finding certain sets of matrices, which can be solved by computer search. So, classification of semifields with high order such as 64, was achieved by Rúa, Combarro, Ranilla in [4], or of order 243 by Rúa, Combarro, Ranilla in [5], and of order 81 by Dempwolff [9]. Based on this approach, classification of finite semifields of any order via a quantum procedure is possible. We present quantum techniques for the classification of semifields with 8 and 16 elements with their respective simulations, based on Grover’s quantum search algorithm.
Influence of typographic properties on user experience in digital interfaces

AbstractThe user experience with products, services, and artifacts responds to different user needs and is affected by the design of the interface that mediates this interaction. In the context of digital interfaces, typography plays a significant role in the functionality and personality of the system, as actions and contents are conveyed to users through signs and symbols that inform and provide guidance and feedback. Given these relationships, this paper aimed to identify how different typographic properties are related to the satisfaction of user needs on user experience in digital interfaces. For that, a narrative literature review was conducted, followed by a systematization of the identified connections. As a result, it is proposed a diagram that presents the connections between five user needs – safety, functionality, usability, pleasurable experience, and individuation – and five properties of typography – accessibility, legibility, readability, personality, and customization – detailed in the context of the user experience in digital interfaces.
Photonic Neural Cellular Automata for Self-Organized Image Classification

Neural networks based on Cellular Automata (CA) have recently yielded more robust, reliable, and parameter-efficient machine learning models. We experimentally demonstrate the first photonic implementation of CA which successfully performs image classification on the Fashion-MNIST dataset.
A Lightweight Monocular 3D Face Reconstruction Method Based on Improved 3D Morphing Models

In the past few years, 3D Morphing Model (3DMM)-based methods have achieved remarkable results in single-image 3D face reconstruction. However, high-fidelity 3D face texture generation has been successfully achieved with this method, which mostly uses the power of deep convolutional neural networks during the parameter fitting process, which leads to an increase in the number of network layers and computational burden of the network model and reduces the computational speed. Currently, existing methods increase computational speed by using lightweight networks for parameter fitting, but at the expense of reconstruction accuracy. In order to solve the above problems, we improved the 3D deformation model and proposed an efficient and lightweight network model: Mobile-FaceRNet. First, we combine depthwise separable convolution and multi-scale representation methods to fit the parameters of a 3D deformable model (3DMM); then, we introduce a residual attention module during network training to enhance the network's attention to important features, guaranteeing high-fidelity facial texture reconstruction quality; and, finally, a new perceptual loss function is designed to better address smoothness and image similarity for the smoothing constraints. Experimental results show that the method proposed in this paper can not only achieve high-precision reconstruction under the premise of lightweight, but it is also more robust to influences such as attitude and occlusion.
Massively-Parallel Broadband Diffractive Processor for All-Optical Computation of a Large Set of Linear Transformations

We design a massively-parallel broadband diffractive processor for all-optical computation of a large number ( &gt; 180) of arbitrarily-selected, complex-valued linear transformations by encoding the input/output of each transformation using a unique wavelength.
Enhanced security using video summarization for surveillance system using deep LSTM model with K-means clustering technique

For the security of the Society, Video surveillance systems have become an important tool that can help in preventing crime in public or private establishments, leading to more safe and secure environment. With the rise of technology that can handle high volune video data and the need for continuous monitoring for security to citizens, the analysis of the video footages, during security breach attempts has become more challenging. Video summarization can play a crucial role in secure surveillance systems by optimizing the analysis time of the security breach. This research attempts to summarise the important aspects of the original video without losing its context and also optimizes the storage requirement. To achieve the same a unique KM-LSTM based technique has been developed that utilizes deep learning methods to understand links among highlight as well as non-highlight video segments through the use of pairwise distance calculations. A new Deep LSTM model has been used to extract the features from all segments of frames. These extracted features are clustered using k-means clustering. Then, such features are trained using a pairwise deep learning model to get highlight scores as well as leverage the comparative similarity among pairs of the highlight as well as non-highlight segments. At last, video summarization takes place to generate the video summary from the highlight segments only. From the results of these experiments discovering the moments of the user’s major or special interest (namely, highlights) in a video, to generating summarization of videos. The above technique can help in increasing the analysis of Video content and thus achieving a more vigilant and secure society.
A Disconnect Between FAIR Infrastructure Devs and Product Devs

Rory Macneil nails it:&lt;sup&gt;1&lt;/sup&gt; This seems to me to be a really important problem. In my experience a lot of the discussion about things like PIDs and controlled vocabularies seems to assume that these things just exist. But when you actually go to try to use them and make them usable in the context of tools that people use in research, that presents a whole additional series of challenges;
Generation of frequency-bin-encoded ququarts in a programmable silicon photonic chip

We present a programmable silicon photonic device for the generation of frequency bin entangled qudits, in which bin spacing, qudit dimension, and bipartite quantum state can be directly reconfigured on a chip.
Ultra-Compact Optical Convolutional Accelerators Based on Polarization-Independent Metasurfaces

We experimentally demonstrate an optical metasurface-based convolutional accelerator with ultracompact volume, low crosstalk, and polarization insensitivity. The optical accelerator computes convolutions in the optical domain for energy-efficient and ultrafast imaging processing.
Findability → Known-Item Search, Discoverability → Exploratory Search?

