Reviewerly/exHarmony huggingface dataset:
Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

Virtual machine (VM) consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers. Most existing studies have considered VM consolidation as a bin-packing problem, but the current schemes commonly ignore the long-term relationship between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs, and obtains the stage demand of resources in the future period through multi-step prediction, which is realized by iterative prediction. Then, based on the multi-step prediction, an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance. During the VM consolidation, the affinity value is used to select the migration VM and placement host. The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform. Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption, VM migration costs, and service level agreement (SLA) violations.
Requesting Strategies in The Doorman Movie Using the Trosbrog's Theory (1995)

In conducting this research, the researcher uses the descriptive qualitative method. This research is qualitative because the data are in the form of words and are analyzed based on the natural setting or context. this research to show the division of request strategy in speech act in the doorman movie. The doorman movie is an action genre movie which mostly from the data obtained is imperative. , the results reveal eight of Trosbrog's (1995) seven types of speech act techniques used by the characters in dialog in the movie entitled "The Doorman". Questioning the imperative strategy type of request speech act has the most data that is 16 data, and the willingness about 7 data and mild request and suggestory formulae, Statement of Obligation, Requesting as a statement of wish, permission and Statement of Speaker Wishes have the fewest data. Furthermore, the imperative strategy is very dominant in this case because the movie that Researcher uses is in the action genre, and what is unique is that the interlocutor after being given an imperative sentence or phrase and willingness and there are several or even indicating that there is no verbal answer but in a non-verbal way or we can say it as action as the answer.
An interpretable semi‐supervised system for detecting cyberattacks using anomaly detection in industrial scenarios

Abstract When detecting cyberattacks in Industrial settings, it is not sufficient to determine whether the system is suffering a cyberattack. It is also fundamental to explain why the system is under a cyberattack and which are the assets affected. In this context, the Anomaly Detection based on Machine Learning (ML) and Deep Learning (DL) techniques showed great performance when detecting cyberattacks in industrial scenarios. However, two main limitations hinder using them in a real environment. Firstly, most solutions are trained using a supervised approach, which is impractical in the real industrial world. Secondly, the use of black‐box ML and DL techniques makes it impossible to interpret the decision made by the model. This article proposes an interpretable and semi‐supervised system to detect cyberattacks in Industrial settings. Besides, our proposal was validated using data collected from the Tennessee Eastman Process. To the best of our knowledge, this system is the only one that offers interpretability together with a semi‐supervised approach in an industrial setting. Our system discriminates between causes and effects of anomalies and also achieved the best performance for 11 types of anomalies out of 20 with an overall recall of 0.9577, a precision of 0.9977, and a F1‐score of 0.9711.
Evaluation of Follow-Up Monitoring Information System Using Hot Fit Model

This study evaluates the effectiveness of the Follow-Up Monitoring Information System (SIPTL) of the Supreme Audit Agency of the Republic of Indonesia (BPK RI) based on the HOT Fit Model. Evaluation is focused on external SIPTL or from the auditee side. This research is qualitative research with a case study approach, and the object of the research is the Inspectorate of Majene Regency, West Sulawesi. Evaluation is carried out on human aspects (system use and user satisfaction), organization (organizational structure), and technology (system, information, and service quality). In the human aspect of human resources (HR) management, it shows the suitability of the field of experience, knowledge, and competence. In the organizational aspect, controlling access rights is very good in the form of passwords for each admin and SIPTL inputer. In terms of technology, the speed of access between pages and the addition of features in the SIPTL application is very high. Thus, in general, the external SIPTL application at the Majene Regency Inspectorate has been effective based on the HOT Fit Model. The disadvantages of SIPTL at this time are the absence of regular training for users, communication with users that has not been running optimally, the absence of regular password changes, and the absence of regular application updates. Further research is recommended to evaluate the internal SIPTL, namely from the perspective of BPK RI as a user and the addition of research objects as a comparative analysis.
Image encryption algorithm based on multi-bit superposition and optical chaos

