Reviewerly/exHarmony huggingface dataset:
Communication Aspects of Media, Publishing, and Editorial Activities of University Libraries

Objective. The communication aspects of media, publishing, and editorial activities of university libraries were studied based on their official websites and the experience of the Scientific Library of the Drahomanov Ukrainian State University. The objective of this study is to track new trends in the library's media and communication activities. Methods. The methods used were observation, description, analysis and comparison of the content of the websites of Ukrainian and foreign libraries, as well as questionnaires and interviews with university stakeholders related to media activities. Results. The survey showed that the interaction of libraries with the media in the areas of information, consulting, providing factual and bibliographic references, access to collections and a pool of experts in various fields, and various educational events is in high demand. The means of popularizing and distributing their own media content are also common to libraries and media. Conclusions. University libraries should intensify their work with media and publishers on parity and mutually beneficial terms. Since the library in modern conditions produces and offers its own media products to the general public, the mass audience, it should also use the work methods of publishers and editorial offices with different groups of readers.
Sergey Arno’s Personal Comments on the Value of Literature and the Function of Literary Criticism Incorporated in His Works

On the one hand, Sergey Arno’s prose stands out due to its black humour, full of paradoxical thoughts and absurd imaginations, but on the other hand, it deals with the condition of literature, its function, value, and literary criticism (i.e. its assessment). Apart from addressing literary subjects, his works relatively often reveal autobiographical and self-referential elements. For this reason, it is possible to extract from Arno’s texts fragments investigating the value of literature and its assessment that can be identified with the author’s own position. This article is an attempt to reconstruct the writer’s views based on four of his works in prose: three fictional pieces and one non-fiction book.
Stable Visual Pattern Mining via Pattern Probability Distribution

Visual patterns are the fundamental elements that compose an image and often convey higher-level semantics. Visual pattern mining can be widely applied to real-world applications and various downstream tasks, such as tourist destination recommendation, scenic area promotion, etc. Therefore, it is a fundamental issue in computer vision. Visual pattern has two properties: frequency and discrimination. Most emerging studies first perform self-supervised representation learning and a supervised classification task to extract discriminative feature, and then mine the visual patterns through clustering algorithms to ensure the frequency. However, these methods require the manual setting of the hyperparameters (e.g. clustering radius eps in DBSCAN, etc.) to which the mining results are sensitive. Improper hyperparameters will result in obtaining redundant visual patterns or missing visual patterns, and the optimal hyperparameters differ among categories. To address these problems, we propose a new stable mining method which directly obtains visual patterns in each category without using clustering algorithms. First, we propose a Pattern Distribution Extractor (PDE) to ensure the discrimination and extract the pattern probability distribution, which represents the probability of the image belonging to each candidate pattern cluster. Experiments demonstrate pattern probability distribution provides more stability in mining stage than the previous methods. Further, we propose a Semantic Density Mining (SDM) strategy which first adaptively removes the non-frequent images to ensure the frequency and then directly obtains the visual patterns in each category according to the pattern probability distribution. Extensive experiments demonstrate the effectiveness of our proposed method.
Enhancing Open-Set Object Detection via Uncertainty-Boxes Identification

Open-set object detection is a challenging task in computer vision, which aims to detect known object categories while simultaneously identifying unknown objects. Inspired by how humans naturally distinguish unseen objects by comparing their similarities and dissimilarities with known objects, we propose a novel network called UNBDet (Uncertainty-Boxes Detection) for enhancing open-set object detection. Our approach, UNBDet, utilizes a Pseudo Proposal Advisor (PPA) to generate a wide range of unknown object candidates to improve learning and make the distribution of pseudo unknowns more consistent with the actual unknown object distribution. Furthermore, we employ an Unknown Probability Estimator (UPE) and Uncertainty-NMS modules to reason the predicted overlapping and uncertainty-boxes from multiple known classes, thus enabling easy identification of unknown objects with high uncertainty. Our experimental results demonstrate that UNBDet significantly outperforms state-of-the-art models in open-set object detection.
Intelligent Head-bot, towards the Development of an AI Based Cognitive Platform

