Reviewerly/exHarmony huggingface dataset:
Implementation of Management Decisions for the Implementation of Blockchain Technologies in Public Authorities

In recent years, there has been growing interest in blockchain-based operating systems from computer science, economics, and law, but the literature is still in its infancy. The public sector is becoming an area of application for blockchain technology, with governments and others having published more than 200 examples of its use worldwide. However, despite the growing interest, actual implementation of blockchain-based technologies and systems in public administration remains limited. Previous studies have highlighted a number of challenges associated with its implementation, including inadequate regulation, security and privacy issues, lack of interoperable infrastructure, inefficient and energy-intensive transactions, the need for changes in administrative processes, and ultimately, the lack of effective governance models. The purpose of this paper is to identify what management decisions need to be made for the development of blockchain technologies and systems in public authorities and how different contextual factors in public sector affect the available options for blockchain governance. The literature review showed that managerial decisions regarding the use of blockchain in public institutions need to be defined at different levels. At the same time, all these levels are not independent levels of blockchain management, but interrelated ones. It was defined, that blockchain governance in public authorities includes different solutions related to decision mechanisms, incentive mechanisms, consensus building mechanisms, organization, accountability, and management control. Several important conclusions were drawn for system developers and decision makers interested in applying blockchain solutions in public administration. First, politicians and system developers need to consider the interrelationships between levels of public administration and assess the impact of decisions at one level on other levels of public administration. Second, the state current frameworks in a particular sector (e.g., health, security, education, etc.) may influence the choice of a particular blockchain-based governance option. Third, the implementation of blockchain-based systems and technologies in public administration depends on the level of public trust in government. Therefore, further research is needed to clarify the relationship between trust in public institutions and the political choice to build a blockchain-based governance structure.
Spatial-Temporal Unfold Transformer for Skeleton-based Human Action Recognition

Transformer-based architecture has been proven to be effective for action and gesture recognition. In contrast to Graph Convolutional Networks (GCNs), it can automatically model joint relationships through attention mechanisms without any predefined topological graph. However, most of the previous approaches do attention to the spatial and temporal dimensions in a completely decoupled manner, ignoring the local dynamic features of the action and human body semantics. And the performance lag behind state-of-the-art GCN-based methods. To overcome the issues, we propose a Spatial-Temporal Unfold Attention Network (STUT). Firstly, it locally unfolds skeleton data in the temporal dimension such that all neighboring frames are included in each unfolded frame. Then, the human body structural semantics of actions are extracted by a hypergraph convolution used for guiding the local spatio-temporal attention operation in each unfolded frame.In addition, in order to distinguish the importance of different frames, we introduce temporal squeezing attention (TSE) for multi-scale global spatial-temporal modeling. Extensive experiments are conducted and our model achieves 96.4\% on NW-UCLA and 96.91\% / 94.88\% on SHREC17 (14-gestures / 28-gestures).
Improved YOLOv5 for Aerial Images Object Detection with the Introduction of Attention Mechanism

Due to the development of drone technology and researches on deep learning in computer vision, object detection of aerial images from UAVs has a wide range of applications in practical production. Aerial images have problems such as large scale variations, small and dense objects and background disturbance. Traditional object detection methods are difficult to apply in practical detection scenarios, while deep learning-based methods each has its pros and cons. In this paper, the YOLOv5 model, which is a good fit for UAVs, is chosen to deal with the challenges of aerial image object detection, and the CBAM attention module is introduced to improve the network of YOLOv5 to obtain better feature extraction capability. This paper uses dataset analysis to obtain insight into the characteristics of objects in different datasets, and uses DOTA dataset for training, combining image pre-processing and data augmentation techniques to improve the training process and enhance the robustness of the model. In addition, this paper uses Grad-CAM visualisation method in an attempt to analyse and interpret the detection results. Compared to the original YOLOv5, the improved model has an accuracy rate of 75.8%, gaining a 2.2% increase. This shows the model performs well and the attention mechanism can successfully assist in resolving the challenging issues with object detection in aerial photography scenarios.
Identifying Trust Regions of Bayesian Neural Networks

Bayesian neural networks (BNNs) offer an elegant and promising approach to deciding whether the predictions of a neural network are trustworthy by allowing the estimation of predictive distributions. However, training and prediction can only be performed approximately, and state-of-the-art approximation methods are known to frequently provide inaccurate uncertainty estimations, thus limiting the broad application of neural networks. To remedy this, we define criteria for trustworthy predictions and propose a new approach capable of identifying input space regions with trustworthy predictions. For this, we use statistical hypothesis testing on the BNN’s predictions and point out some connections to previously known calibration and uncertainty estimation metrics. We demonstrate our method using several state-of-the-art approximate inference methods on two single-input, single-output regression tasks. Our results show that the proposed approach identifies input space regions with well-calibrated uncertainty predictions while providing valuable insights into the test statistics of the underlying distributions.
Pixel Transformer for Synthetic-to-Real Single Image Dehazing

