Reviewerly/exHarmony huggingface dataset:
Faster-RCNN Based Cloud Region Recognition Algorithm for Single Images

Remote sensing imaging is frequently disturbed by clouds which leads to unclear images with low contrast and poor resolution. Cloud obstacles usually arouse the valuable information loss of remote sensing images. Technology of cloud removal from single remote sensing images has attracted worldwide interests since only one image is available. When clouds are distributed unevenly and only a small portion of the images is covered by clouds, it is expected to preserve image information outside of the clouds as much as possible during cloud removal. In this paper, an algorithm based on Faster-RCNN was proposed to detect the cloud regions in the images before cloud removal. The principle of cloud region recognition was analyzed. The structure of Faster-RCNN was introduced. Three convolutional networks i.e. MobilenetV2, Resnet50 and VGG16 were introduced and applied as the backbone of Faster-RCNN respectively. Cloud region recognition algorithm was developed based on Faster-RCNN. Training and testing data sets were established and labeled by applying the proposed remote sensing imaging simulation algorithm to add clouds to the clear remote sensing images. Some experiments were carried out when Faster-RCNN selected MobilenetV2, Resnet50 and VGG16 as its backbone and was trained to optimize the parameters. Their results were compared. It proved the proposed algorithm with MobilenetV2 as the backbone achieved a successful recognition rate of 95% which supports the following cloud removal.
Fake Product Identification using Blockchain

Blockchain technology is the distributed, decentralized, and digital ledger that stores transactional information in the form of blocks in many databases which is connected with the chains. Blockchain technology is secure technology therefore any block cannot be changed or hacked. By using Blockchain technology, customers or users do not need to rely on third-party users for confirmation of product safety. In this project, with emerging trends in mobile and wireless technology, Quick Response (QR) codes provide a robust technique to fight the practice of counterfeiting the products. counterfeit products are detected using a QR code scanner, where a QR code of the product is linked to a Blockchain. It collects the unique code from the user and compares the code against entries in the Blockchain database. If the code matches, it will give a notification to the customer, otherwise it will give the notification to the customer that the product is fake.
Crypt Cloud: Secure and Expressive Data access by CP-ABE

Secure distributed storage, a new cloud management, is designed to give cloud clients with out-of-control data flexible access to information while maintaining the confidentiality of redistributed data. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is regarded as one of the most promising methods for confirming the administration's security. Nevertheless, the inherent "win or bust" decoding feature of CP-ABE may result in an unavoidable security breach known as the abuse of access certification (such as unscrambling rights). In this paper, we research the two major cases of access capability misuse: One is in favor of cloud client, while the other is on the semi-trusted specialist side. To direct the maltreatment, we propose the vitally mindful master and revocable CP-ABE based circulated capacity system with white-box obviousness and auditing, insinuated as CryptCloud+. We also talk about the security investigation and use of our framework in real-world situations
Responsive Web Design and Its Impact on User Experience

The introduction of smartphones and the ability to access the Internet on them altered how people use the Internet. Nowadays, a lot of individuals visit websites on their mobile devices. However, because the Web content on the devices varies in size and the navigation of the pages is also different in the various devices, the different screen sizes of these gadgets have some impact on the experience of their users. The introduction of the responsive web design philosophy changed the layout of web sites and how people see them on various devices.
Sentiment Analysis using Machine Learning Techniques

Analytics research includes the field of sentiment analysis. To make sense of this, computational methods can be used to read raw data. Analysis is what this is. Written expression that is either positive, negative, or neutral can be assessed using sentiment analysis. People use a variety of social media platforms, including Facebook, Twitter, etc. Machine learning algorithms can be effectively used to ascertain people's sentiments. Sentiment analysis is a field that has developed to automate the study of such data. Sentiment analysis aims to identify and extract human emotions from text. It seeks to find opinionated information on the Web and categorise it based on its polarity, or whether it has a positive or bad meaning. In contrast to conventional text-based analysis, it is a text-based analysis that helps to swiftly determine the customer's reaction.
An automated neural network-based stage-specific malaria detection software using dimension reduction: The malaria microscopy classifier

