Reviewerly/exHarmony huggingface dataset:
Hybrid Optimization EHO-GA for Task scheduling in Cloud Environments

Cloud computing is an emerging technology with highly scalable service adopted by different kinds of people from around the world. In cloud environments one of the major problems is task scheduling; most of existing algorithm is not optimal. The proposed hybrid optimization method has combination of Elephant Herd Optimization (EHO) and Genetic Algorithm (GA) for find an optimal resource to schedule task in the Cloud. This proposed method has improves the performance of task scheduling by considering the parameters of response time, makespan, and cost of the cloud. The proposed method has implemented in CloudSim 3.0 toolkit and evaluated the performance with existing algorithm. The Experimental results were proven that proposed algorithm has given better performance compared to other scheduling algorithm.
Examination of Information System Design for Student’s Scoring Processing

The development of information technology is needed in the education system to deal with various problems that arise in managing and solving problems that exist in educational institutions, especially in SMP PGRI 1 Semaka. The design of information processing systems for students uses web technology that can be used by teachers and employees to process student grades quickly and accurately and produce effective and correct information. In designing this information system using an SDLC (Software Development Life Cycle) method. Whereas the programming language used uses PHP and Mysql as its database, xampp as a web server, and Mozilla Firefox as its web browser.
Introduction

The standard model of particle physics, put forward almost 50 years ago, has been so successful at explaining observations that there are few experimental hints, at least from traditional sources such as accelerator centers, that anything more is needed. This situation could still change if something beyond the standard mode emerges from the Large Hadron Collider at CERN; it is, after all, unlikely that the standard model is the end of the story. The Higgs sector seems a little unnatural, among other things. There is also a strong CP problem, there are many free parameters, there is no explanation of dark matter (let alone dark energy) or three fermion generations, and the theory does not accommodate gravity. A minor adjustment is also required to allow for neutrino masses. On the other hand, given this adjustment, there is little reason to doubt the validity of the standard model as a description of all the known elementary particles, and their interactions, down to a distance scale of at least 10−15 cm, and there is good reason to admire a theory which can describe such a wide range of phenomena so economically.
A Robust Algorithm for Real Time Tracking With Optical Flow

Object tracking is a troublesome undertaking and significant extent in data processor perception and image handling community. Some of the applications are protection surveillance, traffic monitoring on roads, offense detection and medical imaging. In this paper a recent technique for tracking of moving object is intended. Optical flow information authorizes us to know the displacement and speed of objects personate in a scene. Apply optical flow to the image gives flow vectors of the points to distinguishing the moving aspects. Optical flow is accomplished by Lucas canade algorithm. This algorithm is superior to other algorithms. The outcomes reveals that the intend algorithm is efficient and accurate object tracking method. This paper depicts a smoothing algorithm to track the moving object of both single and multiple objects in real time. The main issue of high computational time is greatly reduced in this proposed work.
CSPM- Cloud Security Posture Management (Comprehensive Security for Cloud Environment)

As enterprises place more services in public cloud and as the public cloud providers introduce more infrastructure and platform services directly into the hands of developers, it is becoming increasingly complex and time-consuming to answer the seemingly straightforward question “Are we using these services securely?” and “Does the configuration of my cloud services represent excessive risk?” For example, manually assessing the secure setup and configuration in cloud environments across different services, each with varying granularities of authorization policies, is extremely difficult, if not impossible. Simple misconfiguration issues (such as open storage buckets) represent significant risk (see “Open File Shares Are Your Biggest Cloud Security Problem”) and occur often, as evidenced by continuing publicized data disclosures from publicly exposed storage buckets.
What Is the Internet of Things?

Much has been written about the Internet of Things (hence IoT). Some sources are more about promoting IoT as their next big feature; other sources seem to suggest IoT is something everyone needs to learn or be left behind. Fortunately, books and similar media avoid the sales pitch to expand on the science and technology for implementing and managing the data for IoT, while other texts concentrate on the future or the inevitable evolution of our society as we become more connected to the world around us each and every day. However, you need not dive into such tomes or be able to recite rhetoric to get started with the IoT. In fact, through the efforts of many companies, including Microsoft, you can explore the IoT without intensive training or expensive hardware and software.
Wearables

Computing devices have evolved from machines that fill a room, to portable and mobile devices, and now to devices that are worn on our own bodies. Wearable computing devices provide new opportunities for supporting individuals with disabilities via environmental context sensing and ubiquitous input and output. However, designing wearable computing devices to support people with disabilities requires careful design of features, the wearable device itself, and the input and output methods used. This chapter offers an overview of contemporary trends in wearable computing devices for people with disabilities.
Use of Blockchain Technology in Enterprise Management