Existing dehazing methods are always unsatisfactory on real images. In this paper, we propose a novel dehazing model Pixel Transformer with an encoder-decoder structure based on the self-attention mechanism. First, a non-homogeneous synthetic haze dataset, NHS-Haze, is constructed using a random sampling stitching method to simulate real images. Second, to capture the similarity of the internal structure of the image, an encoder consisting of two basic blocks accompanied by a patch merging layer is designed. Also, a pixel shuffle layer is added after each basic block in the decoder to reconstruct the details from the feature maps. Third, the NHS-Haze dataset is used to decrease the domain difference between the real and synthetic images to mitigate the domain drift problem. Finally, cross-testing is performed on the public, synthetic dataset Haze4K and SOTA methods to evaluate the robustness of each dehazing model on different datasets under realistic scenarios.
A Novel Shark Smell Optimization with Paillier Homomorphic Encryption Based Steganography Technique for Cloud Security

Information technology has advanced greatly with cloud computing (CC). Its service-based computing allows it to offer everything as a pay-as-you-go service online. Cloud data transmission was secured by cryptography and steganography. Steganography hides transmission from undesired users, while cryptography encrypts data for security. Steganography secured cloud transmission best. Steganography used digital images as cover elements. Several time- and frequency-domain image steganography methods are described in the literature. For cloud security, this study develops SSOPHE-ST, a novel shark smell optimization with paillier homomorphic encryption. Before embedding, the PHE algorithm encrypts hidden images in the SSOPHE-ST approach. The SSO algorithm optimises key generation to improve PHE performance. Steganography pixel selection employed search and rescue optimization (SRO). Images are extracted from the embedded image on the cloud server. SSOPHE-ST is tested on different photos, and other aspects are examined afterward. Comparative research showed that SSOPHE-ST outperformed other modern methods.
Integrated Design of Artificial Neural Network with Bald Eagle Search Optimization for Osteosarcoma Classification

Osteosarcoma becomes a most leading factor of bone cancer worldwide and results in high death rates. Initial diagnosis might raise survival rate but the process seems to be time-taking (complexity and reliability indulged for extracting the hand-crafted features) and majorly relies on the experience of pathologists. This work mainly concentrates in the enhancement of diagnosis and recognition of osteosarcoma by making use of computer aided diagnosis (CAD) and computer aided detection (CADx). These tools as convolutional neural network (CNN) could reduce the workload of physicians and offer superior prognosis for patient conditions. CNNs must be well-trained on an enormous volume of data for attaining a more promising performance. The study emphasis on the design of bald eagle search optimization with deep transfer learning (DTL) model for osteosarcoma classification (BESODL-OC) algorithm. The projected BESODL-OC methodology scrutinizes the histopathological images (HSI) for the identification of osteosarcoma. To achieve this, the BESODLOC algorithm exploits Gabor filtering (GF) model for noise removal process. Besides, a deep convolutional neural network (DCNN) based Inceptionv3 methodology is exploited for feature extraction process. Since trial and error based hyperparameter tuning process will be a tedious process, the BESO algorithm is utilized in this study. At last, artificial neural network (ANN) method can be exploited for classifying purposes. A wide range of experiments were made to exhibit the improvised outcome of the BESODL-OC system. Wide-ranging comparison analysis demonstrated the enhancements of the BESODL-OC system over other recent approaches.
Automated Routine Colon Cancer Nuclei Classification Using Black Widow Optimization with Deep Learning Model

Precise and efficient classification of histological cell nuclei is of great significance because of its promising application in the domain of medical image analysis. It facilitates the physician to explore various factors and better understand the treatment of cancer. Due to cellular heterogeneity detection and classification of cell nuclei in histopathology images of tissue stained with the typical haematoxylin and eosin stain becomes a tedious process. The deep learning approach has been demonstrated to produce remarkable outcomes on histopathology images in different fields. Therefore, this study designs an Automated Routine Colon Cancer Nuclei Classification Using Black Widow Optimization with Deep Learning (ARCCNC-BWODL) model. The presented ARCCNC-BWODL algorithm focuses majorly on the identification and classification of RCC cell nuclei. The presented algorithm applies improved Faster SqueezeNet model to make feature vectors. Besides, the hyperparameter tuning of the Faster SqueezeNet approach is performed via the BWO system. To classify the nuclei effectively, long short term memory is used. The simulation outcome of the ARCCNC-BWODL model was tested on medical imaging database and the outcomes exhibited the enhancements of the ARCCNC-BWODL over other DL techniques.
Red Colobuses Monkey with Deep Recurrent Neural Network based Gender Classification on Health Web Forum Data

