Reviewerly/exHarmony huggingface dataset:
Ractal structures in architectural design

The high rate of urbanization in the modern world requires a conscious approach to architectural design. A person needs an environment that fills him with new strength and does not drown out his creative potential with monotonous routine. Due to its natural feature, fractal architecture meets these requirements. The article examines the signs of fractal systems in relation to architecture, highlights the concept - "the principle of fragmentation of a fractal", the term "self-similarity" is broader. In addition, the existing experience of fractal architecture has been analyzed, and the relationship between fractality and the success of the project has been revealed using the example of existing objects. The materials of this article can serve for the further development of the theory of creating fractal architecture.
Research on Prediction of Sentiment Trend of Food Safety Public Opinion

Emotion has a nearly decisive role in behavior, which will directly affect netizens' views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance.In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion.Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction.The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out.Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends.The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend.
The role of power and trust for successful information sharing in the supply chain

&lt;span lang="EN-US"&gt;The supply chain is a series of operations consisting of interconnected units both within and outside the company such as suppliers, producers, and distributors, each of which holds information at a different level. These parties need to be coordinated using information sharing as a coordination mechanism. Information sharing enables them to make the right and quick decisions that can improve the performance of the supply chain. Information sharing has received a lot of attention from researchers who analyze it from different perspectives. However, no research shows the relationship between factors that drive information sharing to succeed. This study produces a model of power, trust, and information sharing.&lt;/span&gt;
A Pragmatic Study on Request Behavior in Instant Message: On Characteristics and Validity

Instant messaging is a powerful communication tool people use to make a request. This paper analyzes the speech act of making and answering requests in instant messages from a pragmatic approach. The statistics result from 217 original instant messages collected, among which 137 are concerned with the process of “asking” and 80 with “answering”. These messages are analyzed in terms of “directness” and “indirectness”. The result reveals that the degree of directness is differed by illocution, and it is the degree of intimacy that influences the choice of direct and indirect in making a request, while the degree of politeness is mainly influenced by the nature of an event itself. It is hoped that the result of this study will help people better communicate with instant messages domestically and cross-culturally.
The Role of Soft Skills for Students Accounting Industry in the Concept of Digital Transformation by Output Standard Accessories

In the current renovation process, the education sector in general and universities in particular are innovating in terms of educational contents, programs, and methods in the direction of capacity development for students. Improve the quality of human resource training to meet social needs. The development of soft skills for students in higher education institutions is one of the objective requirements and needs attention. This article points out the role of soft skills for accounting students in the context of digital transformation. At the same time, the article also proposes some basic solutions to develop soft skills in the context of digital transformation towards output standards to meet the needs of accounting students as well as employers labor use.
Research on the Dynamic Mechanism of the Integration of Industry and Education in Higher Vocational Education from the Perspective of Digital Intelligence Transformation

Based on the symbiosis theory, this study combs out the influencing factors of the driving force of industry-education integration in higher vocational education in the new era, uses SEM to conduct quantitative path analysis, and identifies the key points for mechanism optimization, thereby promoting the in-depth development of industryeducation integration in higher vocational education in the context of digital intelligence, providing a useful reference for the digital intelligent transformation of vocational education and industry.
Evaluating narrative visualization: a survey of practitioners

Narrative visualization is characterized by the integration of data visualization and storytelling techniques. These characteristics provide challenges in its evaluation. Little is known about how these evaluation challenges are addressed by narrative visualization practitioners. We surveyed experienced narrative visualization practitioners to investigate their methods of evaluation. To gain deeper insight we conducted a series of semi-structured interviews with practitioners. We found that there is usually an informal approach to narrative visualization evaluation, where practitioners rely on prior experience and their peers for evaluation. Our study also revealed novel approaches to evaluation. We introduce a practice-led heuristic framework to aid practitioners to evaluate narrative visualization systematically. Our practice-led heuristic framework couples first-hand practitioner experience with recent research literature. This work sheds light on how to address narrative visualization evaluation to better inform both academic research and practice.
The Overview of Algorithms Implementation in File Search Applications or Digital Archives

