Reviewerly/exHarmony huggingface dataset:
Cloud Metering and Monitoring

For Service Oriented Infrastructures (SOI) and Cloud services, monitoring and metering are crucial tasks. For the proper execution of cloud applications &amp; the monitoring of SLA compliance, the data gathered through monitoring is required. The motivations for and challenges in monitoring and metering cloud systems are discussed. The methods for monitoring the network and execution environment on virtualized infrastructures will be discussed together with the monitoring tools that are now available on various commercial and research platforms. Every distributed computing system has a basic feature that includes monitoring chores. Every service should be watched over in order to evaluate its effectiveness and enable remedial measures in the event of failure. A functional snapshot of the system's behaviour along the time axis is what monitoring data reflects. Such information is essential for pinpointing the source of issues or for fine-tuning various system components. For example, failure detection and recovery procedures require a monitoring component to determine whether to restart a specific server or subsystem based on the data gathered by the monitoring system.
Use of Big Data in the Cloud Computing

Abstract: Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Massive growth in the scale of data or big data generated through cloud computing has been observed. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. The rise of big data in cloud computing is reviewed in this study. The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced. The relationship between big data and cloud computing, big data storage systems, and Hadoop technology are also discussed. Furthermore, research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance. Lastly, open research issues that require substantial research efforts are summarized. This paper introduces several big data processing techniques from system and application aspects here provide an organized picture of challenges that are focused by the application developers and DBMS designers in developing cum deployment of the internet scale applications. Then we see about the security issues in the cloud computing along with the big data and Hadoop. We show some possible solutions for the issues of the cloud computing and Hadoop.
Model of formation of individual career paths of military university students under conditions of social uncertainty

The article presents the results of scientific and practical research on the problem of individual trajectories formation of professional career of military university students under conditions of social uncertainty, as well as a model of formation of individual career paths of military university students under conditions of social uncertainty. Analysis of the military universities practice shows that cadets need psychological and pedagogical assistance in modeling the individual trajectory of professional career in order to better understand their professional development, which would be built into the training and extracurricular activities of the future officer. The creation of such conditions will reduce the possibility of errors and difficulties in the preparation and modelling of the individual trajectory of his professional career by the future officer, increase the possibility of achieving significant results in the military professional activities with less effort, time and resources. The relevance of the ideas contained in the study lies in the fact that the author in the course of research work formulated pedagogical conditions, as well as the creation of a pedagogical environment conducive to the activation of personal, Professional and career potential of the cadet in the process of formation of individual career paths in conditions of social uncertainty.
Diagnostic tools for assessing the formation of digital culture of future teachers

The article discusses the process of formation of a digital culture of a teacher in the context of digitalization of education, presents a working definition of the concept of “digital culture of a future teacherˮ. In our study, we identified the structural components of digital culture: motivational-value, cognitive, instrumental, communicative, moral-ethical and reflective, which are formed in the process of professional development of the future teacher. Based on the identified structure, an author's methodology was developed to determine the level of formation of the digital culture of future teachers, which includes an author's questionnaire and a diagnostic game. Based on the diagnostic results, it will be possible to make changes in the process of organizing the professional training of a modern teacher, to determine the pedagogical conditions for the more effective formation of his digital culture.
Continuity in the formation of functional literacy of students (from preschool education to basic general education)

This article is devoted to the analysis of the main theoretical approaches to the problem of ensuring the continuity of the formation of functional literacy of students. Based on the analysis of foreign and domestic studies on this topic, the concept of ‟functional literacy” is clarified, the modern system of formation of the phenomenon under study in the context of the development of a child's personality is analyzed. The article provides examples of practice-oriented tasks for the formation of prerequisites for functional literacy in preschool age, the formation of the phenomenon under study in the classroom in primary and secondary schools. The directions of formation of functional literacy of students are proposed.
Modern world educational models: foreign experience of educational management