A cognitive humanoid head is an AI enabled head-bot platform that resembles human's cognitive abilities, such as perception, thinking, learning, and decision-making. The platform is able to interact with human through natural language processing and recognize individuals, thus allowing seamless communication between two parties. No such cognitive platform has been introduced in Bangladesh, thus creating an open field to contribute to the field of Machine Intelligence. This study aims to develop an AI based humanoid head (head-bot) capable of imitating a range of expressions, recognizing individuals, and interacts with visitors through general conversation. The head-bot skeleton is developed using a number of hexagonal blocks of PVC sheet to mimic a human-head-like structure where LCD, camera, microphones, and speaker are mounted. Two separate Machine Learning models are designed for face detection and recognitions, and voice enabled chat-bot implementation. The head-bot platform incorporates 2-DoF neck movements for various head gestures and face tracking. The Artificial Neural Network models are tested with accuracy of 95.05%, and 99.0%, for face detection and recognitions, and speech recognitions and response generation, respectively. According to the overall results and system performances, it seems that the proposed system has a number of good potentials for real life applications such as entertainment, guidance, conversations, interactive receptionists, personal companion, medical assistance, and so on.
ITCNN: Incremental Learning Network Based on ITDA and Tree Hierarchical CNN

In class incremental learning, sensitive changes in network parameter tuning pose a significant problem. To address this challenge, we propose a new model called ITCNN, which combines a tree-like hierarchical network structure with incremental tensor discriminant analysis (ITDA). Unlike previous deep convolutional neural networks (DCNNs) that rely on data organization, ITCNN is feature-driven, using a tree hierarchy that can learn and grow with new data while retaining the classification functions of previous categories. The use of ITDA obviates the need to represent high-dimensional training samples in vector form, and the incremental computation has less space and time complexity. Compared with other incremental learning methods, our proposed network achieves competitive accuracy on multiple datasets. Our results suggest that ITCNN is an effective model for addressing the challenges of class incremental learning, providing increased accuracy while retaining efficiency and scalability.
SAMDConv: Spatially Adaptive Multi-scale Dilated Convolution

Dilated convolutions have received a widespread attention in popular segmentation networks owing to the ability to enlarge the receptive field without introducing additional parameters. However, it is unsatisfactory to process multi-scale objects from different spatial positions in an image only by using multiple fixed dilation rates based on the structure of multiple parallel branches. In this work, a novel spatially-adaptive multi-scale dilated convolution (SAMDConv) is proposed to adaptively adjust the size of the receptive field for different scale targets. Specifically, a Spatial-Separable Attention (SSA) module is firstly proposed to personally select a reasonable combination of sampling scales for each spatial location. Then a recombination module is proposed to combine the output features of the four dilated convolution branches according to the attention maps generated by SSA. Finally, a series of experiments are conducted to verify the effectiveness of the proposed method based on various segmentation networks on various datasets, such as Cityscapes, ADE20K and Pascal VOC. The results show that the proposed SAMDConv can obtain competitive performance compared with normal dilated convolutions and depformable convolutions, and can effectively improve the ability to extract multi-scale information by adaptively regulating the dilation rate.
Water respiratory system of Zhatai working village

The village of Zhatai is located on the left bank of the Lena River, 15 km below the city of Yakutsk, covering an area of 22 km2, where 9,200 people live. This article examines the state of the water drainage and wastewater treatment system in the village of Zhatai. In 2022, new sewage treatment facilities were put into operation. The operation of sewage treatment facilities for a year showed that the complex cannot provide the required degree of purification. Unfortunately, after a detailed study of the design documentation, a number of design flaws were identified, which also would not allow the required degree of wastewater treatment to be achieved. Recommendations for eliminating design deficiencies were developed and issued to the customer.
High-Performance Encryption Algorithms for Dynamic Images Transmission

With the proliferation of the internet, the issue of image tampering has escalated, necessitating robust image encryption schemes. Despite the multitude of proposed image encryption algorithms, challenges such as slow computational speed, weak security, and suboptimal visibility persist. This study addresses these challenges by introducing a high-performance encryption algorithm tailored for dynamic images—QEDI (Quick Encryption Algorithm for Dynamic Images). QEDI leverages elliptic curve-based asymmetric encryption algorithms and hash algorithms. The process involves using a hash algorithm to calculate the hash of the dynamic image slated for encryption, employing the elliptic curve algorithm to generate public and private keys, and utilizing the asymmetric encryption algorithm to process the image hash, resulting in a signature ciphertext. This ciphertext is embedded into the dynamic image, completing the encryption process. To enhance the execution speed without compromising the image quality and visibility, the bit depth of the dynamic image is reduced before hash calculation, and custom information fields are encapsulated to embed the signature ciphertext into the dynamic image. Experimental evaluations, conducted within a dynamic image generation system, encompassed assessments of security, encryption algorithm execution time, and image quality. The results indicate that utilizing QEDI for dynamic image encryption yields better security compared to EDI (Encryption Algorithm for Dynamic Images). QEDI exhibits minimal impact on image quality, with a noteworthy 69.77% reduction in execution time compared to EDI. Furthermore, when compared to existing algorithms employing image conversion for encryption, QEDI-encrypted images demonstrate enhanced visibility, better image quality, and expedited encryption speed.
Analisis Framing Penulisan Pemberitaan Tragedi Kerusuhan Stadion Kanjuruhan Malang di Kompas.id

