Reviewerly/exHarmony huggingface dataset:
Enabling Efficient Deduplication and Secure Decentralized Public Auditing for Cloud Storage: A Redactable Blockchain Approach

Public auditing and data deduplication are integral considerations in providing efficient and secure cloud storage services. Nevertheless, the traditional data deduplication models that support public auditing can endure the enormous waste of storage and computation resources induced through data redundancy and repeated audit work by multiple tenants on trusted third-party auditor (TPA). In this work, we introduce blockchain-based secure decentralized public auditing in a decentralized cloud storage with an efficient deduplication model. We employ blockchain to take on the task of centralized TPA, which also mitigates the implications of malicious blockchain miners by using the concept of a decentralized autonomous organization (DAO). Specifically, we employ the idea of redactability for blockchain to handle often neglected security issues that would adversely affect the integrity of stored auditing records on blockchain in decentralized auditing models. However, the proposed model also employs an efficient deduplication scheme to attain adequate storage savings while preserving the users from data loss due to duplicate faking attacks. Moreover, the detailed concrete security analysis demonstrates the computational infeasibility of the proposed model against proof-of-ownership, duplicate faking attack (DFA), collusion attack, storage free-riding attack, data privacy, and forgery attack with high efficiency. Finally, the comprehensive performance analysis shows the scalability and feasibility of the proposed model.
U-Net Architectures for Prostate Cancer Radiation Therapy

Prostate Cancer (PCa) was first diagnosed in 1853 as cirrhosis of the prostate gland. Silently asymptomatic, PCa is usually diagnosed with Digital Rectal Examination (DRE) and Prostate Specific Antigen (PSA) levels. Since the first treatment of an advanced prostatic malignancy with X-rays by Imbert and Imbert in 1904, External Beam Radiation Therapy (EBRT) is now a curative option for localised and locally advanced disease and a palliative option for the metastatic low-volume disease. With the introduction of computers in EBRT and better imaging techniques, volume delineation is still a very time-consuming task. While Deep Learning methods are urged, EBRT systems still rely on manual or semi-automatic segmentation techniques. The U-Net architecture was specially designed for medical image segmentation presenting promising results. This literature review gathers work using U-Net architectures, outlining methods, techniques and obtained outcomes as a potential foundation for an automated segmentation framework for PCa.
A 3D Path Planning Method Based on Improved Artificial Bee Colony Algorithm

Path planning is an important problem for unmanned underwater vehicle (UUV). A fast path planning method based on an improved artificial bee colony algorithm is proposed in this paper. The purpose of this work is to improve the disadvantages of the original ABC algorithm, such as slow convergence speed and low convergence accuracy. This paper greatly enhances the local exploration capability and optimizes the disadvantages of the ABC algorithm by incorporating the dual cognitive ability and weighting factors of individuals into the local search strategy. Based on this, a quick path planning approach based on Bézier curve generated path is introduced, which applies Bézier path to satisfy the path angle control constraint and adjusts the turning radius to satisfy the constraint. In summary, an improved ABC-based Bézier path planning method is proposed. Simulation and comparison experiments are conducted under the conditions of the established underwater environment threat model. The experimental results try to require the path between two points to be the shortest one. Evaluation metrics are established to quantity the effectiveness of the algorithm in terms of the sum of algorithm convergence time and path length. The simulation results show that the improved ABC has a high convergence rate and good local search capability and it does not converge prematurely and can be better used in certain areas in the future.
Dysgraphia detection based on convolutional neural networks and child-robot interaction

Dysgraphia is a disorder of expression with the writing of letters, words, and numbers. Dysgraphia is one of the learning disabilities attributed to the educational sector, which has a strong impact on the academic, motor, and emotional aspects of the individual. The purpose of this study is to identify dysgraphia in children by creating an engaging robot-mediated activity, to collect a new dataset of Latin digits written exclusively by children aged 6 to 12 years. An interactive scenario that explains and demonstrates the steps involved in handwriting digits is created using the verbal and non-verbal behaviors of the social humanoid robot Nao. Therefore, we have collected a dataset that contains 11,347 characters written by 174 participants with and without dysgraphia. And through the advent of deep learning technologies and their success in various fields, we have developed an approach based on these methods. The proposed approach was tested on the generated database. We performed a classification with a convolutional neural network (CNN) to identify dysgraphia in children. The results show that the performance of our model is promising, reaching an accuracy of 91%.
Simulation model of ACO, FLC and PID controller for TCP/AQM wireless networks by using MATLAB/Simulink