The article is devoted to the description of principal world educational models (European, American, and Asian), reforms are implemented by state governments for raising up high school effectiveness are presented in the paper. Theoretical significance of the research is following: exposed and described positive world experience in the field of modernization of the high educational system; characterized structures, basics, main futures and processes of forming the leading educational models of high school. Analytical review of scientific material observes the structure and content of the reforms of high education in the world, its characteristics and results, models of high education. Materials of the paper have practical significance: it could be used by governmental and university managers who is interested in problems of high school organization, reformation and ruling.
Pengaruh Penggunaan Media Gambar Berseri Berbasis Digital Bertema Sosial Terhadap Keterampilan Menceritakan Kembali Teks Fabel Siswa Kelas VII MTS Al-Hikmah Purwoasri Kabupaten Kediri

This research is motivated by the tendency of teachers to use the old approach or simply convey theory which results in students being less actively involved in the teaching process. The purpose of this study was to determine the effect of using social-themed digital serial media images on the skills of retelling fable texts for class VII students of MTs Al-Hikmah Purwoasri, Kediri Regency. This type of research is a quantitative research with a quasi-experimental method with a non-equivalent group design. Sampling technique with purposive sampling. The sample in this study is the control class and the experimental class. The variables in this study are independent variables, dependent variables, and control variables. The data of this study is the skill test scores of retelling fable texts without and by using social-themed digital serial media images. The results of this study were the mean scores for the control and experimental pretest classes, namely 61.25 and 68.5, the control and experimental posttest classes, namely 73.25 and 81.5. Processing the t-test with the condition that the two samples must be normally distributed and the variance is homogeneous. The posttest t-test results obtained from the control and experimental classes were tcount 3.535 and ttable 2.024. The condition for the t-test is that if tcount ttable then Ho is rejected. So, there is a significant influence on social-themed digital-based serial image media on the skills of retelling fable texts for class VII MTs Al-Hikmah Purwoasri, Kediri Regency.
Natural Historical Attitude: Objectivity Before Truth Book Review: Turner D. Making Prehistory: Historical Science and the Scientific Realism Debate. Cambridge: Cambridge University Press, 2007.

Derek Turner believes that a proper interpretation of Arthur Fine’s natural ontological attitude can help to reveal the nature of the difference between «historical» (geology, archeology, forensics) and «empirical» (physics, chemistry) sciences. From his point of view, the apparent asymmetry between these sciences is a consequence of different understanding of the possibilities to «manipulate» the objects of study and the role played by background theories. In our opinion, Turner’s concept is a good example of how profound and inviting the instrumentalistic concept of science could be. First, it is «reflexive» in the sense that it constrained other instrumentalist theories (B. Fraassen’s constructive empiricism deeply flawed). And secondly, it is «constructive» – the emphasis that «the strength of arguments for or against scientific realism can vary depending on the scientific context» quite unexpectedly leads to the fact that «ideographic / nomothetic division is not very helpful», and to the fact that «epistemic disadvantage, but the same epistemic status». Reflections on the book: Turner D. Making Prehistory: Historical Science and the Scientific Realism Debate. Cambridge University Press, 2007.
VALID: a perceptually validated Virtual Avatar Library for Inclusion and Diversity

As consumer adoption of immersive technologies grows, virtual avatars will play a prominent role in the future of social computing. However, as people begin to interact more frequently through virtual avatars, it is important to ensure that the research community has validated tools to evaluate the effects and consequences of such technologies. We present the first iteration of a new, freely available 3D avatar library called the Virtual Avatar Library for Inclusion and Diversity (VALID), which includes 210 fully rigged avatars with a focus on advancing racial diversity and inclusion. We present a detailed process for creating, iterating, and validating avatars of diversity. Through a large online study (n=132) with participants from 33 countries, we provide statistically validated labels for each avatar's perceived race and gender. Through our validation study, we also advance knowledge pertaining to the perception of an avatar's race. In particular, we found that avatars of some races were more accurately identified by participants of the same race.
Exploring topic models to discern cyber threats on Twitter: A case study on Log4Shell