I keep confusing findability and discoverability. It seems that findability is often equated to known-item search, and discoverability to exploratory search. Known-item search is compatible with “instant search”, aka search-as-you-type interfaces. Exploratory search is compatible with “autocomplete” (incl. re-spelling, infix matching, synonym substitution, etc.) interfaces. Recommendation can be a part of exploratory search, i.e. bundled in response to a user’s...
All-Optical Computing of a Group of Linear Transformations Using a Polarization Multiplexed Diffractive Neural Network

We report the all-optical implementation of a group of arbitrary complex-valued linear transformations using a polarization multiplexed diffractive optical network that consists of spatially-engineered isotropic diffractive surfaces and pre-determined linear polarizer arrays.
Towards logical foundations for probabilistic computation

The overall purpose of the present work is to lay the foundations for a new approach to bridge logic and probabilistic computation. To this aim we introduce extensions of classical and intuitionistic propositional logic with counting quantifiers, that is, quantifiers that measure to which extent a formula is true. The resulting systems, called cCPL and iCPL, respectively, admit a natural semantics, based on the Borel σ-algebra of the Cantor space, together with a sound and complete proof system. Our main results consist in relating cCPL and iCPL with some central concepts in the study of probabilistic computation. On the one hand, the validity of cCPL-formulae in prenex form characterizes the corresponding level of Wagner's hierarchy of counting complexity classes, closely related to probabilistic complexity. On the other hand, proofs in iCPL correspond, in the sense of Curry and Howard, to typing derivations for a randomized extension of the λ-calculus, so that counting quantifiers reveal the probability of termination of the underlying probabilistic programs.
High-performance chip-to-chip QKD with hybrid integrated photonics

Here we demonstrate a hybrid integrated quantum key distribution chip which combines through edge-coupling an ultra-low-loss SiN interferometer with a high-speed InP phase modulator. This novel devices produces record chip-to-chip secure key rates.
Translating Identifiers

Good identifiers are opaque, so translation is by association – owl:sameAs, skos:exactMatch, or some other relationship. Translation doesn’t follow from reading a sign, but from retrieving a sense. If metadata is relationships between identifiers,1 then metadata is the medium of conceptual convergence. M. Bide, “Standard Identifiers: an overview of the current landscape,” presented at the USPTO Open Meeting: Facilitating the Development of the Online Licensing...
Efficient Deep Neural Network Training Techniques for Overfitting Avoidance

A deep learning neural network’s ultimate goal is to produce a model that does well on both the training data and the incoming data that it will use to make predictions. Overfitting is the term used to describe how successfully the prediction model created by the machine learning algorithm adapts to the training data. When a network is improperly adapted to a limited set of input data, overfitting occurs. In this scenario, the predictive model will be able to offer very strong predictions on the data in the training set, and will also capture the generalizable correlations and the noise produced by the data, but it will predict poorly on the data that it has not yet seen during his learning phase. Two methods to lessen or prevent overfitting are suggested in this publication among many others. Additionally, by examining dynamics during training, we propose a consensus classification approach that prevents overfitting, and we assess how well these two types of algorithms function in convolutional neural networks. Firstly, Early stopping makes it possible to store a model’s hyper-parameters when it’s appropriate. Additionally, the dropout makes learning the model more challenging. The fundamental concept behind dropout neural networks is to remove nodes to allow the network to focus on other features which reducing the model’s loss rate allows for gains of up to more than 50%. This study looked into the connection between node dropout regularization and the quality of randomness in terms of preventing neural networks from becoming overfit.
Adaptive Data Structures for 2D Dominance Colored Range Counting

We propose an optimal adaptive data structure for 2D dominance colored range counting in the word RAM model; namely, for n colored points in two-dimensional rank space, we present a linear space data structure that supports each query in $$O(1+\log _{w} k)$$ time, where w denotes the number of bits in a word and k denotes the number of distinct colors in the query range. To this end, we design adaptive data structures for 2D 3-sided stabbing counting. Previously, in the orthogonal colored counting problems, the adaptive data structure is only known for 1D [TODS’2014].
Integrated Tunable Bell State Generator and Hong-Ou-Mandel Experiment on AlGaAsOI

We report an AlGaAsOI quantum photonic circuit with multiple pair sources and tunable interferometers. We demonstrate Hong-Ou-Mandel interference between two microring resonators and a tunable Bell state generator producing Ψ + and Φ − bi-photon entangled states.
MONITORING OF COGNITIVE DETERMINANTS OF PROFESSIONAL AND PERSONAL SELF-DETERMINATION OF PARTICIPANTS IN THE EDUCATIONAL PROCESS

The main cognitive determinants of modern youth are considered, which are leading, guiding against self-determination at the professional and personal level.The main conditions in the context of the socio-cultural functioning of the participants in the educational process are highlighted, which are defining against trends in levels of a particular cognitive determinant.Recommendations for improving the pedagogical conditions for the prevalence of such components of selfdetermination as family values and a multifaceted personality are provided, which allow improving the activity of participants in the educational process, increasing the effectiveness and efficiency of professional and personal self-determination.Based on the results of the monitoring carried out in the context of studying the cognitive determinants of professional and personal self-determination of the participants in the educational process (students), a number of recommendations can be proposed.Firstly, the educational process of higher education should be built in Журнал «Наукові інновації та передові технології» № 9(23) 2023 (Серія «Психологія») 18 such a way that the intellectual and professional potential of the nation remains in Ukraine and increases the economic and social standards of life.It is necessary to create such conditions of study and employment, in which the student will have a personal interest in the professional knowledge of the industry that they have chosen.This is possible by increasing practical training, during which students will have the opportunity to practice their skills, learn to independently solve professional problems and take responsibility for making decisions.One of the ways to gain practical knowledge can be meetings and direct contact with representatives of different professions who have already achieved success in their business.Using the example of their own professional activities, specialists share not only professional knowledge, but also experience of building a career, creating their own business, the ability to act in new or non-standard conditions, providing specific examples, revealing personal secrets and secrets of the success of colleagues.