&lt;span lang="EN-US"&gt;The current work aims to develop a suitable design for control systems as part of a queue management system using the transmission control protocol/and active queue management (TCP/AQM) protocol to handle the expected congestion in the network. The research also aims to make a comparison between the different control methods, including the traditional proportional integral derivative (PID) and the expert fuzzy logic control (FLC), as well as the optimal ant colony optimization (ACO) that is used according to the performance improvement criteria to reach the best values for parameters the traditional controller (kd, ki, k p), where the addition of the performance indicator time-weighted absolute error (ITAE) was adopted. The use of this method without any other optimization algorithm that can be applied to adjust the parameters of the PID to verify the possibility of improving performance and enhance that with experience and to know the level of improvement for this particular system being the subject of the study. The results showed the superiority of the optimal ACO over both the FLC expert and the conventional PID, as well as the superiority of the FLC expert over the traditional PID.&lt;/span&gt;
Single patch super-resolution of histopathology whole slide images: a comparative study

PurposeThe latest generation of scanners can digitize histopathology glass slides for computerized image analysis. These images contain valuable information for diagnostic and prognostic purposes. Consequently, the availability of high digital magnifications like 20 × and 40 × is commonly expected in scanning the slides. Thus, the image acquisition typically generates gigapixel high-resolution images, times as large as 100,000 × 100,000 pixels. Naturally, the storage and processing of such huge files may be subject to severe computational bottlenecks. As a result, the need for techniques that can operate on lower magnification levels but produce results on par with outcomes for high magnification levels is becoming urgent.ApproachOver the past decade, the digital solution of enhancing images resolution has been addressed by the concept of super resolution (SR). In addition, deep learning has offered state-of-the-art results for increasing the image resolution after acquisition. In this study, multiple deep learning networks designed for image SR are trained and assessed for the histopathology domain.ResultsWe report quantitative and qualitative comparisons of the results using publicly available cancer images to shed light on the benefits and challenges of deep learning for extrapolating image resolution in histopathology. Three pathologists evaluated the results to assess the quality and diagnostic value of generated SR images.ConclusionsPixel-level information, including structures and textures in histopathology images, are learnable by deep networks; hence improving the resolution quantity of scanned slides is possible by training appropriate networks. Different SR networks may perform best for various cancer sites and subtypes.
Justifying convolutional neural network with argumentation for explainability

Convolutional neural network (CNN) has emerged as one of the most accurate methods for sentiment analysis, but it is largely uninterpretable, while case-based reasoning (CBR) is less accurate but o ff ers interpretable outputs in the form of arguments from analogy. This paper presents an approach to combine these two methods, CNN for accuracy and CBR for explainability, using an assumption-based argumentation (ABA) framework. Our approach focuses on justifying CNN outputs using analogous sentences from CBR, while ensuring that the combined process is argumentative and hence self-explainable. To demonstrate the proposal, we construct a CNN model M 1 and a CBR model M 2 for sentiment analysis using di ff erent subsets of a dataset of which the remaining part is used for testing and comparing these input models with combined models. For an input sentence, if M 1 and M 2 predict the same sentiment, then the analogous sentence, which M 2 finds, is used to explain the sentiment. If they give conflicting sentiments, a hybrid model M 3 determines which one should be followed using a system of strict rules that takes into account how assertive M 1 and M 2 are. Another hybrid model M 4 , which is implemented by an ABA framework, improves on M 3 by considering the probability distribution of the set of all labels from M 1 , and the second (or third) most similar sentences from M 2 . M 3 and M 4 preserve the accuracy of the CNN model (specifically, 88.32% and 88.28% in comparison with 87.59% accuracy of the CNN). They justify 69.95% and 74.53% of CNN outputs, respectively.
An Efficient Association Rule Mining-Based Spatial Keyword Index

Spatial keyword query has attracted the attention of many researchers. Most of the existing spatial keyword indexes do not consider the differences in keyword distribution, so their efficiencies are not high when data are skewed. To this end, this paper proposes a novel association rule mining based spatial keyword index, ARM-SQ, whose inverted lists are materialized by the frequent item sets mined by association rules; thus, intersections of long lists can be avoided. To prevent excessive space costs caused by materialization, a depth-based materialization strategy is introduced, which maintains a good balance between query and space costs. To select the right frequent item sets for answering a query, the authors further implement a benefit-based greedy frequent item set selection algorithm, BGF-Selection. The experimental results show that this algorithm significantly outperforms the existing algorithms, and its efficiency can be an order of magnitude higher than SFC-Quad.
MVPN: A Defense Architecture against VPN Traffic Hijacking Based on MTD