The Blockchain is an emerging technology and is gaining the popularity over worldwide. This technology is not owned by anyone but used by everyone. The Blockchain technology can be applied in the fields for the ease of operations in business, finance, networking, law etc. This paper aims to make offerings to theory, practical and practice in blockchain technology. A framework focusses on the use of blockchain technology from entry to exit of any process in the system. This is represented in terms of use case discussions and evaluation. This gives an opportunity to use the by Blockchain and to increase trust, mitigate the risk while doing the transaction (Markovitch &amp; Willmott, 2014). This paper presents the detailed understanding and identification of flow of any enterprise. It also covers the identification of important areas where the technology as Blockchain can be applied and implemented. Finally, the blueprint was discussed keeping the view of important areas of implementation of Blockchain technology. This aims to help the enterprise to automate and digitalize their current processes used by an organisation.
Evaluation of learners' online learning behaviour based on the analytic hierarchy process

The online learning behaviour has become an important factor in predicting the learning achievement. Evaluating online learning behaviour is one of the hot topics in the field of IT education, but does not carry out specific weights and score distribution. Therefore, the online learning behaviour data from the 'Moso Teach' cloud platform was analysed by correlation analysis, cluster analysis and analytic hierarchy process analysis. We found that problem-solving behaviour of learners is the least, which is not common in online learning behaviour, while social interaction behaviour is better than problem-solving behaviour, and resource learning behaviour is the most common in online learning. The score distribution diagram of resource learning behaviour shows an inverse s-shaped curve, while the curves of the score distribution diagram of social interaction behaviour and problem solving behaviour are close to a straight line. There are learning achievement differences in resource learning behaviour and problem-solving behaviour, and core-marginal differences in resource learning behaviour and social interaction behaviour.
Preventing the bitcoin Double Spend using Transaction Hash and Unspent Transaction Output

The red-hot crypto currency is a bitcoin which occupies first position in the capital investment of financial world which is assaulted by various factors like wallet attacks, network attacks, mining attacks and double spending attacks. Double spending is the major attack in which the attacker tries to cheat the network nodes and use the same coin for more than one set of transactions. Of this the original transaction identification from the set of transactions is a challenging one. In this paper we propose a solution for identifying the primary transaction from the set of double spended or multi spended transactions. The proposed approach finds the authentic transaction from the list of double spended transactions using transaction hash value, which is primarily used for every transaction in the Bitcoin network. Transaction hash value is used as transaction identifier for each bitcoin transaction. By comparing the transaction hash value with the existing pool of unconfirmed input pool, transhash pool and utxopool one can identify the genuine transaction from the flawed transaction list. The firsthand transaction is then added to the Confirmed input pool which is then entered into the newly added block of the blockchain. This architecture will prevent the double spend of bitcoin further in the network which facilitates the network nodes as well as minimize the miners task for verification and validation of transaction.
Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective

Knowledge graph completion (KGC) aims to infer missing knowledge triples based on known facts in a knowledge graph. Current KGC research mostly follows an entity ranking protocol, wherein the effectiveness is measured by the predicted rank of a masked entity in a test triple. The overall performance is then given by a micro(-average) metric over all individual answer entities. Due to the incomplete nature of the large-scale knowledge bases, such an entity ranking setting is likely affected by unlabelled top-ranked positive examples, raising questions on whether the current evaluation protocol is sufficient to guarantee a fair comparison of KGC systems. To this end, this paper presents a systematic study on whether and how the label sparsity affects the current KGC evaluation with the popular micro metrics. Specifically, inspired by the TREC paradigm for large-scale information retrieval (IR) experimentation, we create a relatively "complete" judgment set based on a sample from the popular FB15k-237 dataset following the TREC pooling method. According to our analysis, it comes as a surprise that switching from the original labels to our "complete" labels results in a drastic change of system ranking of a variety of 13 popular KGC models in terms of micro metrics. Further investigation indicates that the IR-like macro(-average) metrics are more stable and discriminative under different settings, meanwhile, less affected by label sparsity. Thus, for KGC evaluation, we recommend conducting TREC-style pooling to balance between human efforts and label completeness, and reporting also the IR-like macro metrics to reflect the ranking nature of the KGC task.
The Construction of Sports Health Management Model Based on Deep Learning

