Reviewerly/exHarmony huggingface dataset:
ESJF Algorithm to Improve Cloud Environment

Nowadays, after the technological development in societies, cloud computing has become one of the most important technologies. It provides users with software, hardware, and platform as remote services over the Internet. The increasing number of cloud users has caused a critical problem in how the clients receive cloud services when the cloud is in a state of instability, as it cannot provide required services and, thus, a delay occurs. Therefore, an algorithm was proposed to provide high efficiency and stability to work, because all existing tasks must operate without delay. The proposed system is an enhancement shortest job first algorithm (ESJF) using a time slice, which works by taking a task in the shortest time first and then the longest first from the queue. Through the experimental results in decreasing the waiting and completion time of the task, as well as taking into account reducing tasks starvation, the result of the proposed ESJF algorithm was compared with the traditional shortest job first (SJF) algorithm. These algorithms were applied when all tasks arrived at the same time, and it proved that the ESJF algorithm works more efficiently compared to SJF.
Modern Educational Technologies For Ensuring Strategic Academic Leadership

The study is aimed at developing the conceptual foundations of digitalization of education: it is impossible to achieve academic leadership by Russian universities without an effective digital transformation of the domestic higher education system.The results of the study contribute to the theory of digital didactics of higher education, which can not only form digital competencies of participants in the educational process, but also, in combination with other pedagogical technologies, become a promising direction for the development of modern higher education.Today, the educational process of universities requires a new understanding and development of adequate approaches to transformation due to a number of objective reasons and global trends: dissonance between the growing demands on the part of customers of educational services and the interests of universities; low levels of digital literacy and digital skills among educators; ineffective use of individual student reserves; insufficient level of development of didactics and infrastructure of digital education in universities.All this predetermines the need to identify and develop a new flexible interactive digital education system using various smart technologies and smart devices, which allow not only to increase the amount of educational information and enhance the individualization of the educational process, but also to diversify the ways of interactive interaction of subjects of the educational process to increase its effectiveness.
Vision-based Excavator Activity Analysis and Safety Monitoring System

In this paper, we propose an excavator activity analysis and safety monitoring system, leveraging recent advancements in deep learning and computer vision. Our proposed system detects the surrounding environment and the excavators while estimating the poses and actions of the excavators. Compared to previous systems, our method achieves higher accuracy in object detection, pose estimation, and action recognition tasks. In addition, we build an excavator dataset using the Autonomous Excavator System (AES) on the waste disposal recycle scene to demonstrate the effectiveness of our system. We also evaluate our method on a benchmark construction dataset. The experimental results show that the proposed action recognition approach outperforms the state-of-the-art approaches on top-1 accuracy by about 5.18%.
Adolescent Perspectives About Online Hate Speech: Qualitative Analysis in the SELMA Project

This paper aims to study the views, perceptions and representations of online hate speech among adolescents in the Greek cohort of the SELMA Project.Qualitative research was conducted in focus groups of 36 Greek adolescents and the data were processed through thematic analysis method.The majority was unfamiliar with the term "hate speech" and confused it with cyberbullying. The target characteristics of hate, ethnicity, race, gender, religion, physical weakness, disability, sexual orientation, and appearance emerged. Regarding people involved in hate speech, perpetrators in both hate speech and bullying were described to share common characteristics. The emphasis was placed on the victims' resilience, as well as their socialization, as protective behaviors. Participants stressed the value of the right to freedom of speech, although there was no agreement on its limits. Additionally, it was highlighted that awareness of what is right and wrong is mostly taught by parents, while the role of education was also important. An important finding was that the majority of teenagers were optimistic, supporting the belief that it is possible to find a realistic solution.The findings support the need for prevention strategies in the school environment, so that adolescents will be able to recognize and potentially combat hate speech in the online and offline worlds.
Deep Learning Technology in Pathological Image Analysis of Breast Tissue

To explore the application value of the multilevel pyramid convolutional neural network (MPCNN) model based on convolutional neural network (CNN) in breast histopathology image analysis, in this study, based on CNN algorithm and softmax classifier (SMC), a sparse autoencoder (SAE) is introduced to optimize it. The sliding window method is used to identify cells, and the CNN + SMC pathological image cell detection method is established. Furthermore, the local region active contour (LRAC) is introduced to optimize it and the LRAC fine segmentation model driven by local Gaussian distribution is established. On this basis, the sparse automatic encoder is further introduced to optimize it and the MPCNN model is established. The proposed algorithm is evaluated on the pathological image data set. The results showed that the Acc value, F value, and Re value of pathological cell detection of CNN + SMC algorithm were significantly higher than those of the other two algorithms (P < 0.05). The Dice, OL, Sen, and Spe values of pathological image regional segmentation of CNN algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant (P < 0.05). The accuracy, recall, and F-measure of the optimized CNN algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively. In the two databases with segmentation standards, the segmentation accuracy of MPCNN is 55%, 73.1%, 78.8%, and 82.1%. In the deep convolution network model, the training time of the MPCNN algorithm is about 80 min. It shows that when the feature dimension is low, the feature map extracted by MPCNN is more effective than the traditional feature extraction method.
Features of the introduction of innovative technologies in the professional training of Teachers