As time goes by, the number of documents or files or archives in a company continues to increase. These files are in the form of data collection, activity reports, accountability reports, work proposals, letters, decrees, regulations, and so on. At first, the company tried to convert paper-based files into digital files, then took advantage of several free platforms to store them. Furthermore, companies find it difficult to control digital files because of the non-centralized storage. Finally, companies are trying to create various information systems that allow them to centrally manage digital files. A large number of digital files in it makes the search process even longer, so it is necessary to optimize the search features. The purpose of this study is to review several algorithms that have been implemented to optimize file searches in previous studies. This research takes Indonesian scientific articles from Google Scholar from 2015 to 2022. The result of this research is the explanation of the use of algorithms in previous studies so that this research can become a reference and suggestion for further research. This study found the fact that every information system or application that functions to manage company files or archives requires a search algorithm to improve system performance
SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation

This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net based discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel as well as global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.
A Convolutional Neural Network Accelerator Based on FPGA

This paper analyzes and studies the hardware programmable logic resources on small-scale FPGA chips, providing reasonable hardware resource support for subsequent neural network accelerator designs. A flexible 32-bit instruction set is designed for control by the Processing System (PS) on the Programmable Logic (PL) side, making motion state detection flexible and controllable. When designing the hardware side, this paper uses a resource-sharing strategy, and most of the calculation modules are designed using on-chip DSP resources to reduce the resource consumption of the calculation module. An innovative strategy of partially not caching the data between layers of the neural network is applied to reduce the demand for on-chip cache. To optimize on-chip storage space, this article partitions the limited BRAM space on the chip in a reasonable manner and improves the efficiency of on-chip data reading and writing through parallel processing, thereby improving the real-time performance of the neural network.
Are chatbots really able to think like people?

The lofty goal of artificial general intelligence is within reach, according to researchers at Microsoft, but others are sceptical, finds Matthew Sparkes
Speckle Pattern Improvements for Digital Image Correlation to Capture Small Strains in Tensile Tests on Rock

Digital image correlation (DIC) is a popular optical strain measurement technology for deformation analysis in rock mechanics laboratory testing. Although reliable and repeatable, traditional strain gauge and extensometer measurement techniques have drawbacks including high recurring cost and limited spatial distribution of data. DIC utilizes continuous photography and computer software to track paint speckle pixel displacements of a material deforming under load, producing a full-field strain map for each recorded increment. Although DIC presents an opportunity to overcome the limitations of traditional methods, significant precision and care is required to track typical total displacements of 0.005 mm (0.01% strain) in Brazilian tensile stress (BTS) tests of igneous and sedimentary specimens measuring 47.6 mm diameter. For computer algorithms to precisely track individual pixels between frames, it is often necessary to apply a unique speckle pattern conforming to several criteria to the rock specimen. A variety of speckle application techniques have been reported in rock mechanics literature; however, the importance of a high-quality speckle pattern and resulting influence on the accuracy of DIC results is frequently overlooked. The laboratory testing program in this research utilized 2-Dimensional DIC on BTS tests to investigate the cost, efficiency, ease of application, and effectiveness for several speckle application methods. In addition to testing traditional spraypaint, airbrush, and stamp application methods, an innovative laser engravement speckle application technique was developed in this research that provides more reliable DIC results. This research provides practical speckle pattern application recommendations for small strain 2D DIC measurements.
Supplementary Figure S3 from Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors

&lt;p&gt;Supplementary Figure S3 shows ranking of variable importance plot for random forests and gradient boosting machines for surveillance benefit.&lt;/p&gt;
Performance Comparison between NoSQL (RethinkDB) and MySQL Database Replication from Master to Slave in Big Data