Deep learning is a new direction in the field of machine learning. It learns the inherent laws and representation levels of sample data. The information obtained in the learning process plays a great role in interpreting data such as text, images, and speech. Health management refers to the process of identifying, evaluating, and effectively intervening the health of individuals or groups of people. The purpose of this article is to build a sports health management model based on deep learning, to train students to actively take physical exercises, thereby promoting their physical fitness. This article first introduces related concepts such as deep learning and convolutional neural networks and then conducts an experimental exploration on the combination of convolutional neural networks and multilayer perceptrons. Secondly, this article designs a plan for sports health management of sports interventions and compares and analyzes the data before and after health management. The experimental results show that the average value of the overall health dimension after the experiment has reached 85.28, and the average value has reached a very high score, indicating that exercise intervention can effectively improve the physical fitness of students. At the same time, it improves the physical condition of students.
Tackling Explicit Material from Online Video Conferencing Software for Education Using Deep Attention Neural Architectures

The spread of the COVID-19 pandemic affected all areas of social life, especially education. Globally, many states have closed schools temporarily or imposed local curfews. According to UNESCO estimations, approximately 1.5 billion students have been affected by the closure of schools and the mandatory implementation of distance learning. Although rigorous policies are in place to ban harmful and dangerous content aimed at children, there are many cases where minors, mainly students, have been exposed relatively or unfairly to inappropriate, especially sexual content, during distance learning. Ensuring minors’ emotional and mental health is a priority for any education system. This paper presents a severe attention neural architecture to tackle explicit material from online education video conference applications to deal with similar incidents. This is an advanced technique that, for the first time in the literature, proposes an intelligent mechanism that, although it uses attention mechanisms, does not have a square complexity of memory and time in terms of the size of the input. Specifically, we propose the implementation of a Generative Adversarial Network (GAN) with the help of a local, sparse attention mechanism, which can accurately detect obscene and mainly sexual content in streaming online video conferencing software for education.
“It’s Not Hate but … ”: Marginal Categories in Rural Journalism

Journalists who cover rural areas in the United States say they are afraid to report on hate groups, and this fear is exacerbated by close community ties and limited resources among rural journalists. We examine the concept of “hate speech” as a boundary object, analyzing in-depth interviews with U.S. journalists reporting in rural communities (n = 33) to better understand how rural journalists report on hate. We find that rural journalists articulate a clear definition for hate speech but struggle to apply that definition to events within their communities, even as they articulate numerous forms of hate. Journalists often dismissed acts of hate using the residual category of “not hate, but … ” to signal something that they felt was out of place or unsuitable but did not rise to the legal definition of hate speech and thus was not worth reporting on. This approach ends up challenging journalists’ normative commitments to their communities and exemplifies their desire to avoid an objectivity trap.
Performance of phase-matching quantum key distribution based on wavelength division multiplexing technology

Quantum key distribution (QKD) generates information-theoretical secure keys between two parties based on the physical laws of quantum mechanics. The phase-matching (PM) QKD protocol allows the key rate to break the quantum channel secret key capacity limit without quantum repeaters, and the security of the protocol is demonstrated by using equivalent entanglement. In this paper, the wavelength division multiplexing (WDM) technique is applied to the PM-QKD protocol considering the effect of crosstalk noise on the secret key rate. The performance of PM-QKD protocol based on WDM with the influence of adjacent classical channels and Raman scattering is analyzed by numerical simulations to maximize the total secret key rate of the QKD, providing a reference for future implementations of QKD based on WDM techniques.
Study of Clustering Algorithms in Object Tracking and Image Segmentation

Mean Shift is a kind of clustering algorithm, which is mostly used for target tracking, image segmentation, etc. In order to solve the problem that image information is not effectively utilized because of unclear traffic video images and random jitter between image sequences, this paper has studied how to achieve stability of traffic video images and proposed an improved Mean Shift algorithm about how to conduct object centroid registration in compensation for the deviations in space localization and, on this basis, how to select kernel window width to eliminate the errors in scale positioning. This algorithm gets the tracking effect and computation analysis of improved Mean Shift from the perspective of applications, removes or relieves the impact motion has on imaging, improves the quality of the video image information obtained, automatically adjusts the size of window according to the scale changes of moving object in the image, and effectively enhances the stability and real time of object tracking. Besides, in the postprocessing stage, the superpixel based on the Mean Shift algorithm is applied to further optimize the segmentation result; it is a popular mode-seeking clustering algorithm, which makes Mean Shift method ideal on low-dimensional applications such as image segmentation. Finally, we show promising results for remote sensing image segmentation.
Mini Drone Linear and Nonlinear Controller System Design and Analyzing