Recently, the club from Malang is facing sad news. Several mass media reported an event about Indonesian football over the tragedy of riots in the Liga 1 Indonesia match at Malang's Kanjuruhan Stadium which is the home of the Arema FC club. The riot occurred when the match between Arema FC and Persebaya Surabaya was held on Saturday, October 1, 2022, at 20.00.. When the incident happened, it had shocked the world of football. Some media began to change the headline of the news into a riot event at Malang's Kanjuruhan stadium. One of them is Kompas.id media, where media journalists Kompas.id shape and frame an event that will later become a headline in their media. So this study aims to find out how the writing of media journalists Kompas.id through Syntactic, Script, Thematic and Rhetorical Structures in framing the tragedy of the riot at Malang's Kanjuruhan Stadium which will later be used as a report. The method used in this study is a descriptive qualitative method with framing analysis of Zhong Dang Pan and Gerald M. Kosicki models. The results of this study show that journalists Kompas.id in framing the tragedy of the riot at Malang's Kanjuruhan Stadium more prominent information related to the causes and mistakes committed by security forces and the organizing committee in the content of the news text with different discussion themes in each report, so that the news led to this opinion ofthe public who viewed The image of the police and the organizing committee is also bad for the community. It can be seen from the four framing structures observed in this study, namely (Syntax, Script, Thematic, Rhetorical).
IMPLEMENTASI ALGORITMA SHA UNTUK ENKRIPSI TEKS BERBASIS MOBILE

In today's digital era, text messages have become one of the most common communication methods used by many people around the world. However, the use of text messages also poses security risks that need to be considered. Text messages can be targeted by hackers who are after personal information or important data that is sent through text messages. Mobile devices that are always connected to the internet can make text messages easily hacked or intercepted by unauthorized parties. Therefore, the use of encryption methods on text messages is very important to enhance the security and privacy of the information sent through text messages. This research aims to implement the Secure Hash Algorithm (SHA) algorithm on a mobile application for text encryption. The implementation of the SHA algorithm on the mobile application is done using the Java programming language and utilizing Android's built-in library to process data. The results of the study show that the mobile application using the implementation of the SHA algorithm can provide higher security for data containing messages sent over public networks
Assistance in the Use of the Quizizz and Mentimeter Platforms to Improve the Competence of Mathematics Teachers in the City of Flores

The background to the implementation of the Community Service Program was the difficulties teachers and prospective math teachers in East Flores City had in designing and managing interactive learning online. So, the main purpose of this service is to increase the competence of teachers and prospective teachers according to the needs of the target community. The dedication stage consists of (1) the Preparation Stage; (2) the Implementation Stage, and; (3) the Practice, Monitoring, and Evaluation Stage. All stages of the service are carried out in the period 1-30 August 2021. The implementation stage of the service is carried out through the webinar method and the implementation of interactive learning designs using the Quizizz and Mentimeter platforms. The target community is 20 people. The Service Team consists of 3 students and one supervisor. One mathematics education expert from Ahmad Dahlan University (UAD) was involved in the implementation stage. Based on the Practice, Monitoring, and Evaluation Stages, it is known that all participants have implemented the webinar material, and more than 85% of participants stated that this service program was very useful and increased competency in designing and managing interactive learning online. Thus it can be concluded that the implementation of community service has achieved the stated goals.
CoomFL:Improving Global and Local Model Coordination Strategies for Heterogeneous Medical Image Tasks in Federated Learning