When database is stored in one computer or server, certain issues related to disaster (in terms of natural or un-natural) problem may arise with reference to geographical connection and geographical distribution of servers. Here, replication feature plays an important role. There were many techniques and methodologies for geographically distributed servers, but the problem was of one master. When an authorized user updates his database on a client database server then this client database server updates his master database server and at last, the master database server updates all its client’s database servers. The described process is called database replication. In this process, there are several main parameters that are important, i.e., bandwidth, memory, and processor time. In this paper, the analysis is focused on comparing which database (NoSQL family-RethinkDB and MySQL) utilize how much memory, processor, and time during replication of database from client to master.
Remote sensing image registration of disaster-affected areas based on deep learning feature matching

With the rapid development of remote sensing technology, remote sensing registration plays an important role in the assessment of various natural disasters, especially earthquakes. However, multi-temporal remote sensing images for the assessment have some characteristics, e.g. large-scale and rotation, resulting in challenges of remote sensing registration. In order to better register remote sensing images, we propose a new image registration method with a deep learning feature matching strategy. We first extract the pre-match point sets M and S by using SIFT-FLANN (SIFT-Fast Library for Approximate Nearest Neighbors). Second, we filter out the correct matching point pairs from M and S by using a multiscale neighborhood information network and a dual-path ConvNeXt network with self-attention-guided local information enhancement. Thirdly, we register multi-temporal remote sensing images by solve the model parameters of the spatial transformation. Finally, we evaluate our proposed method using a variety of remote sensing images with different phases, including visible light images with different illumination, scale and geometry changes. On the remote sensing image dataset containing images of pre- and post-earthquake, we compare our method to existing state-of-the-art methods and provide the results with the evaluation indexes such as Root Mean Square Error (RMSE). The results show that our method for multi-temporal remote sensing registration has a higher registration accuracy and more robustness.
Thermal infrared face image data enhancement method based on deep learning

The high accuracy of deep learning in various computer vision tasks often relies on massive image data, but there is usually not enough image data to complete the task in actual project applications. Aiming at the problem that thermal infrared face images are few and difficult to obtain in the absence of light, this paper proposes a method to generate thermal infrared face images based on color face images to obtain more thermal infrared face image data. This method takes the conditional generative adversarial (CGAN) network as the framework and uses the deep high-resolution network and the Patch Net network as the generator and discriminator to build the network model. By verifying the network model on the two datasets, it can be proved that the proposed method is clearer, and the details of the facial features are more obvious than the thermal infrared face images generated by the traditional image generation methods. Compared with the FID values of the original generation method, the FID values of the generated image using this method were reduced by 136 and 92. Therefore. The unsupervised data augmentation method based on HRNet network as a generator can also be applied to other visual tasks such as object recognition and detection that lack image data.
Embryonics-based reconfigurable digital system for aerospace applications

Aerospace technology has entered the era of high modularization and integration. In order to ensure the tactical performance of the aerospace system and improve the availability and reliability, a FPGA+DSP based heterogeneous multi-core digital system hardware design scheme is proposed in this paper. The scheme has flexible structure and strong universality, which is suitable for modular design. Furthermore, in order to enhance the reliability of the system, an embryonic-based circuit design framework for avionic applications is proposed. The design framework combines the advantages of bio-inspired self-repairing technology and dynamic reconfiguration, which supports on-chip self-test and fault tolerance. The proposed architecture can be widely used as a new platform for self-repairing systems.
Open Dots: Securely Connecting Like-Minded People Using Machine Learning

