Reviewerly/exHarmony huggingface dataset:
Hybrid compression technique for image hiding using Huffman, RLE and DWT

All kinds of information should be compressed before being sent over the web to save time & expense. The primary goal of this article is to create and construct a systematic and safe technique for encryption data that can be utilized to carry out a crisp compacting steganography technique. The approach may be used to reduce the volume of data sent with each transfer, allowing for faster delivery when utilizing sluggish web or taking up less space on a drive. The study offers a densification with steno approach that may be utilized to hide information with substantial privacy and complete invisible by combining RSA encryption technique with steganography expertise such as RLE (Run-length encoding), Huffman coding, DWT (Discrete Wavelet Transform) with LSB cryptographic technology. The very first process is based on RSA's ability to decode and encode private texts. The next process is compressing, which will include Huffman encoding based approaches to compress confidential stuff using efficient steganography technologies, RLE, which is a traditional approach to encrypt information, or DWT based on hatchbacks covering imagery by loss encryption in order to decrease the covering picture's size. The encrypted information will be implanted in the compressed cover picture using LSB (Least Significant Bit). When compared to various developed countries (Chen et al., 2021; Lakshmanan, 2020; Song et al., 2011; Yang et al., 2021; Ali et al., 2020) of the architecture that uses simulation results, this technique has a good ranking efficiency and a methodical system.
Mutual Learning Networks Of Actor Relation Graph For Group Activity Recognition

The Actor Relation Graph (ARG) is an effective method for detecting group behaviour but still needs improvement in some areas. In this paper, we propose using the sum of absolute differences (SAD) to compute the similarity of characters' appearance, introduce deep mutual learning to support the network's training, and add a visualization model. By training with the extended dataset, the results show that our improved network can achieve the expected better prediction accuracy of group activities.
Anticipate the impact of climate change at tidal swamplands through water management technology

Abstract Climate change significanly affects the production of agricultural commodities, including tidal swamplands. The current and future tidal swamplands have become one of the mainstay areas for agrarian commodity production due to the shrinking productive land. One of the keys to successful tidal swampland management is water management. In addition to increasing land productivity, water management can also reduce the impact of global climate change. Water management in agricultural development at tidal swampland needs to pay attention to the type of overflow, land typology, and land characteristics. Water management technology at recurrent event wetland embrace two-ways flow systems, unidirectional flow systems, one-way flow system and dam overflow conservation (SISTAK), water management for surjan, and shallow evacuation systems. the employment of this water management technologies depend upon the condition of water system within the fields. The water control era ought to enhance water first-class while mixed with different plant cultivation technological additives and it will likely be extra powerful in growing rice manufacturing at tidal swampland. This paper was a review of research results of water management technology in rice cultivation and its impact on increasing rice production.
A Recommendation Method for Process Modeling based on Clustering and Graph Neural Network

Process modeling plays a very important role in production and life, but the traditional process construction method is time-consuming and error-prone, so process modeling can improve the efficiency of process construction to a large extent. The task flow is modeled as graph structure, and the similarity of graph is calculated according to a hierarchical partitioning method, and then the graph is clustered by spectral clustering algorithm, so that the graph is divided into clusters. The core workflow of the cluster is identified as the center of the cluster. Finally, the clustering centers of different clusters were trained by depth graph convolutional neural network, and the results of training were used for the ordering and recommendation of task flow. Experimental evaluation shows that our technique is effective and accurate in ordering and recommending specific workflows.
Structural Entropy of the Stochastic Block Models

With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of science, effective treatment and compression schemes of context-dependent data is extremely desirable. A particularly interesting direction is to compress the data while keeping the "structural information" only and ignoring the concrete labelings. Under this direction, Choi and Szpankowski introduced the structures (unlabeled graphs) which allowed them to compute the structural entropy of the Erdős-Rényi random graph model. Moreover, they also provided an asymptotically optimal compression algorithm that (asymptotically) achieves this entropy limit and runs in expectation in linear time. In this paper, we consider the stochastic block models with an arbitrary number of parts. Indeed, we define a partitioned structural entropy for stochastic block models, which generalizes the structural entropy for unlabeled graphs and encodes the partition information as well. We then compute the partitioned structural entropy of the stochastic block models, and provide a compression scheme that asymptotically achieves this entropy limit.
DEVELOPMENT FEATURES OF UNIVERSITY STUDENTS’ PROFESSIONAL AND PERSONAL QUALITIES: FUTURE SOCIAL-WORK SPECIALISTS UNDER RUSSIAN NPO CONDITIONS