Abstract Aiming at the problem that the leakage of single-layer key encryption may lead to the theft of image information, we propose an image encryption algorithm based on multi-bit superposition and optical chaos. The generated optical chaotic sequence is used to encrypt the encrypted image together with the user input key. In the scrambling stage, the 3D-cat map is used to encrypt the bit plane, and the multi-bit superposition method is used to encrypt the key space between the bit planes again by using the sub-block internal and external encryption methods. In the diffusion stage, the Logistic-tent map is used to diffuse the scrambling image to generate the ciphertext image, and the optical chaotic waveform is used as the carrier to mask the ciphertext image information in the transmission process. At the recipient, the ciphertext image information is obtained through the synchronized optical chaotic waveform, and the image is decrypted by using the synchronized optical chaotic sequence and the key transmitted through the secure channel. If only one of the two cannot decrypt the image, the security of image transmission is improved. The simulation results show that the correlation of adjacent pixels of the image encrypted by the algorithm is close to zero, the gray histogram is evenly distributed, the pixel change rate, the normalized average change intensity and the block average change intensity are close to the ideal value, and the sensitivity of the plaintext is further enhanced, which can effectively resist statistical attacks, differential attacks and exhaustive attacks.
The Considered Interest in Criminalizing Violations of Intellectual Property Rights

Determining the protected interest is important as it serves as a guide for resolving many legal issues. Therefore, the protected interest has been determined in the scope of heinous crimes related to intellectual property rights, which pertain to three types of property, namely literary, industrial and commercial property. These three types have included penal provisions that protect property rights, and it is clear that there are two types of protected interests in these provisions, the first being the direct protection of the literary and financial rights. The first right is a personal right of the author, which is not transferable, while the second right is a property right, which represents the right to exploit the intellectual output. The second interest is non-direct and represents social content, such as the protection of general cultural interests and the protection of economic development. This study is an intrinsic case study of qualitative method. The research questions of this study are :( 1) What interest did the legislator seek to protect against criminalizing infringement of intellectual property rights? And (2) Is the interest that the legislator sought to protect a public interest or a private interest? The study concludes that violating the literary right constitutes a heinous attack on the public interest, as it undermines public trust. Conversely, violating the financial right constitutes a heinous attack on private interests.
On the Source and Instability of Probability Weighting

We propose and experimentally test a new theory of probability distortions in risky choice. The theory is based on a core principle from neuroscience called efficient coding, which states that information is encoded more accurately for those stimuli that the agent expects to encounter more frequently. As the agent’s prior beliefs vary, the model predicts that probability distortions change systematically. We provide novel experimental evidence consistent with the prediction: lottery valuations are more sensitive to probabilities that occur more frequently under the subject’s prior beliefs. Our theory generates additional novel predictions regarding heterogeneity and time variation in probability distortions.
A Study on the Development and Application of AI Convergence Algorithm for Object Recognition and Human Dynamics Analysis in Smart Factory

Many small and medium-sized enterprises in Korea have built smart factories with government support. However, the majority of SMEs are producing products that are difficult to automate and require a lot of manpower on behalf of large companies. In particular, it is difficult for manufacturing companies to enter smart factories because it is difficult to input real-time data.&#x0D; According to research needs as described above, in this study, in order to improve the fact that humans collect information by relying on visual information for more than 70%, object recognition and human dynamics analysis are used to overcome the problem of blind spots in performance aggregation. In order to achieve the purpose of the study, first, the requirements were investigated by qualitative and quantitative methods, and based on this, a prototype model was designed and implemented. Second, the implemented prototype system was applied to actual small and medium-sized manufacturing companies to prove the effectiveness of the AI convergence algorithm through qualitative and quantitative evaluation.&#x0D; The results of this study are expected to provide basic information necessary for calculating standard costs and building digital twins of smart factories by overcoming the limitations of SMEs’ smart factory and enabling real-time data aggregation.
A Stylometric Dataset for Bengali Poems