With the increasing awareness of privacy protection, Virtual Private Networks (VPNs) are widely used to build a more secure communication tunnel. However, a traffic hijacking attack called blind in/on-path has seriously threatened the security of VPNs. Inspired by Moving Target Defense (MTD), Moving VPN architecture (MVPN) is designed to defend against such attacks. MVPN includes multiple nodes to encrypt and decrypt traffic to enhance reliability. Thus, the consistency judgment algorithm is proposed to make MVPN obtain the ability to perceive attacks. Moreover, according to the judgment result and the state update strategy, the MVPN state is dynamically changed so as to achieve the purpose of active defense. In addition, this paper also designs the multichannel packet classification mechanism and availability assurance strategy, which not only ensures the security and availability of the system but also reduces the performance loss caused by the defense strategy. The simulation verifies that MVPN architecture can reduce the success rate of blind in/on-path attacks by five orders of magnitude. In addition, we implemented and deployed MVPN based on the fast-forwarding framework of the Data Plane Development Kit (DPDK). Experiments in the real environment also show that the MVPN system can effectively prevent attackers from carrying out blind in/on-path attacks.
Synchronous restoration of video key frame loss based on digital media communication protocol

Abstract This paper presents a synchronous recovery method for video key frame loss, aiming to analyze the experimental video through digital media feature extraction algorithm. By analyzing and designing effective communication protocols in real-time embedded systems, video data can be better processed. On this basis, key frames are restore synchronously through digital media communication protocol, and verified by comparing with other algorithms. Experimental results shows that the recall and precision of this algorithm for key frame extraction are 90.1% and 100% respectively, Among the three algorithms compared, the recall and precision of video key frame extraction based on single feature algorithm are the highest, 80.2% and 85.9% respectively. At the same time, the synchronization restoration time of this algorithm for lost key frames is 12.2 s, which took less time than other algorithms. It can be seen that the algorithm based on digital media feature extraction is of great significance for synchronous restoration of video key frame loss. It can be seen that the algorithm based on digital media feature extraction is of great significance for the synchronous recovery of video key frame loss, and can effectively promote the development of video data diversity and the improvement of information interaction. Under the digital media communication protocol, it can effectively synchronize the audio and video receiving end of multimedia information, and then recover and predict the lost frames in the video sequence. First, it can enhance the internal characteristics of video frames and the similarity and consistency of images between frames. Secondly, it can significantly improve the accuracy and efficiency of video data processing. Finally, from the perspective of practical development, the synchronous recovery of video key frame loss based on digital media communication protocol can effectively promote the sustainable development of media data, which has certain social value and practical significance in the current era of big data.
IoT Technology for Intelligent Management of Energy, Equipment and Security in Smart House

The Internet of Things means that many of the daily devices used by humans will share their functions and information with each other or with humans by connecting to the Internet.The most important factor of the Internet of Things is the integration of several technologies and communication solutions.Identification and tracking technologies, wired and wireless sensors and active networks, protocols for increasing communication and intelligence of objects are the most important parts of the Internet of Things.In this article, an attempt has been made to determine the parts that can be used to make a house smart among the concepts and technologies related to web-based programs based on Internet of Things technology.Since it is very time-consuming to investigate the effect of all the Internet of Things technologies in smart homes, by studying and examining various types of research, the web-based program based on the Internet of Things is selected as an independent variable, and its effect on smart home management is investigated.For this purpose, a web-based program based on the Internet of Things for intelligent building energy management, intelligent equipment management, and intelligent security has been designed and implemented.As experimental results shown the proposed method the proposed method achieves better results compared to other existing methods in energy consumption by 33.8% reducing energy usage.
Face Mask and Helmet Facial Detection for ATM Security Using Image Processing in Cloud Computing

As we have seen in lots of newspaper, news and many magazines that robbery became the common and main issue going on over the world. My biggest concern is on secure ATM transaction, where nowadays many ATM’s get misleaded and robbery are occurring very easily. So, to handle this situation Image processing has been playing a major role to identify the person who are doing the transaction via ATM. Because has the covid cases are increasing many people wear mask and helmet and do misuse the ATM for illegal transaction, here where the image processing helps to differentiate the person wearing mask/helmet and without mask/helmet for the further ATM transaction. And secondly Cloud is also playing a major in storing the datas where it stores the static data to train the model and to detect the accuracy and stores the captured real time image in different folder so that we can easily identify the person with mask/helmet and without mask/helmet.
Frequent linguistic errors in the writing of Yemeni EFL Arabic-speaking learners