Gathering information about cyber threats from various sources can help organisations improve proactive cyber defense and mitigate potential cyber attacks. Recently, Twitter has shown to be beneficial in providing timely Cyber Threat Intelligence (CTI) concerning cyber threats, software vulnerabilities and exploits. However, manually identifying and investigating useful insights, patterns, and trends from abundant unstructured tweets is difficult. This work proposes an end-to-end data-driven framework to collect, analyze, and monitor tweets using unsupervised topic modeling techniques. A novel visualization technique is also proposed to monitor the dynamic topic trends over time, offering an interpretable way to gain insights into a topic's lifecycle. A case study is conducted on the Log4shell vulnerability incident to demonstrate the applicability of the proposed framework. Experiments are carried out on a real-world Twitter dataset collected from 47 users within the CTI community. Results indicate that the proposed framework can discover emerging topics relevant to real-world cybersecurity incidents, with Log4Shell-related topics identified before the common public disclosure date by the National Vulnerability Database (NVD). This framework can expedite the data processing workflow and visualization for cyber threat analysis, enabling organizations to identify trends and patterns that can potentially indicate a security breach or attack.
Towards efficient long-horizon decision-making using automated structure search method of hierarchical reinforcement learning for edge artificial intelligence

Hierarchical reinforcement learning (HRL) is a promising approach for efficiently solving various long-horizon decision-making tasks in the Internet of Things (IoT) domain. However, HRL algorithms are known to rely on expert knowledge to preset an appropriate hierarchical structure for different IoT tasks, which leads to higher trial costs and limits its wider application. In this paper, we propose a new method called DHRL (Dynamic-Level Hierarchical Reinforcement Learning) and it is able to adaptively search for the optimal hierarchical structure while maintaining the generality of framework design. DHRL incorporates an embedded exploration and exploitation mechanism that effectively solves the challenges caused by dependence between different levels and achieves a balance between maximizing benefits and current evaluation accuracy. Nonetheless, the more exploration processes inevitably has a negative impact on the performance. To mitigate this influences, we propose a synchronous training architecture to support DHRL operating in a distributed and parallel manner, in which the adaptive evolutionary method is also introduced to accelerate the convergence. Extensive experimental evaluations are conducted to demonstrate the effectiveness of our theory and method.
Geospatial Big Data Analytics for Quality Control of Surveys

Geospatial big data analytics allows survey quality control analysts to draw important conclusions about survey data quality that otherwise would take excessive time and resources. In this work, we explored two algorithmic methods that can help ensure reliability of survey interviews by detecting geospatial outliers. Focusing on geospatial data collected from surveys, we implemented outlier detection techniques with two different distance metrics to identify statistical anomalies in real-world datasets that may have qualitative interpretations. We found that one algorithm, which considers the local distribution of points in a dataset, identifies a different set of outliers when compared to another method, which considers the global distribution of points. Since there was a small overlap (10-19%) of flagged points between the two algorithms implemented, it may be helpful for analysts to focus on the fewer “outlier” points that are flagged by both methods rather than all the “outlier” points that are flagged by each algorithm. Finally, analysts should consider the computational costs, as the algorithms differ significantly.
A conditional GAN-based approach to build 3D facies models sequentially upwards

This paper presents an alternative way of simulating 3D facies models using conditional generative adversarial networks (GANs) by reconstructing the 3D volume upward with customised thickness from generated 2D slices. To mimic natural fluvial reservoirs, GANs need to learn the complicated facies’ lateral and vertical sequences, connectivity patterns and spatial correlation, e.g. the distribution of channel deposits and lateral accretion packages, including channel lags and point bars in a meandering fluvial system. 3D GAN modelling remains challenging, despite some success in 3D, due to the increased data and GAN model complexity caused by the 3D training data. For example, a 3D facies model with a larger number of voxels introduces more complex geobody shapes and requires 3D convolutions that increases the computational burden compared to 2D convolutions. Our proposed conditional GAN-based framework predicts 2D upper layers slice by slice to build 3D facies models, mimicking the sedimentary process in a purely aggrading system, which is less computationally costly than generating the facies models used as training data and generating facies models directly in 3D from a GAN. Each upper layer is predicted conditionally to a lower layer that contains the facies distributions in all layers below the target using several unique training enhancements. All those methods help prevent the simulated 3D models from gradually losing realism and the preset sedimentary settings vertically while inferring the plausible evolution of the channel system. The results demonstrate that the trained conditional generator, a SPADE generator, can work as a simulator to produce unconditional 3D meandering fluvial facies models by inputting a user-defined spatial correlation strength between layers and the number of layers needed (thickness in meters) without post-processing or retraining. This framework’s performance was evaluated using a connectivity metric towards the range of avulsion scenarios that represent a range of depositional uncertainty.
Adaptive semi-paired query hashing for multi-modal retrieval