Poetry is a form of literature that conveys feelings using different styles, aesthetics, and rhythms. The Bengali language has an enriched collection of poems. Every poet has an individual style of expressing their thoughts and emotions. However, stylometric research in this branch of the Bengali language is still in its early stage of development. In this paper, we have presented a stylometric dataset, which has 6,070 poems of 137 poets stored in the textual format. To the best of our knowledge, this is the first stylometric dataset for Bengali poems which will add an extra dimension to the expanding research arena of the Bengali language. To explore the usability of this dataset, we developed poem genre classifiers using deep learning that can classify these poems. Performance analysis of some deep learning classifiers has been presented in addition to classification. The classifiers include GRU and CNN. Among these two, GRU showed better performance by 91.48 in terms of the F1-score. The dataset will be publicly available at https://github.com/shuhanmirza/Bengali-Poem-Dataset after publishing this article.
Short-term prediction of ICU admission for COVID-19 inpatients

Since the COVID-19 outbreak, many hospitals suffered from a surge of some highrisk inpatients needing to be admitted to the ICU.In this study, we propose a method predicting the likelihood of COVID-19 inpatients' admission to the ICU within a time frame of 12 hours.Four steps, the Bayesian Ridge Regression-based missing value imputation, the synthesis of training samples by the combination of two rows (the first and another row) of each patient, customized oversampling, and XGBoost classifier, are used for the proposed method.In the experiment, the AUC-ROC and F-score of our method is compared with those of other methods using various imputation techniques and classifiers.Our method achieves the best performance among the methods.
Rain Process Education as Interactive Multimedia in Early Childhood using the Multimedia Development Life Cycle (MDLC)

Learning media has a positive impact because it can be fun, increase students' interest in learning, and be interactive. The learning method in Early Childhood Education (PAUD) Ratnanigsih uses a Sentra approach and the environment by playing sambal learning to develop the intelligence of a potential child. The facts in PAUD Ratnaningsih found obstacles in the teaching and learning process. These obstacles occur due to the lack of visualization or media for teachers, and the absence of variations in learning can cause saturation in children. Users in this study are early childhood teachers in PAUD Ratnaningsih. Based on these problems, a learning press is needed for teacher and child relations educated at PAUD Ratnaningsih. The learning media design method in this study uses the Multimedia Development Life Cycle (MDLC) approach and learning media using Macromedia Flash 8. The results of this study are in the form of storyboard design, visualization of interface design, implementation of learning media, and testing with Blackbox Testing. Based on the conclusions of this study shows that all components and features of learning media function properly and can be used by teachers in the learning process "Universe" with the sub-theme "Rain".
Mobilised, Isolated and Hybrid Forms of Scientific Activity

The paper discusses different versions of the definition of mobilised, isolated and hybrid scientific activity. The paper traces the evolution of mobilization projects during the Soviet epoch. The case of Gordon Research Conferences as an instrument of intensification of scientific exchanges in the isolated format is considered. Examples of contemporary successful and unsuccessful mobilization projects are presented. The success of a project depends on a correct estimation of necessary resources, on the degree of concentration of research potential, as well as on the task formulation. Re-engineering, integration and adaptation of existing solutions are preferred as feasible tasks for mobilization projects.
Relevance and Problems of the Procedure of Interdisciplinary Scientific Seminars

Conducting interdisciplinary scientific seminars in the context of a continuous process of differentiation and integration of sciences, an increasing volume of scientific information requires new approaches to their organization. Traditional methods of holding scientific seminars limit the cumulative intellectual potential of the participants and leave the discussion unfinished in the discussion of the stated topics. The structure of scientific seminars, built on the basis of the dominance of the position of the speaker over the listeners, initially predetermines their unequal status as subjects of discussion. With an increase in the number of seminar participants, the management of an expert social discussion is reduced to form and regulations, which leads to communication barriers, in fact, endowing the majority of listeners with the status of “subjectlessness”. The authors of the article propose to look for other approaches to the management of interdisciplinary scientific seminars, based on the use of a polylogue procedure and the professional identification of each participant as an equal subject of communication. Management of the course of an expert social discussion in the format of a seminar in the identification and professional self-identification of participants is possible with the use of polylogue technologies of “multi-subject” interaction, including the use of process digitalization tools. In the “new abnormal reality”, the crisis of modern Russian science actualizes the development of new methodological tools and information and communication approaches for organizing expert and social discussion in the format of an interdisciplinary scientific seminar.
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems

Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective recommender system models because they model the rich relational information. The ever-growing volume of data can make training GNNs prohibitively expensive. To address this, previous attempts propose to train the GNN models incrementally as new data blocks arrive. Feature and structure knowledge distillation techniques have been explored to allow the GNN model to train in a fast incremental fashion while alleviating the catastrophic forgetting problem. However, preserving the same amount of the historical information for all users is sub-optimal since it fails to take into account the dynamics of each user's change of preferences. For the users whose interests shift substantially, retaining too much of the old knowledge can overly constrain the model, preventing it from quickly adapting to the users’ novel interests. In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible. In this work, we propose a novel training strategy that adaptively learns personalized imitation weights for each user to balance the contribution from the recent data and the amount of knowledge to be distilled from previous time periods. We demonstrate the effectiveness of learning imitation weights via a comparison on five diverse datasets for three state-of-art structure distillation based recommender systems. The performance shows consistent improvement over competitive incremental learning techniques.
«Visibility» of Domestic Publications in the Scientific Field «Physics and Astronomy» in the Period 1980–2020

Various scientific publication’s arrays visibility (visibility) determines greater or lesser chances for attracting this field scientist’s and specialist’s attention and interest, their response, for the likelihood increasing publications author’s inviting for exchange views and for future joint research. Visibility also determines the general world scientific community’s picture (“image”) and the domestic science in this area development level, which, to a certain extent, forms its scientific world prestige. The article discusses the domestic publications in the field of science “Physics and Astronomy” (according to SCOPUS data for 1980–2020) array’s visibility dynamics development from this area specialist’s point of view. This arrays dynamic’s parameters are analyzed in terms of number of journals filling, the ratio of domestic and foreign journals, the number of publications on these components, their conditional “quality”, determined by these journals scientometric’s characteristics (divided into quartiles). It is shown that the array of publications increased during this period from 878 to 17913 (20,4 times), but the number of the array journals increased only 7.2 times (from 102 to 730), due to a significant average publications number by one journal title per year increase from 8,7 to 24.6 (i.e. 2,8 times).It is determined that the array underwent significant changes in the period of the 80–90s of the last century and came to relative number of parameters 2000-s stabilization – the annual publications per journal title number, the percentage of foreign and domestic sources used (journals), as well as the distribution of publications balance between these groups of journals. This balance is near to a 50/50 ratio with a significantly larger share of array’s foreign journals than domestic ones.The existence of a certain “core” of journals was revealed, which, which includes less than 40% in terms of the number of journals, and includes from ~ 80% to 90% of all annual publications, that points journals should be attracted, first of all, to the attention of library and information specialists, ensuring the convenience of work users.The conditional “quality” of the “core” journals array was assessed (according to the level of quartiles to which these journals are assigned, according to the SCOPUS database data for the corresponding years). This quality is defined as quite high – more than 60% of journals of 1–2 quartiles.
Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos

Learning with noisy label is a classic problem that has been extensively studied for image tasks, but much less for video in the literature. A straightforward migration from images to videos without considering temporal semantics and computational cost is not a sound choice. In this paper, we propose two new strategies for video analysis with noisy labels: 1) a lightweight channel selection method dubbed as Channel Truncation for feature-based label noise detection. This method selects the most discriminative channels to split clean and noisy instances in each category. 2) A novel contrastive strategy dubbed as Noise Contrastive Learning, which constructs the relationship between clean and noisy instances to regularize model training. Experiments on three well-known benchmark datasets for video classification show that our proposed truNcatE-split-contrAsT (NEAT) significantly outperforms the existing baselines. By reducing the dimension to 10% of it, our method achieves over 0.4 noise detection F1-score and 5% classification accuracy improvement on Mini-Kinetics dataset under severe noise (symmetric-80%). Thanks to Noise Contrastive Learning, the average classification accuracy improvement on Mini-Kinetics and Sth-Sth-V1 is over 1.6%.
Developing Education to Increase Staff Member Competency With Organ Procurement

AORN JournalVolume 118, Issue 1 p. P9-P11 Solutions at Work Developing Education to Increase Staff Member Competency With Organ Procurement First published: 27 June 2023 https://doi.org/10.1002/aorn.13961Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume118, Issue1July 2023Pages P9-P11 RelatedInformation