Computational Linguistics emphasizes the real-time consequences of modeling the utility of human languages. Applied Computational Linguistics can be utilized for several purposes in today's domain of scientific research and development, linguistic research, and business. Public utilizes health-based web forums to access health information easily. The government will operate medical web forums for providing medical data, managing needs and feelings of patients, and fostering information-sharing between patients. Gender classification is needed for automated identification of gender among people. This article presents a novel Deep Recurrent Neural Network for Computational Linguistics Driven Gender Classification (DRNNCL-GC) on Health Web Forums. The presented DRNNCL-GC technique is employed for the examination of health web forum data for gender recognition process. For attaining this, the DRNNCL-GC technique applies data preprocessing and feature engineering phases. Next, the DRNNCL-GC technique make use of DRNN model for gender classification process. Finally, Red Colobuses Monkey (RCM) algorithm is utilized for hyperparameter optimization process, which is stimulated by the characteristics of red monkeys in nature. The performance evaluation of the presented DRNNCL-GC technique is tested and the outcomes were measured under distinct aspects. A comprehensive comparison analysis highlighted the enhancements of the DRNNCL-GC technique over other recent approaches.
San Andreas fault may shake less than once feared

Precariously balanced rocks near Los Angeles hold clues to the hazard of a giant earthquake.
Unsupervised Feature Selection with Latent Relationship Penalty Term

With the exponential growth of high dimensional unlabeled data, unsupervised feature selection (UFS) has attracted considerable attention due to its excellent performance in machine learning. Existing UFS methods implicitly assigned the same attribute score to each sample, which disregarded the distinctiveness of features and weakened the clustering performance of UFS methods to some extent. To alleviate these issues, a novel UFS method is proposed, named unsupervised feature selection with latent relationship penalty term (LRPFS). Firstly, latent learning is innovatively designed by assigning explicitly an attribute score to each sample according to its unique importance in clustering results. With this strategy, the inevitable noise interference can be removed effectively while retaining the intrinsic structure of data samples. Secondly, an appropriate sparse model is incorporated into the penalty term to further optimize its roles as follows: (1) It imposes potential constraints on the feature matrix to guarantee the uniqueness of the solution. (2) The interconnection between data instances is established by a pairwise relationship situation. Extensive experiments on benchmark datasets demonstrate that the proposed method is superior to relevant state-of-the-art algorithms with an average improvement of 10.17% in terms of accuracy.
Pembuatan E-Logbook Kegiatan Harian Karyawan Honorer Di Dinas Perpustakaan Dan Kearsipan Kota Samarinda

This study aims to explore the process of creating daily logbooks for contract employees at the Library and Archives Department of Samarinda City and to assess the practicality of these logbooks. The research findings indicate that the implementation of an E-logbook for the daily activities of contract employees in the aforementioned department can be considered a significant innovation in human resource management. This E-logbook facilitates the easy and efficient recording and tracking of daily employee activities. Furthermore, the E-logbook proves instrumental in managing the daily tasks of contract employees, enhancing accountability, and aiding in more informed decision-making within the company or organization.
Analysis of adversarial attacks on the machine learning models of cyberprotection systems.

Modern trends in the development of cyber protection systems are associated with the widespread use of machine learning technologies to detect and prevent cyber threats. At the same time, attackers are looking for ways to evade detection by such systems, using both traditional attack methods and new ones aimed exclusively at countering artificial intelligence - adversarial attacks. Therefore, finding ways to counteract adversarial attacks is an urgent scientific and technical task. Adversarial Machine Learning (AML) is used to study them, which consists in simulating such attacks. The purpose of research is to determine ways to increase the resilience of cyber defense systems operating with the use of machine learning technologies to the impact of attacks based on AML models. The article provides examples of the application of machine learning methods in cyber protection systems. The models of adversarial attacks are described, namely: models of evasion, poisoning, functional extraction, inversion, and models of membership inference attack. Possible scenarios of their implementation are considered. Examples of adversarial attacks on machine learning models for recognizing images and text messages, detecting domain name generation algorithms, HTTP traffic malware, malicious content in e-mails, bypassing antivirus software are analyzed. Studies have shown that even without access to the algorithms of machine learning models, it is possible to bypass the cyber protection system. Therefore, to ensure the security of networks and services by means of cyber protection with artificial intelligence, it is necessary to take into account the need to counter adversarial attacks. For this purpose, it is proposed to: collect and aggregate training data for each machine learning model individually, instead of obtaining them from publicly available sources; optimize the content of event logs, taking into account the possibility of using the information contained in them to create adversarial attacks; to ensure the protection of training data and algorithms of the functioning of models; in the case of deploying cyber protection systems on critical infrastructure objects, use specially developed machine learning models that are not publicly available, which will complicate the possibility of creating a functional extraction attack.
The modulation and elimination of temporal organization in free recall.