Often, in today’s world, it is difficult to make new acquaintances if we discuss in our social group, and even for individuals, finding someone who has common interests can be challenging. Additionally, by utilizing web3, WebRTC, and machine learning, this project facilitates safe connections between individuals with shared interests located all over the world. Every person in this world has unique interests, preferences, and dislikes. Everyone wants to get in touch with someone who shares their interests so that they can communicate more effectively. We support the connection of all types of people in this project because some people need mentoring, others want to practice interviews, others enjoy listening to stories, and still, others want to perform stand-up comedy. People who enjoy learning about new cultures from various nations and languages can also connect.The entire user’s interest data, including age, favourite subject, learned programming languages, consulting interest, interview interest, current employment history, favourite Netflix shows, favourite movies, favourite hero, and favourite song playlist, will be collected for this project. We use all the data from the various individuals to match people using a machine learning algorithm, and then, based on the outcomes, we connect the people using WebRTC so that they can communicate face-to-face while sharing real-time audio and video. More user interest information will increase the precision of finding the ideal match. Our algorithm matches you with various people who can observe, suggest to you, and help you eliminate loneliness by talking to other people while people share their screens and work on tasks like studying and coding.
MOBILE APPLICATION FOR FOOD GROCERY SYSTEM “SHOW ME A FOOD EXPIRY”

In the Internet of Things (IoT), mobile applications are gaining attention nowadays.Mobile applications have been widely used for sharing data.So, digital food grocery is replacing the traditional way of purchasing food items.In today's world, working people can't manage all the things at the same time.A Food Grocery System plays a very important role because of this system small-scale shopkeepers can enhance their area.Buyers can register themselves and access food discounts.Multiple products are available in one place with multiple choice.Existing methods for food grocery systems are based on either discounts or other details of food items and, therefore are not reliable to customers.To overcome this problem hybrid approach is proposed.The hybrid approach shows the details of food items but this hybrid approach does not show the expiry date and manufacturing date of food items.The proposed work aims to show the collection of various food items such as green vegetables, fruits, grains and pulses and dairy products and their details such as market name, manufacturer name, packing date, expiry date, Net contents, MRP and current discount on the food items as per the food expiry date on a database.As the food expiry date arrives, the discount gradually increases from the previous discount.The user gets the desired details about the food items on a mobile application such as discount that showcases the market name, manufacturer name, packing date, expiry date, Net contents, MRP and current discount.The user compares the discounts on the food grocery with another market that is displayed on the application for ensuring better shopping of food that saves time and money.
Historical and Philosophical Perspectives on the Activity of Modelling

The activity of modelling entails the conception, representation and execution of a model.In this paper we explore this activity from a historical perspective, by positioning it within the philosophy of science, and by taking a practice-based perspective.We draw a clear distinction between the internal and implicit conceptual model that exists within the mind of the modeller, and its external and explicit representational and executable forms.As a field we have focused much of our efforts on model representation and execution, while largely skirting around the processes that lie behind how models are conceived.Ultimately, better understanding these processes is important for improving the practice of modelling.
When David and Goliath work together

When MCI was founded in 1994, there was no way to know what it would become more than 25 years later, namely a university of applied sciences. As this development was unplanned and unforeseen, MCI had to devise an unconventional solution for providing library resources. As a result, a cooperation agreement was formulated between the University Library and MCI. I will describe the evolution of this collaboration and its impact on library strategy in general. I will also discuss the cooperation's advantages, disadvantages, and challenges. Finally, I will provide insights into recent developments and the future outlook.
From fee to free

Interlibrary loan (ILL) service has always been considered as a special service within the library offer. As such, it is customary to find that ILL has its own cost policy and is not a free service for registered users. At UT2J university, ILL is free, both for end users and for other ILL services that request materials from our collections. Indeed, by providing free ILL for other libraries, we are able to provide free ILL for our staff and students. The fact of eliminating the revenue generated by lending materials also eliminates the expenses related to borrowing requests thanks to the principle of reciprocity. This economic model is virtuous, guarantees equal access to the documentation to all users, and allows us to promote our collections at a national level.
Four years of ILL service at the Faculty Library of Arts and Philosophy (Ghent University)

This paper aims to give an insight into the working of ILL services at the Faculty Library of Arts and Philosophy of the Ghent University. Additionally, we take a closer look at two challenges in the field of ILL: e-book lending and controlled digital lending (CDL). Putting these challenges into practice can give a new incentive to ILL. At this moment, however, it's still work in progress.