Due to climate change and the COVID-19 pandemic, the number of malaria cases and deaths, caused by the Plasmodium genus, of which P. falciparum is the most common and lethal to humans, increased between 2019 and 2020. Reversing this trend and eliminating malaria worldwide requires improvements in malaria diagnosis, in which artificial intelligence (AI) has recently been demonstrated to have a great potential. One of the main reasons for the use of neural networks (NNs) is the time saving through automatising the process and the elimination of human error. When classifying with two-dimensional images of red blood cells (RBCs), the number of parameters fitted by the NN for the classification of RBCs is extremely high, which strongly influences the performance of the network, especially for training sets of moderate size. The complicated handling of malaria culturing and sample preparation does not only limit the efficiency of NNs due to small training sets, but also because of the uneven distribution of red blood cell (RBC) categories. To boost the performance of microscopy techniques in malaria diagnosis, our approach aims at resolving these drawbacks by reducing the dimension of the input data and by data augmentation, respectively. We assess the performance of our approach on images recorded by light (LM), atomic force (AFM), and fluorescence microscopy (FM). Our tool, the Malaria Stage Classifier, provides a fast, high-accuracy recognition by (1) identifying individual RBCs in multi-cell microscopy images, (2) extracting characteristic one-dimensional cross-sections from individual RBC images. These cross-sections are selected by a simple algorithm to contain key information about the status of the RBCs and are used to (3) classify the malaria blood stages. We demonstrate that our method is applicable to images recorded by various microscopy techniques and available as a software package.•Identifying individual RBCs in multi-cell microscopy images.•Extracting characteristic one-dimensional cross-sections from individual RBC images. These cross-sections are selected by a simple algorithm to contain key information about the status of the RBCs and are used to.•Classify the malaria blood stages. We demonstrate that our method is applicable to images recorded by various microscopy techniques and available as a software package.
Public Library Image through the Eyes of Users: Results of Sociological Diagnostics

The article presents the main results of the qualitative study of the opinions of information consumption subjects on the nature of the image crisis of the contemporary domestic public library. The survey, conducted in December 2022, involved 181 respondents aged from 14 to 76 years. The authors analyze the views of the user audience on the prerequisites and reasons for the decline in popularity of library and information institutions associated with the possible transformation of their social role in the context of stable growth of digital information consumption and mass access of the population to the Internet. The paper establishes the main motives for accessing library services and products in connection with the indicators of frequency of contacts with the institution and recognition of its competitive advantages. The article presents an overview of the respondentsʼ points of view on the relationship between the crisis of reading quality and the weakening of the libraryʼs market position. The authors reveal some characteristics of the subjective image of the library profession in the user environment as an image component of the activities of library and information institutions. The paper considers the attitude of the respondents to the possible employment of their growing or future children and grandchildren in the library field. The authors analyze respondentsʼ perceptions of key areas of reputational risks for a modern public library, as well as user assessments of the efficiency of its marketing efforts to promote all range of products and services. The paper outlines immediate prospects for the development of the research, in particular, related to the further study of the phenomenon of socio-psychological influence on the evolution of the model of consumer behaviour in the information market, marketing factors of the formation and fixation of consumer preferences of the library audience.
Application of Cognitive Architecture in Multi-Agent Financial Decision Support Systems

The multi-criteria character associated with making decisions entails the need for analysis and evaluation of a large amount of information and drawing conclusions on the basis of such information. Since the process is time-consuming and practically impossible to be performed by a decision-maker in real time, it is necessary to use computer decision support systems, including multi-agent systems. To support financial decision-making, the cognitive technologies can be used. These technologies support using natural language processing to sentiment analysis for the consumer ambience and to buy/sell decision-making. The aim of this chapter is to present an approach to apply cognitive architecture in a multi-agent financial decision support system. On the basis of performed researches, it can be stated that cognitive architecture allows to increase the usability of the multi-agent system and consequently to improve the process of taking investment decisions. The ability to make automatic decisions is also of high importance here. For example, the cognitive agent can make real transactions (open/close short/long position) on the Forex market.
Information Support of the Cultural Sector: Achievements, Problems, Prospects