All analyses (quantitative, qualitative) of issues related to the discipline's functioning indicate that in Central and Eastern Europe, the research, publishing and environmental activity of adult-education researchers remains low compared to such centres as Sweden and Portugal.The present publication, therefore, may serve as an important element, with its inclusion of discourses and research results from Polish, Czech, Slovak, Ukrainian and Russian universities, in the awareness of a wider audience, along with conveying messages about research topics, developing andragogical theories and scientific activities of local andragogical communities in this region of Europe.Our publication is also an attempt to establish a critical dialogue with the publications mentioned above -while they are from the West, we will show perspectives from our part of Europe, perhaps still little known and underrecognised among other research circles.This publication's aim is to reflect theoretically on contemporary forms of lifelong educational activity among adults, groups and communities, and to present research results concerning the areas, models, goals and functions of adult education, teacher and educator competences, and adult-learning processes in Central and Eastern European countries.In the pages that follow, the reader will find the results of research related to contemporary adult-learning spaces, presented from different methodological perspectives and introducing different points of view.The editors' aim has been to create a space for discussion, an opportunity to present the latest research findings, thoughts and ways of interpreting educational reality.We seek to explore conditions shaping contemporary adult-learning spaces, including a comprehensive comparative perspective which holds great cognitive value, further enriched by the publication's international form.Readers are introduced to trends in developing andragogical theory and practice, and to informal learning areas as well.Attention is paid to (auto)reflection, whether shaped through knowledge or by individual experience and even common knowledge.The turn towards positively evaluating the idea of localism and social commitment has brought significant changes in many areas of contemporary adult education.Problems that adult educators face are also raised here, both in terms of their key competences and challenges they must contend with today.Institutional education is a space that researchers have abandoned in recent decades, yet it merits indepth reflection, exploration and reflection.Activities carried out by associations and foundations, the dissemination of knowledge, activities of open-educational institutions, the sector of non-public educational services: these important topics can all inspire contemporary researchers and practitioners in the adult-education field.We invite the reader to treat this publication as an inspiring, comprehensive,
Grasping Adversarial Attacks on Deep Convolutional Neural Networks for Cholangiocarcinoma Classification

Researchers have proposed plenty of novel deep convolutional neural networks (CNNs) architectures to achieve state-of-the-art performance in computer vision benchmarking datasets. Nevertheless, deep CNNs are still vulnerable to attacks such as adversarial attacks, which can effectively fool them. As a consequence, this attack will hinder the deployed model during production. Moreover, this fact causes a new obstacle towards the development of the future deep CNNs architecture, especially in some crucial fields, for instance, healthcare that uses deep learning to assist physicians in detecting disease from medical images. Since there are many novel adversarial attacks methods out there, this work evaluates the attack efficacy of various adversarial attacks methods against the model performance, specifically on the cholangiocarcinoma classification task. Intriguingly, from our experiment using several CNNs models, EfficientNet-BO gives the highest averaged accuracy after being attacked by some adversarial attack methods.
Relationship between 5G and transportation

In this article we would like to show how 5G mobile networks could be suitable for supporting transportation functions as bearer infrastructure. Some basic functions for supporting transportation are already implemented in 2G mobile networks – mostly in GSM-R used by the railway network systems – but in the near future new demands will emerge. Fifth generation mobile network standards contain these kinds of capabilities, but the implementation is not automatic and obvious. Support of transportation in modern mobile networks (self-driving cars, drones, etc.) is only possible if several certain quality characteristics (Key Performance Indicators) are met.
A Highly Efficient Intrusion Detection and Packet Tracking Based on Game Theory Approach

Wireless Sensing Networks (WSNs) offer excellent environmental monitoring capabilities and use many interesting applications in advanced network applications. The problem is that the security mechanisms used in wired networks cannot be incorporated directly in WSN due to scarce resources. In this paper, specific measures that need to be taken against across distributed wireless networks, explore some of the approaches to detect attacks, and propose a new game theory-approach in identifying the intrusion detection. Two key elements which are most challenging in mobile and wireless networks, the geo-location of the nodes will change dynamically and security between the nodes is limited and monitoring packet transmission in order to avoid various intrusion methods involved between the nodes and data transmission has to be secured with a guarding mechanism. This paper implements about how to provide advanced authentication schemes for network programming to avoid and reduce intrusion and precautionary measures to be considered in avoiding intrusion detection in advantage of vulnerabilities and attack network and causes failure. The results show that implemented research on sensor network security focuses on the ability to detect third-party intrusion attempts, even if they are attempts to attack even before it has happened If the intruder is detected quickly, appropriate action is taken before any damage occurs or if any data is available made a compromise.
A Skip-Connected CNN and Residual Image-Based Deep Network for Image Splicing Localization