Federated learning has been widely applied in medical image processing tasks as a new collaborative learning strategy. However, the heterogeneity of image data between different institutions leads to distribution differences, which damage the performance of the joint model. Previous research has mainly addressed the drift issue caused by distribution differences through local or global processing, but there is still significant room for improvement in jointly addressing drifts generated by local and global coupling. To address this issue, we have designed a new coordination framework called CoomFL. First, we employed an image amplitude normalization strategy to generate a more coordinated feature space at the local client, reducing drift in local updates. Next, we designed a parameter perturbation term based on the coordinated feature space and the idea of generating adversarial images to construct a more easily aggregated client model. We then designed a coordinated momentum gradient update strategy for global and local models to reduce the differences between global and local updates. During parameter aggregation, we also designed a weight penalty strategy. Our perturbation strategy and coordinated momentum update strategy jointly promote model aggregation, reducing the gap between global model parameters and local model parameters, while guiding the model towards the optimal solution in a global sense. Under the influence of the aggregation weight penalty, our model can also control the stability of the optimization process. We conducted multiple experiments on medical image classification and segmentation tasks, and the results show that CoomFL achieves state-of-the-art performance in terms of convergence and accuracy in tasks such as image classification. Specifically, CoomFL ensures that the model performance has a lower performance difference in federated learning tasks with heterogeneous clients.
A Sampling-Based Logistic Regression Model for Credit Card Fraud Estimation

One of the most frequent problems that we are facing today is credit card fraud detection, and the most definite reason behind it is the phenomenal increase in online transactions. Currently, we are often facing such fraud cases due to unauthorized purposes of money transactions in our everyday life. Hence, to detect such fraudulent activities, we can use credit card deceit assessment model. In this paper, we propose our approach to detect such frauds. Our study mainly attempts to address upon the application of predictive techniques on this domain. The algorithms that we have used are logistic regression, decision tree classifier, and random forest classifier. The derived results are evaluated using accuracy, precision, recall, and F1-score. We have used all three algorithms for both undersampling and oversampling cases. The logistic regression technique generates the optimum result thereby giving the best accuracy, precision, recall, and F1-score. Thus, it can be inferred as the best alternative in detection of credit card frauds.
A self-supervised network-based smoke removal and depth estimation for monocular endoscopic videos

In minimally invasive surgery videos, label-free monocular laparoscopic depth estimation is challenging due to smoke. For this reason, we propose a self-supervised collaborative network-based depth estimation method with smoke-removal for monocular endoscopic video, which is decomposed into two steps of smoke-removal and depth estimation. In the first step, we develop a de-endoscopic smoke for cyclic GAN (DS-cGAN) to mitigate the smoke components at different concentrations. The designed generator network comprises sharpened guide encoding module (SGEM), residual dense bottleneck module (RDBM) and refined upsampling convolution module (RUCM), which restores more detailed organ edges and tissue structures. In the second step, high resolution residual U-Net (HRR-UNet) consisting of a DepthNet and two PoseNets is designed to improve the depth estimation accuracy, and adjacent frames are used for camera self-motion estimation. In particular, the proposed method requires neither manual labeling nor patient computed tomography scans during the training and inference phases. Experimental studies on the laparoscopic data set of the Hamlyn Centre show that our method can effectively achieve accurate depth information after net smoking in real surgical scenes while preserving the blood vessels, contours and textures of the surgical site. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods in effectiveness and achieves a frame rate of 94.45fps in real time, making it a promising clinical application.
Spatial and Temporal Awareness Network for Semantic Segmentation on Automotive Radar Point Cloud

Radar sensors are vital for autonomous driving due to their consistent and dependable performance, even in challenging weather conditions. Semantic segmentation of moving objects in sparse radar point clouds is an emerging task that contributes to improving the safety of autonomous driving. However, the methods still need to be explored since radar points in driving scenes are distributed sparsely and irregularly. Typical methods address the sparsity by aggregating multi-scan data to generate dense point clouds, where the temporal correlation is ignored. Inputting consecutive frames of point clouds can preserve temporal information, but the irregular distribution makes it difficult to achieve interframe communications. In this paper, we propose a scheme to process points of multi-scan data into a single frame with the availability of temporal features. Our novel network, called Spatial and Temporal Awareness Network (STA-Net), enables points at different times to interact and establish their spatiotemporal connections to comprehend the surroundings of these points. Furthermore, we design a shallow feature extraction method based on the radar measurements of points, which enhances the representation of local features. To further improve the capability of the network to distinguish between various moving objects, we also introduce a prompt layer inspired by prompt-based learning. This layer instructs the network to generate a discriminative representation for each type of moving object. Our experiments demonstrate that our network achieves state-of-the-art performance compared to other methods designed on the RadarScenes dataset. In particular, it shows a remarkable ability to segment small objects such as pedestrians.