Multi-modal hashing has attracted enormous attention in large-scale multimedia retrieval, owing to its advantages of low storage cost and fast Hamming distance computation. Existing multi-modal hashing methods assume that all multi-modal data are well paired and then encode the paired multiple modalities into joint binary codes. However, it is not ensured that all data are fully paired in practical applications. In this paper, we present an adaptive semi-paired query hashing method, which facilitates learning the hash codes for semi-paired query samples. The proposed method performs projection learning and cross-modal reconstruction learning to maintain the semantic consistency between multi-modal data. Meanwhile, the semantic similarity structure and the complementary multi-modal information are preserved by hash codes to obtain a discriminative hash function. In the encoding stage, the missing modality features are completed via the learned cross-modal reconstruction matrices. In addition, the multimodal fusion weights are fine-tuned adaptively for the new query data to capture the modality difference. The extensive experiment results on three benchmark datasets show that our proposed algorithm outperforms state-of-the-art multi-modal hashing methods.
Depth super-resolution from explicit and implicit high-frequency features

Guided depth super-resolution aims at using a low-resolution depth map and an associated high-resolution RGB image to recover a high-resolution depth map. However, restoring precise and sharp edges near depth discontinuities and fine structures is still challenging for state-of-the-art methods. To alleviate this issue, we propose a novel multi-stage depth super-resolution network, which progressively reconstructs HR depth maps from explicit and implicit high-frequency information. We introduce an efficient transformer to obtain explicit high-frequency information. The shape bias and global context of the transformer allow our model to focus on high-frequency details between objects, i.e., depth discontinuities, rather than texture within objects. Furthermore, we project the input color images into the frequency domain for additional implicit high-frequency cues extraction. Finally, to incorporate the structural details, we develop a fusion strategy that combines depth features and high-frequency information in the multi-stage-scale framework. Exhaustive experiments on the main benchmarks show that our approach establishes a new state-of-the-art. Code will be publicly available at https://github.com/wudiqx106/DSR-EI.
Visual attribution using Adversarial Latent Transformations

The ability to accurately locate all indicators of disease within medical images is vital for comprehending the effects of the disease, as well as for weakly-supervised segmentation and localization of the diagnostic correlators of disease. Existing methods either use classifiers to make predictions based on class-salient regions or else use adversarial learning based image-to-image translation to capture such disease effects. However, the former does not capture all relevant features for visual attribution (VA) and are prone to data biases; the latter can generate adversarial (misleading) and inefficient solutions when dealing in pixel values. To address this issue, we propose a novel approach Visual Attribution using Adversarial Latent Transformations (VA2LT). Our method uses adversarial learning to generate counterfactual (CF) normal images from abnormal images by finding and modifying discrepancies in the latent space. We use cycle consistency between the query and CF latent representations to guide our training. We evaluate our method on three datasets including a synthetic dataset, the Alzheimer's Disease Neuroimaging Initiative dataset, and the BraTS dataset. Our method outperforms baseline and related methods on all datasets.
CAST: Context-association architecture with simulated long-utterance training for mandarin speech recognition

End-to-end (E2E) models are widely used because they significantly improve the performance of automatic speech recognition (ASR). However, based on the limitations of existing hardware computing devices, previous studies mainly focus on short utterances. Typically, utterances used for ASR training do not last much longer than 15 s, and therefore the models often fail to generalize to longer utterances at inference time. To address the challenge of long-form speech recognition, we propose a novel Context-Association Architecture with Simulated Long-utterance Training (CAST), which consists of a Context-Association RNN-Transducer (CARNN-T) and a simulating long utterance training (SLUT) strategy. The CARNN-T obtains the sentence-level contextual information by paying attention to the cross-sentence historical utterances and adds it in the inference stage, which improves the robustness of long-form speech recognition. The SLUT strategy simulates long-form audio training by updating the recursive state, which can alleviate the length mismatch between training and testing utterances. Experiments on the test of the Aishell-1 and aidatatang_200zh synthetic corpora show that our model has the best recognition performer on long utterances with the character error rate (CER) of 12.0%/12.6%, respectively.