The main purpose is to determine the main aspects and features of the implementation of innovations in the professional training of teachers. Now all over the modern world standards, technologies of vocational education and training of specialists are being revised, updated. Indeed, the reform of professional pedagogical education requires the approval of fundamental pedagogical education, the harmonization of methodological, didactic and psychological knowledge, which will allow a specialist to fully realize the humanitarian, cultural functions of education, master innovative technologies of teaching and upbringing. The formation of a new education provides for natural processes of the development of pedagogical practice, a purposeful control effect on the training system, retraining of pedagogical personnel, significant adjustments to the content, style of activity of pedagogical institutions, future teachers, that is, the transition of the educational system to functioning on a new basis. The current state of the training of future teachers in higher educational institutions dictates the need to look for new ways to improve the quality of their theoretical training, the ability to independent creative work, and professional self-development. The methodological training of future teachers should be based on modern teaching technologies, which graduates should have flawlessly. Based on the results of the study, the key features of the implementation of innovations in the professional training of teachers were identified.
Implementation of Apriori Data Mining Algorithm on Medical Device Inventory System

The pattern of the need for drugs and medical devices in various hospitals has a tendency to be repeated and similar in a relatively long period of time, especially in one particular department, because the cases found are often similar or even similar. Ensuring the availability of stock in each departmental depot is very vital, because the procurement of medical devices must go through a certain process and time, so that cases of critical rheumatism often occur but the equipment needed at depositors does not meet the standards. need or run from inventory and must indent first. By calculating the trend of demand patterns and needs using an algorithm (Apriori Association) in the dataset, a rule is formed that in the pattern of dependence between itemsets that have supporting criteria in the form of 33.3% support and 85% Confidence, where the items that appear are items with frequency of occurrence and associations so that it can be taken into consideration to ensure the availability of drugs and medical devices.
Tomographic entanglement indicators in frequency combs and Talbot carpets

Abstract Recent theoretical investigations on tomographic entanglement indicators have showcased the advantages of the tomographic approach in the context of continuous-variable (CV), spin and hybrid quantum systems. Direct estimation of entanglement using experimental data from the IBM quantum computing platform and NMR experiments has also been carried out in earlier work. A similar investigation in the context of CV systems is necessary to assess fully the utility of the tomographic approach. In this paper, we highlight the advantages of this approach in the context of experiments reported in the literature on two CV systems, namely, entangled Talbot carpets and entangled biphoton frequency combs. We use the tomographic entanglement indicator to estimate the degree of entanglement between a pair of Talbot carpets, and demonstrate that this provides a simpler and more direct procedure as compared to the one suggested in the experiment. We also establish that the tomograms corresponding to two biphoton frequency combs carry clear entanglement signatures that distinguish between the two states.
Three-Dimensional Reconstruction of Cerebrovascular and Algorithm Realization

In the three-dimensional reconstruction of CT cerebrovascular medical image registration, a new optimization algorithm based on the relative position information between the contours of various blood vessels in the image is proposed.Using the rule that the center of gravity of the vascular tissue structure on the series of slices has continuity, find the registration relationship between the contours of the vessels in the two adjacent slices. Because the shape of cerebrovascular contour is relatively symmetrical, its center of gravity is slightly away from its geometric center. Therefore, the geometric center is used to replace the center of gravity, and the "mass" of each contour is calculated according to the area of each contour to achieve the registration of the blood vessel contour.The method has the characteristics of global optimization and stronger robustness.The cerebrovascular image obtained by this method is more realistic and can be used for the import of various software, simulation training, and later research, which provides an effective method for preoperative simulation of cerebrovascular intervention surgery.
Grammatical Error Analysis of English Abstracts Translation in Jurnal Ilmiah Ekonomi Islam (JIEI) Journal