The 2nd International Scientific and Practical Online Conference “Information Context of Culture: Resources, Technologies, Service” was held on October 11—12, 2022. Conference organizers are the Russian State Library (RSL), the Russian State Library of Arts (RSLA) and St. Petersburg State Institute of Culture (SPSIC). The purpose of the event is to summarize scientific research and practical experience on the issues of information support in the cultural sector, demonstrate the best achievements, identify problems and ways to overcome them. The leitmotif of the forum was the topic of cooperation between libraries, archives and museums of the member states of the Commonwealth of Independent States. During two days of the forum, more than 80 representatives of the cultural sector from the RSL, the RSLA, the Boris Yeltsin Presidential Library, the national libraries of Kazakhstan and Uzbekistan, public libraries of almost two dozen regions of Belarus, Kazakhstan and Russia, as well as from the universities of culture in Moscow, St. Petersburg, Chelyabinsk, Tashkent, as well as from the South Ural State University, worked in video communication mode. During the event, more than 900 viewings of the broadcasts of the meetings were recorded on the RSL YouTube channel. Within the framework of the conference, there were held the plenary session, two sessions of the Section “Information potential of libraries for the development of the cultural sector” and two special events: The 12th Conference/Meeting of the heads and specialists of information services on culture and art “Libraries — in the Year of Cultural heritage of the peoples of Russia” and the Round table “Development of library and information space on the basis of border libraries of the CIS member states: conceptual approaches and advanced directions”. In order to improve the work on information support for representatives of the cultural and art sector, the conference participants recommended to promote digital transformation aimed at the formation of common access resources on culture and art and improvement of information services; as well as to strengthen cooperation on the formation and mutual use of information resources created by the libraries of the CIS member states.
Research on crop diseases classification model based on MobileNet

To address the problems of large size and low-recognition accuracy of the Convolutional Neural Networks (CNNs) for crop disease recognition, this paper proposes an improved model of MobileNet called MobileNet-LR-SE. The model firstly uses the LR structure to reverse the feature information and get the new feature information. These together with the original feature information form the input to the feature fusion layer. The LR structure introduces residual connection and uses the Leaky ReLU activation function. Secondly, it embeds the SE module to complete the final image classification. The LR structure solves the problem of ignoring negative feature information in the training process of ordinary neural networks. The SE module improves the attention of the network model to the useful channels. Experiments show that the MobileNet-LR-SE model has a high-accuracy rate when evaluated on the two crop disease data sets, with the number of network parameters of only 2.01M.
Incorporation of motion prediction and state estimation to track dots for vision-based in-crash toeboard deformation measurement

This paper presents a marker-based stereovision method to measure toeboard deformation during a crash test. The method tracks a set of colored dots marked on the surface for stereovision using recursive state estimation that consists of a prediction step and a correction step. Prediction is based on a motion model derived by feature matching and mapping in the stereo image. In the correction step, the dots are first tracked using a dot centroid tracking (DCT) algorithm that weights the color, saturation, and brightness of pixels in a search kernel predicted by the motion model. An extended Kalman filter (EKF) subsequently fuses the prediction and the DCT measurement for the most likely dot centroid. The accuracy and robustness of the proposed method for in-crash toeboard deformation measurement are presented in a vehicle crash test. The experiment shows the proposed technique tracks the deformed surface accurately and is robust in varied light conditions and conditions when the field of view (FOV) is temporarily blocked.
DzNER: A large Algerian Named Entity Recognition dataset

Named Entity Recognition (NER) is a natural language processing (NLP) task that involves assigning labels like Person, Location, and Organization to words in text. While there is a good amount of annotated data available for NER in English and other European languages, this is not the case for Arabic and its dialects. The goal of the paper is to introduce DzNER, an Algerian dataset for NER that consists of more than 21,000 manually annotated sentences (over 220,000 tokens) from Algerian Facebook pages and YouTube channels, with a focus on three prominent classes. In this study, we provide a detailed analysis of the NER tag-set used in the dataset and show that it has a good balance of quantity, diversity, and coverage of different domains. For the proof of resource-effectiveness, we also demonstrate the effectiveness of the dataset by using various language models for the sequence labeling task of NER and comparing the results to existing datasets. According to our research and knowledge, currently no available dataset meets the standards of both variability and volume as well as DzNER. We hope that this dataset and the accompanying code and models will be useful for further research on NLP for Algerian dialect and fill the gap of low resources.
Digital Twin Through Physical Assets Tokenization in Blockchain