Image forgery is a part of the majority of the cybercrimes that are reported nowadays. Cyber forensics demands methods for easier and accurate analysis of image forgery. Recently, deep learning networks have revolutionized various classification tasks involving images, videos, text etc. In this paper, we propose a simple 19-layered convolution neural network (CNN) with skip connection to efficiently localize image splicing forgery. A residual image which is a byproduct of image denoising is divided into blocks and given as input to the proposed network which classifies these blocks into two categories—forged/non-forged. A binary tampering possibility map is generated as the initial output of our design which is later modified by applying suitable post-processing operations for better localization of the forged region. Several experiments are conducted to ensure the efficiency of our network. Our results outperform various benchmark methods in efficiently localizing image forgery with a pixel-wise F1-score of 0.8298 in the test set from DSO-I dataset. A cross-dataset evaluation on Wild Web splicing dataset is also performed to generalize our method which yields a comparable accuracy of 0.7195.
Lightweight Stacked Hourglass Network for Efficient Robotic Arm Pose Estimation

Existing robotic arm pose estimation methods usually only focus on improving the accuracy of the model, but ignoring model efficiency, therefore the development of models with a large number of parameters and computation in practical use. To solve the problem, this paper proposes a new HetConv-Based Stacked Hourglass Network (HetConv-SHN). HetConv-SHN develops from the Stacked Hourglass Network (SHN), which constructs an SHN with two stacks, and compresses the model by replacing the standard convolution in the SHN with a lightweight HetConv (Heterogeneous Kernel-Based Convolution) module. Experiments conducted on four typical datasets demonstrate that HetConv-SHN can achieve better accuracy than its counterparts with fewer parameters and computation. It can be better deployed on robotic arms, enabling lightweight robotic arm pose estimation.
Comparative Analysis of Two Hardware-Based Square Root Computational Algorithms

Square root computation is a complex but nevertheless a widely used arithmetic operation. The complexity arises from the irrational behavior of the non-perfect squares and the iterative nature of its calculation. Traditionally, systems have relied on the software for these computations. However, with the concept of integration of signal processing and VLSI technology, hardware implementation of the square root computation algorithms derive attention. This paper focuses on two such algorithms, one is the Newton Raphson Inverse method and the other, Non Restoring method. A detailed description of the algorithms and the comparative results from their hardware synthesis have been presented.
Target Tracking Algorithm of Similarity Detection Based on Correlation Filtering

Tracking-Learning-Detection (TLD) algorithm is a better method in the study of a continuous single target tracking algorithm. However, the optical flow method used in TLD is an ideal model based on three assumptions, errors are prone to occur in the change of object scale, and the errors will accumulate gradually. In view of the limitations of TLD tracker, this paper proposes a fusion algorithm of Sale Adaptive with Multiple Features (SAMF) tracker and Kernelized Correlation Filter (KCF) tracker. Similarity is calculated by tracking results, and the detection module switching is judged by similarity. The target tracking frame is adjusted by combining the two results. The fusion algorithm can stably and well output tracking results in complex situations such as blur, fast moving, object occlusion, illumination change and has good robustness, which is suitable for long-term target tracking.
Research and Application of Mineral Prediction Method Based on Cluster Analysis and Factor Analysis—Taking the Gold and Copper Deposits in the East of Yanbian as an Example

Based on the study of the geological survey of the study area, 27 geological variables are extracted, and then the geological problems are transformed into mathematical problems. Cluster analysis and factor analysis models are established to classify the four ore-forming grades of A, B, C and D. Suggestions are put forward for the strategic deployment of prospecting for gold and copper deposits in the study area in the future.
A Fast and Efficient Vehicle Multi-target Tracking Algorithm for Edge Computing Devices