Scientific writing like abstract demands perfect grammar writing in academic language. A common error usually is the use of an automated translator such as Google Translate to translate abstracts from Indonesian to English. The translation produced needs to be polished again to avoid mistakes in the grammar itself. The objective of this research is to find the type of grammatical error in English abstracts published in Jurnal Ilmiah Ekonomi Islam (JIEI) journal in 2020 both English and Indonesian. This research uses descriptive qualitative. The result of the study shows that three types of grammatical errors found in English abstracts in the JIEI journal are misinformation, omission, and addition. In misinformation error, there are 98 data or 44.5 %. In omission error, there are 69 data or 31.36 %. In addition to error, there are 53 data or 24.09 %. In misinformation, several errors were found in the use of part of speech such as article/determiner, the preposition, verb (subject-verb agreement), noun (singular-plural), and pronoun. In omission, several errors were found in the omission of part of speech such as article ‘a, an, the’, preposition ‘on’ and ‘to’. In addition, several errors were found in the addition part of speech such as article/determiner ‘a, an, the”, preposition “of, to, in, on, at”. Preposition sometimes is not used with certain verbs and expressions. The most dominant type of grammatical error is misinformation. There is the misuse of grammar forms into other grammatical forms in the use of part of speech such as in the determiner/article, preposition, verb, noun, and pronoun.
A Mask-RCNN based object detection and captioning framework for industrial videos

In today's world, automation and automated systems are growing at a tremendous rate.A huge effort is applied for creating systems and programs that eliminate the need for human effort in day-to-day working.The use of robots in factories to assemble machinery [1], to perform various tasks is not new.However, for labor-intensive industries, that create employment for the masses, it is important to generate reports, find the material availability, perform productivity analysis, etc.Moreover, in any business, it is important to optimize the working of its systems for maximum profit based on minimum investment.To achieve this, productivity analysis is an essential step in any business model.
Concurrent Video Denoising and Deblurring for Dynamic Scenes

Dynamic scene video deblurring is a challenging task due to the spatially variant blur inflicted by independently moving objects and camera shakes. Recent deep learning works bypass the ill-posedness of explicitly deriving the blur kernel by learning pixel-to-pixel mappings, which is commonly enhanced by larger region awareness. This is a difficult yet simplified scenario because noise is neglected when it is omnipresent in a wide spectrum of video processing applications. Despite its relevance, the problem of concurrent noise and dynamic blur has not yet been addressed in the deep learning literature. To this end, we analyze existing state-of-the-art deblurring methods and encounter their limitations in handling non-uniform blur under strong noise conditions. Thereafter, we propose a first-to-date work that addresses blur- and noise-free frame recovery by casting the restoration problem into a multi-task learning framework. Our contribution is threefold: a) We propose R2-D4, a multi-scale encoder architecture attached to two cascaded decoders performing the restoration task in two steps. b) We design multi-scale residual dense modules, bolstered by our modulated efficient channel attention, to enhance the encoder representations via augmenting deformable convolutions to capture longer-range and object-specific context that assists blur kernel estimation under strong noise. c) We perform extensive experiments and evaluate state-of-the-art approaches on a publicly available dataset under different noise levels. The proposed method performs favorably under all noise levels while retaining a reasonably low computational and memory footprint.
Kernelized Lagrangian particle tracking

In this article, we present a novel Lagrangian particle tracking method derived from the perspective of the tracking-by-detection paradigm that has been adopted by many vision tracking tasks. Under this paradigm, the particle tracking problem consists of first learning a function (the tracker) that maps the target particle's image projection backwardly to its possible position inferred from its precedent tracking information. The target particle's actual position is then detected by applying the learned function to particle images captured by cameras. We also propose to solve the function learning problem using kernel methods. The proposed method is therefore named Kernelized Lagrangian particle tracking (KLPT). The current state-of-art LPT approach Shake-The-Box (STB), despite equipping a highly efficient image matching and shaking-based optimization procedure, tends to be trapped by local minimum when dealing with challenging cases featuring sparse temporal data, data extracted from complex flows and noisy data. KLPT can overcome these optimization difficulties since it features a highly robust function learning procedure combined with an efficient linear optimization technique. We assessed our proposed KLPT against various STB implementations both on synthetic and real experimental datasets. For the synthetic dataset depicting a turbulent cylinder wake-flow at Re=3900, we focused on studying the effects of particle density, time separation, and image noise. KLPT outperformed STB in all cases by tracking more particles and producing more accurate particle fields. This performance gain, compared to STB, is more prominent for the dataset with larger seeding density, time separation, and more noise. For real experimental data on an impinging jet flow in a water tank, KLPT can capture longer tracks and provides more detailed flow reconstruction at highly turbulent regions than STB. Overall, comparison to STB shows significant improvements in accuracy (lower reconstruction positional error) and robustness (more and longer tracks). We finally show the results of KLPT on the 1st LPT challenge datasets. Our algorithm has achieved state-of-the-art performance with particle images up to 0.08 ppp (particles per pixel).
Comic-Based Learning Media with the Topic of Natural Resources