Tokenized assets can provide relevant advantages in terms of accessibility and market size when compared with the original assets capabilities. Furthermore, when tokens are based on the public blockchain, they natively gain peer-to-peer notarization ability, which is the required feature to grant peer-to-peer market accessibility. We attribute these motivations as the main reason of the recent boost of the token adoption. With this work we first analyze some relevant aspects of the main available tokenization approaches, such as the IoT-based, and then we propose a new concept architecture that aim to combine the best side of each approach into a new hybrid solution that use both on-chain and off-chain transactions to being suitable for any kind of physical asset.
RIMD: A novel method for clinical prediction

Electronic health records (EHR) are sparse, noisy, and private, with variable vital measurements and stay lengths. Deep learning models are the current state of the art in many machine learning domain; however, the EHR data is not a suitable training input for most of them. In this paper, we introduce RIMD, a novel deep learning model that consists of a decay mechanism, modular recurrent networks, and a custom loss function that learns minor classes. The decay mechanism learns from patterns in sparse data. The modular network allows multiple recurrent networks to pick only relevant input based on the attention score at a given timestamp. Finally, the custom class balance loss function is responsible for learning minor classes based on samples provided in training. This novel model is used to evaluate predictions for early mortality identification, length of stay, and acute respiratory failure on MIMIC-III dataset. Experiment results indicate that the proposed models outperform similar models in F1-Score, AUROC, and PRAUC scores.
Hybrid partial differential elliptical Rubik’s cube algorithm on image security analysis

Purpose Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks. Design/methodology/approach In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle. Findings To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission. Originality/value The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.
FOODwell

Hunger is an often neglected issue, with millions of people worldwide affected by it in 2021 according to the United Nations. This issue also affects minority populations in Taiwan. One solution to this problem is the implementation of suspended meals, which are free meals provided to those in need. Our proposed solution, FOODwell, aims to allow recipients to dwell on better food provision and obtain wellness for their food needs. It is a system that simplifies the process of reserving and collecting suspended meals for recipients by enabling them to interact with the kiosk at the convenience store and cashier at the restaurant, without compromising confidentiality or requiring electronic devices. Overall, FOODwell eliminates obstacles for recipients when reserving and collecting suspended meals.
COVID-19 as an Energy Intervention: Lockdown Insights for HCI

As the operation of buildings accounts for around 30% of global CO2 emissions, reducing their energy consumption is considered crucial for climate change mitigation. Aware of this significance, the sustainable HCI (SHCI) community has conducted research on energy consumption for over 15 years. However, compared with domestic environments, commercial organisations are comprised of complex mixed-use buildings, and the socio-technical understanding of space and resulting energy use are relatively under-explored. In this late-breaking work, we present the initial findings of a longitudinal analysis that uses building energy data from a period covering the COVID-19 lockdown measures to help identify the energy associated with these buildings and their users. Viewing the pandemic as a unique, grand-scale 'energy intervention', the resulting consumption patterns are used to inform questions about leverage points for achieving change, stakeholder agency vs. infrastructure demand; and highlight the importance of putting energy data in context.
Bridging Cultural Differences with Critical Design in a Globalized World

Are simplicity and minimalism the universal standards for interaction design? How can we avoid stereotyping with personas in design practices? What AI algorithms and design mechanism made "digital blackface" phenomenon on social media so popular? This interactive course teaches participants to reconsider some commonly held design beliefs and routine design practices with a lens of cultural differences. Illustrated with design case studies, it introduces strategies and techniques to turn differences into design resources for inclusivity. Participants will learn essential critical design skills of creating engaging and empowering designs in a globalized world at a divisive time.