A multiple vehicle target tracking algorithm is proposed to solve the problem of data association and target loss in vehicle multi-target tracking. Firstly, Tiny-Yolov4 can be used to get the detection results. Then, gain intersection over union distance metric was introduced, which measures the similarity of the target frame information of the previous and subsequent frames for target data association. Finally, for the problem of target loss, a re-tracking method was proposed which take a feature expression method that combines multi-scale color features and multi-scale gradient features into consideration, the similarity between missing vehicle and newly detected could be measured by cosine distance. Compared with Sort, DeepSort, FairMot and other algorithms, the experimental results show that the algorithm in this paper can improve the accuracy of small target data association and solve the problem of tracking target loss. Testing on NVIDIA Jetson TX2, the average tracking time is only 7.66 ms.
Towards Discriminative Visual Search via Semantically Cycle-consistent Hashing Networks

Deep hashing has shown great potentials in large-scale visual similarity search due to preferable storage and computation efficiency. Typically, deep hashing encodes visual features into compact binary codes by preserving representative semantic visual features. Works in this area mainly focus on building the relationship between the visual and objective hash space, while they seldom study the triadic cross-domain semantic knowledge transfer among visual, semantic and hashing spaces, leading to serious semantic ignorance problem during space transformation. In this paper, we propose a novel deep tripartite semantically interactive hashing framework, dubbed Semantically Cycle-consistent Hashing Networks (SCHN), for discriminative hash code learning. Particularly, we construct a flexible semantic space and a transitive latent space, in conjunction with the visual space, to jointly deduce the privileged discriminative hash space. Specifically, a semantic space is conceived to strengthen the flexibility and completeness of categories in feature inference. Moreover, a transitive latent space is formulated to explore the shared semantic interactivity embedded in visual and semantic features. Our SCHN, for the first time, establishes the cyclic principle of deep semantic-preserving hashing by adaptive semantic parsing across different spaces in visual similarity search. In addition, the entire learning framework is jointly optimized in an end-to-end manner. Extensive experiments performed on diverse large-scale datasets evidence the superiority of our method against other state-of-the-art deep hashing algorithms.
A Study of Feeding Robot Based on RFID and Ultrasonic Obstacle Avoidance

According to the environmental characteristics of the printing workshop, the free path planning and autonomous obstacle avoidance of the paper feeding robot in the printing workshop were studied by using RFID technology and ultrasonic sensor technology, the path planning system, obstacle avoidance system, control system and driving system are designed and developed, and the experimental prototype is made. The robot's self-guidance and automatic obstacle avoidance are simulated in the laboratory environment. The results show that in the environment as printing workshop with the stations are relatively fixed but surroundings are complex and changeable, paper conveying robot has the advantages of flexible path change and low cost, not easy to be affected by environment and so on. This method provides a feasible reference for robot self-guidance and obstacle avoidance in dynamic environment, and has a broad application in prospect.
Improving Storage Efficiency with Multi-cluster Deduplication and Achieving High-Data Availability in Cloud

The Cloud service providers (CSP) such as iCloud, Google Drive, Mega and others adopt secure data deduplication to remove redundancies of encrypted data. Earlier research in deduplication of encrypted files resulted in an idea of converging encryption, which generates identical cipher texts from identical plain text. While the use of converging encryption does not address security challenges since users can still determine the occurrence of deduplication. Few users access the data from the Cloud for a particular time period and later they do not need the data. In that case the user is charged for the unused data, which leads to wastage of storage space and cost for keeping unused data. To overcome the above limitations, in this paper, a multi cluster deduplication techniques is proposed. The proposed work use the erasure coding technique to achieve high data availability in the cloud, by keeping the encrypted files in multiple servers. The cloud server minimizes the storage space by deleting the unused files from the server. The implementation of RC4 (Rivest Cipher 4) symmetric encryption technique reduces the cipher text size over AES (Advanced Encryption Standard) and RC4 consumes less time to encrypt the data compare to AES. The performance analysis shows that the proposed scheme improves system efficiency by reducing storage space, minimizing storage cost, reduce the file uploading and downloading time and maintain data integrity and data security as compared to the existing scheme.
Further results on higher weights of codes over finite rings

The descriptions of generalized Hamming weights with respect to rank are given for codes over chain rings. Based on the descriptions of generalized Hamming weights with respect to rank, the double chain condition, in particular, the chain condition, is introduced and some judging criteria for the double chain condition are presented. As an application of the chain condition, we determine generalized Hamming weights with respect to rank of the tensor product of certain codes satisfying the chain condition. By using generalized Hamming weights with respect to rank, we generalize some results obtained in recent references. We introduce relative generalized Hamming weights to codes over chain rings and principal ideal rings, and present equivalent descriptions and bounds on them. We also generalize maximum distance codes with respect to rank to relative maximum distance codes with respect to rank and give a series of judging criteria for relative maximum distance codes.