The limitations of learning media in the learning process are mainly in the content of social studies with the topic of natural resources in grade IV. This study aims to develop a valid comic-based learning media. The development model used is the ADDIE model which has five stages, namely: analysis, planning, development, implementation, and evaluation. Comic-based learning media products were the subject of this study, then the validity of comic-based learning media was the object of research. The data collection method was conducted using a questionnaire method. In measuring the validity of comic-based learning media, a rating scale instrument is used in the form of a media validity assessment sheet filled in by 1 lecturer as a material expert and 1 lecturer as a media expert and 2 teachers as a practitioner expert. The media validity data obtained were then analyzed using the mean formula to determine the average score of the validity of comic-based learning media. The average score obtained by each expert on the material expert is 4.80, the media expert is 4.79, and the practitioner expert is 4.88, the three average scores obtained belong to the "very good" category. Based on this, the comic-based learning media developed on the topic of natural resources is declared valid and can be used in learning in grade IV elementary schools.
Cost-Effective Proxy Signcryption Scheme for Internet of Things

The Internet of things (IoT) has emerged into a revolutionary technology that enables a wide range of features and applications given the proliferation of sensors and actuators embedded in everyday objects, as well as the ubiquitous availability of high-speed Internet. When nearly everything is connected to the Internet, security and privacy concerns will become more significant. Furthermore, owing to the resource-constrained nature of IoT devices, they are unable to perform standard cryptographic computations. As a result, there is a critical need for efficient and secure lightweight cryptographic scheme that can meet the demands of resource-constrained IoT devices. In this study, we propose a lightweight proxy in which a person/party can delegate its signing authority to a proxy agent. Existing proxy signcryption security approaches are computationally costly and rely on RSA, bilinear pairing, and elliptic curves cryptography (ECC). The hyperelliptic curve cryptosystem (HECC), on the other hand, employs a smaller key size while maintaining the same level of security. When assessed using the random oracle model (ROM), the proposed scheme provides resilience against indistinguishable under adaptive chosen ciphertext attacks (IND-CCA) and unforgeable under adaptive chosen message attacks (UU-ACMA). To demonstrate the viability of the proposed scheme, security analyses and comparisons with existing schemes are performed. The findings show that the proposed scheme provides high security while reducing computational and communication costs.
Four Decades Research on Higher Vocational Education: A Bibliometric Review

Vocational education is seen as an integral part of the educational system, either at the school level or at a post-secondary level. It is indicated that vocational education has immensely influenced life and plays a vital role in evolving societies worldwide. Thus, analysing these scientific domains evolution and creation are essential. This study discusses the bibliometric review of scholarly research performed in higher vocational education in recent years. The study uses the Scopus database and a wide variety of bibliometric metrics, including publications, citations, and publishing patterns. A graphical visualisation of bibliographic details using the VOS viewer software is also presented. Results show that the number of studies covering this subject has fluctuated, with the highest number of publications being 65 in 2020. Often, by reviewing publications and research areas, it can be construed that higher vocational education research is diverse, including social sciences, computer science, and engineering. The bibliometric review conducted offers a rigorous and thorough view of higher vocational education research that may benefit higher vocational education practitioners and researchers keen on the pursuit of enhancing or discovering new knowledge in this field.
Provably Secure Authentication Approach for Data Security in Cloud Using Hashing, Encryption, and Chebyshev-Based Authentication

Secure and efficient authentication mechanism becomes a major concern in cloud computing due to the data sharing among cloud server and user through internet. This paper proposed an efficient Hashing, Encryption and Chebyshev HEC-based authentication in order to provide security among data communication. With the formal and the informal security analysis, it has been demonstrated that the proposed HEC-based authentication approach provides data security more efficiently in cloud. The proposed approach amplifies the security issues and ensures the privacy and data security to the cloud user. Moreover, the proposed HEC-based authentication approach makes the system more robust and secured and has been verified with multiple scenarios. However, the proposed authentication approach requires less computational time and memory than the existing authentication techniques. The performance revealed by the proposed HEC-based authentication approach is measured in terms of computation time and memory as 26ms, and 1878bytes for 100Kb data size, respectively.