Choosing the mini-drone for a specific payload for designing purposes is one of the most challenging for both cost and design purposes. It is important to develop and analyze the flight control systems of the quadcopter-type Parrot mini drone and how to make the drones more tolerant of adverse weather conditions. The main problem with any quadcopter is that it loses its balance when exposed to any external influence, even if that influence is weak. Where the controller is the most important part of the drone, six plane controllers cover the six degrees of freedom (6dof) in the movement of the drone. In our research, we have improved the height controller in the drone, thus improving the altitude controller by using (PD) and increasing the values of (Kp and Kd) in the altitude controller of the Parrot Mini Drone Mambo to make it more bearable to external influence and to maintain its altitude. We assumed that the aircraft was exposed to bad weather conditions, such as snowfall and dust, which led to an increase in the speed at which the drone fell. We also increased the free fall constant of the object in the simulation design of the drone from (-9.81 m/s2 to -12.81 m/s2) and used Matlab R2021a Simulink to undertake the tuning of the (Kp and Kd) values. This study yielded good results, as illustrated in the results section. Therefore, this research paper suggests adopting the PD controller in the altitude controller and the new values of Kp and Kd to make the drone more tolerant of weather conditions. We tested these results in practice and got good results.
Complexity Graph-Based Multilabel Classification Method of Human Action in Rope Skipping Scene

Aiming at the problem of insufficient accuracy of multilabel classification of human action at present, a multilabel classification method of human action in the rope skipping scene is proposed. It realizes feature recognition and classification by collecting human action features in the scene of skipping rope movement and uses RNN to optimize the human action feature recognition algorithm. On the basis of feature recognition, the characteristics of human movement in the rope skipping scene are classified, the confidence map of the key point position is obtained by using the Gaussian modeling method, and the action multilabel classification is realized. Finally, experiments show that the multilabel classification method of human action in rope skipping scene has high accuracy and fully meets the research requirements.
Fuzzy Deep Daily Nutrients Requirements Representation

The optimal regime models implement parameters presented by nominal values, intervals, fuzzy models, intuitionistic models. Unfortunately, these models are restrictive and ignore a significant portion of the knowledge contained in the specifications. To overcome this problem, we propose an optimal system that implements deep learning artificial neural networks and fuzzy genetic algorithms for the first time in the literature. The deep neural network extracts the information, the neural network units memorize this information, genetic algorithms select the best architecture of the auto-encoder basing on new regulation function, and fuzzy logic allows some flexibility for our system. First, we collect the expert's nutrients recommendations from different expert research papers. These recommendations are, then, represented in terms of trapezoidal numbers by adopting appropriate rules that encourage the consumption of the favorable nutrients and limit consumption of the unfavorable nutrients in daily diets. Then, we generate large data sets basing on the trapezoidal representation. To transform the expert's recommendations into significant crisp values, we call the auto-encoder neural network, and we propose an original regulation term that controls all the auto-encoder units. To select the best auto-encoder architecture, we use the fuzzy genetic algorithm basing on a simple fuzzy rule to determine the crossover percent, the mutation percent, and the population size at each iteration. Compared to the random systems, the proposed method has shown a great capacity to generalize its experience to unseen recommendations. In a clinical setting, our system can be used by a dietician to accurately determine the daily nutrient requirements of a given individual.
Opportunities and Problems of Teaching Professional Modules in the Period of Digital Transformation

Introduction. Digitalization has become firmly established in the world practice of higher education. Universities transform the process of teaching professional modules, replenish the educational environment with digital technologies and resources. Digital transformation in higher education contributes to the achievement of educational effects and personalization in training. &#x0D; Aim. The purpose of the article is to study the possibilities and problems of teaching courses in the context of digitalization of education in the form of professional modules. &#x0D; Methods. literary and bibliographic review and analysis of sources, terminological analysis, survey of students and teachers, monitoring of digital resources.&#x0D; Results. The analysis of resource provision and monitoring of digital educational platforms, web applications and instant messengers was carried out, which revealed a large number of digital platforms that enable teachers to widely use digital tools. Surveys of students and teachers have shown great interest and a high need for online learning. &#x0D; Scientific novelty. The novelty of the results obtained lies in the expansion of interdisciplinary knowledge about the possibilities of digital educational platforms and tools, innovative technologies. &#x0D; Practical significance. The results obtained can be useful for developing ideas about the possibilities of Web resources, digital tools and choosing ways to solve these problems.