The Yemeni EFL learners are prone to share their knowledge and views regarding what and how to say. The constraints of combining this expertise have hampered learners’ writing success. Those obstacles can cause learners to make errors. Error Analysis (EA) and Surface Strategy Taxonomy (SST) were used to analyze learners’ linguistic errors. Error causes were also investigated. This research used a qualitative process style to use a case study approach. Ellis’ five-step EA procedure was followed to analyze essay data each comprising 100-350 words or more written by 20 Yemeni EFL eighth semester Arabic-speaking learners at the Department of Education, Sana’a University, Yemen. They were purposely selected as research subjects. It was noticed that omission was the most common error detected in the learners’ writings. Overall, this form of error accounted for 58.71% of 118 cases out of 201 cases. The learners’ common error categories were the number marker, verb-tenses articles, prepositions, subject-verb agreements, and pronouns. This was preceded by addition (20.39%), incorrect formation (15.92%), and word order (4.97%). Intralingual transfer turned out to be the key reason that caused the errors in the learners’ writing. Any of the interlinguistic comparisons was the cause behind the errors. In terms of verb conjugation component, inflectional morpheme, and auxiliary verb abandonment, Arabic and English have different formal definitions. Interlingual transfer and learning context also caused errors. To prevent errors from fossilizing, language instructors should provide continual corrective feedback, and learners should pursue the correct target language form.
PEMETAAN PUBLIKASI ILMIAH TENTANG PERPUSTAKAAN DIGITAL TAHUN 2011-2021 MELALUI APLIKASI VOSViewer (MAPPING SCIENTIFIC PUBLICATIONS ABOUT DIGITAL LIBRARY 2011-2021 THROUGH VOSViewer APPLICATION)

Abstract. A digital library is a library that already has a collection in digital form, and can serve its users boldly without having to come directly to the library building. The digital library is a technological innovation to make it easier for users to meet their needs. This study aims to determine the trend of scientific publications about digital libraries in Indonesia based on keywords that often appear, the authors who discuss this topic, and how many scientific publications have been published with the keywords digital library in the 2011-2021 timeframe. The method used is a quantitative descriptive method with a bibliometric approach. Scientific publication data is taken from the Google Scholar database using the Harzing's Publish or Perish application by entering the keyword “digital library” then the data is processed in the VOSviewer application. The results of this study found that the co-occurrence that appears is more words related to technology. In addition, the results are also divided into 7 clusters of keywords that appear most often in each cluster. For the most prolific authors, there are 18 top authors with at least 4 scientific publications and the most prolific writer is Rodin, R. with 11 scientific publications. The conclusion of this study is that the trend of scientific publications regarding digital libraries has experienced positive growth along with the development of technology with the appearance of the VOSviewer visualization.Keyword: knowledge mapping, bibliometrics, VOSviewer, digital library.
Virtual Teamwork in Gamified 3D Environment

Virtual teamwork has spread strongly in various fields in recent times. COVID lockdown measures radically boosted the use of distance-independent remote collaboration methods. Although there are many modern virtual 3D spaces available for teamwork today, they lack additional motivational factors other than the visual 3d experience. Based on this, we tried to research the possible benefits of using gamification technics in 3D virtual collaboration environments to increase virtual team members' motivation. This article attempts to explore and highlight the possibilities of this area, and propose a model framework for implementation of gamified elements in 3D virtual teamwork environments.
Maximization of Solar Radiation for Fixed and Tracking Surfaces in Antalya Province of Türkiye

Solar energy has gained increasing importance in today's world and become a viable primary energy source in the recent decade. Solar radiation obtained by the solar surface is highly affected by its orientation, azimuth, and tilt angles. Therefore, in this study, the performances of the fixed-axis system, one-axis, and two-axis solar tracking systems are investigated to enable maximal solar radiation in solar systems to be installed in Antalya by using climatic and latitude data provided by NASA. Furthermore, the optimal tilt angles are determined by examining the values of angles for which the total solar radiation falling on the tilted surface is maximal. The case study and measurement data investigations are conducted for the four districts of Antalya. The obtained radiation values throughout the year for one-axis and two-axis solar tracking systems are compared to an annual fixed system for evaluating the existing solar potential in Antalya province. Besides all these, solar system cost analyses including the average payback period for residential, commercial, and large-scale solar systems based on LCOE are investigated. The proposed methodology can be implemented for performance and cost analysis of the solar potential in a certain location of Türkiye and extended to any place in the world.