Experiences occur in a continual succession, and the temporal structure of those experiences is often preserved in memory. The temporal contiguity effect of free recall reveals the temporal structure of memory: when a particular item is remembered, the next response is likely to come from a nearby list position. This effect is remarkably robust, appearing across a wide variety of methodological variations of the task. The temporal contiguity effect is also central to retrieved-context models, which propose temporal organization arises from the interaction of a temporal context representation with the contents of memory. Across six experiments, we demonstrate methodological manipulations that dramatically modulate and even eliminate temporal organization in free recall. We find that temporal organization is strongly modulated and in some cases potentially eliminated by strong semantic structure, the presence of retrieval practice, and a long list length. Other factors such as orienting task, paired-associate item structure, and retention interval duration have more subtle effects on temporal organization. In an accompanying set of simulations, we show that the modulation and elimination of the temporal organization follows lawful patterns predicted by the context maintenance and retrieval (CMR) retrieved-context model. We also find cases where CMR does not specifically predict the modulation of temporal organization, and in these cases our analysis suggests how the theory might be developed to account for these effects. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Embedded Real-Time Human Activity Recognition on an ESP32-S3 Microcontroller Using Ambient Audio Data

The task of Human Activity Recognition (HAR) finds application in smart homes and assisted living and is mostly approached with machine learning. Sensor data for HAR is often collected and processed by separate devices. Performing the recognition task directly on the sensor device can decrease network traffic and reduce the related energy consumption and privacy concerns. In this work, an intelligent sensor device is developed that performs real-time HAR on an ESP32-S3 microcontroller using ambient audio data. A convolutional neural network to classify human activities is designed and subsequently reduced in size and computation cost in order to enable its inference on the microcontroller. This is achieved with neural architecture search, resulting in multiple simplified model variations. These are tested on the ESP32-S3 and a feasible version is identified.
Teachers’ Collaborative Reflections on Classroom Materials: From Traditional Consumers to Digital Materials Developers and Teacherpreneurs

L2 teachers, no longer considered to be predominantly passive coursebook consumers, act as mediators between pedagogy and materials in the current era of technology-assisted language instruction and should be empowered to digitalize language learning materials to meet the immediate needs of the digital generation. In this study, we aimed to investigate L2 teachers’ collaborative reflections on the current gaps in their classroom coursebook that could be bridged by the purposeful use of digital tools and address their attitudes towards their changing roles as digital materials developers. The findings demonstrated that, in their critical reflections, from a pedagogical perspective, the teachers pinpointed the dominance of passive language usage, the absence of personalized strategy-based instruction, and the lack of interactive content in the coursebook. From a task design perspective, they referred to the dominance of edutainment-free tasks, the prevalence of artificial peer tasks, the lack of multimodal scenario-based tasks, and the absence of adaptive feedback. The teachers further highlighted their shifting roles. As digital materials developers, they could create digital content for the “digital natives,” make digital mini-lessons for classrooms, attend project-based training courses, and co-produce digital content and tasks with students’ assistance. As digital teacherpreneurs, they could run private social media-based businesses, join Edu-Tech co-working spaces, and produce digital artifacts in edutainment centers. As digital materials designers, they could collaborate with computer-assisted language learning (CALL) stakeholders and do interdisciplinary projects in virtual communities. This study promises implications for L2 teachers, teacher educators, and materials developers.
Morphological Reanalysis of Word-Formation Elements

Recently, it has been frequently observed in the Korean language that a portion of a word actively participates in the process of word formation in a fixed form. However, there is a lack of clear discussions regarding the morphological status of this “fixed element” whether it has a morphological status, what kind of morphological status it possesses if it does, and how it is formed. Therefore, this study aims to argue that such “fixed elements” can have a morphological status. By focusing on the phenomenon where elements that were previously used as part of a word appear productively in the process of word formation and acquire new function, the study aims to typologize how they are formed and what they become based on morphological reanalysis. These results suggest that fixed elements that were not morphemes in the process of neologism formation become “combining form”.