Reviewerly/exHarmony huggingface dataset:
A Robust Algorithm for Multi-tenant Server Consolidation

We study server consolidation problems in the cloud under conditions of simultaneous failure of multiple servers, where consolidation means that cloud providers put tenants on shared servers to improve resource utilization and thus reduce operation and maintenance costs. With replicas of each tenant on multiple servers, our objective is to minimize the total number of open servers and ensure that a particular failure will not result in overload of any remaining server. In this paper, we propose the Adaptive Tenant Placement Considering Fault Tolerance algorithm. Unlike existing consolidation algorithms, our algorithm can tolerate multiple server failures while ensuring that no server becomes overloaded. Furthermore, it does not classify replicas into different types. Through experimental evaluations, we show that the proposed algorithm performs better than existing solutions and achieves near-optimal replication allocation.
Video Event Classification and Recognition Using AI and DNN

The paper proposes an efficient deep learning and AI approach for video event recognition. The proposed approach consists of several steps as video acquisition, framing, object detection, joint velocity detection, heat map generation, pose data collection, finally, the classification using deep neural network. The proposed approach is divided into two parts; the first part is object detection, in which we use bounding boxes to detect human presence. The second part is posing detection, which includes identifying human body joints by using open pose. Then, we extract features of body velocity; normalize joint positions and joint velocity. After this step we apply Principle Component Analysis (PCA) to reduce the features and then classify by using Deep Neural Network (DNN) of 3 layers of 50 × 50 × 50. The proposed approach can render up to 6 fps; however, it can be improved with the GPU. The performance of the proposed approach is quite satisfactory with the accuracy of 94.67%.
Secure Cloud Control Using Verifiable Computation

Security for outsourced control applications can be provided if the physical plant is enabled with a mechanism to verify the control signal received from the cloud. Recent developments in modern cryptography claim the applicability of verifiable computation techniques. Such techniques allow a client to check the correctness of a remote execution. This article delivers a proof of concept for applicability of the verifiable computation scheme to control applications over the cloud. We showcase the practicality of verifiable computation on physical plants with different timing demands and deliver a real-life example using a watertank system as the client and Microsoft Azure as the cloud. We show the effectiveness of the verifiable computation scheme on cloud-based implementation of advanced control methods, such as Model Predictive Control.
A novel method for driving path planning with spark

Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, Excellence Coefficient is introduced into PSO and GA combine by serial hybrid structure and make a parallel design based on Spark, which is called EC-SPPSOGA. Excellence Coefficient, calculated by length of the side, can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design of EC-SPPSOGA is based on time-consuming analysis of heuristic algorithms. The performance of EC-SPPSOGA is validated based on the real latitude and longitude of Hohhot and the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability.
Statistical satellite-derived irradiance estimation: A case study in Singapore

Conversion of geostationary satellite imagery to clear-sky index is an important stage of gridded solar resource estimation and solar forecasting. In this research, we demonstrate an approach of combining multiple imaging bands of the Advanced Himawari Imager on-board the Himawari-8 meteoro-logical satellite to produce an estimation of the clear-sky index in a generalized approach for any availability of geostationary satellite bands. This is demonstrated using a network of 25 ground stations in Singapore.
Less is More: Feature Choosing under Privacy-Preservation for Efficient Web Spam Detection

Researches on detecting Web spam are in full swing. However, very high feature dimension and sensitive information leakage restrict the mining. In this paper, a cascade feature selection for mining spam is proposed, which bases on Privacy Preservation (PP) method and Genetic Algorithm (GA). Two criteria, privacy protection degree and maximum classification reliability, are used to pick the representative features to form an optimal minimum feature subset. Discretization, data balancing, feature selection, and ensemble learning method are integrated to detect Web spam. The approach not only greatly reduces the data dimension but also protects the sensitive features from detection. Good spam detection performance is achieved by using only 22 features.
A multiphase level set method for stroke lesion segmentation

Manual delineation of stroke lesion is a time-consuming and tedious task. This paper applies the three-phase level set method[1] to the segmentation of stroke lesion from DWI images. First, we select a region of interest (ROI) around the stroke lesion, and then two random initial level set functions are generated in the ROI. By iterating the level set functions, a three-phase segmentation result is obtained as a partition of the ROI into three subregions with one subregion being the segmented stroke lesion. The experimental results show that, the dice coefficient of our method reached 0.84 and the hausdorff distance reached 5.89. Compared with a level set method and a deep learning method, our method has better segmentation performance, which can obtain expected effect of segmentation.
Robust Comment Sections Need Robust Resources

"Robust Comment Sections Need Robust Resources." Journal of Media Ethics, ahead-of-print(ahead-of-print), pp. 1–2
Research on Blended Teaching under the background of Intemet+

With the advent of the era of “Internet +”, online teaching has made fundamental changes in college students' learning and teachers' teaching because of its significant advantages such as breaking through time and space constraints and free choice. Taking the course of data structure and algorithm as an example, starting from the online teaching needs, this paper deeply discusses how to effectively use the online platform and tools to carry out the course teaching. Combined with the college students' questionnaire, this paper analyzes the online basic situation, learning process experience, learning effect and existing problems, and puts forward some suggestions on the implementation of online teaching design. At the same time, it also puts forward some suggestions. It is explained that the mixed teaching mode will be imperative in the future.
Humanistic meanings in the educational conventions of modernity: on the problem of understanding the philosophical content of subjectivity

A holistic understanding of the current approaches and strategies circulating in the educational environment, where in a situation of global transformation, there is an intensive search for ways and methods to overcome the crisis, is relevant. Within the framework of the pedagogical and philosophical analysis, the authors attempt to discover new aspects of the humanistic content of subjectivity. It is necessary to recognize the absolute value in students, which is seen through the manifestation of individual qualities and abilities. This means that it is necessary to ontologically recognize any subject of knowledge as self-sufficient, regardless of the ranks on the institutional ladder that are common in social structures, in other words, it is necessary to go beyond the boundaries of evaluative categories that, as is known, determine the intellectual status of the subject according to the degree of acquired and certified knowledge. First, the teacher overcomes his power complex, dominance over the student, which is deliberately placed by the traditional approach in the position of ignorant and incompetent person. Secondly, the emphasis on such concepts as discipline, norm, and standard has been changed. Third, the strengthening of group solidarity, to guarantee the basic social meanings in a world of increasing risks, where there is a growing desire to consolidate not with society as a whole, as in previous eras, but with a separate small group. Fourth, the most important conceptual link in these constructions is the ability to communicate. Fifth, there is a rejection of excessive rationalistic perception of reality. A new post-materialistic pedagogical paradigm is formulated, which puts in the first place the deep aspects of the subject, conditioned by his emotionality, soulfulness, humanity, which are least associated with such behavioral parameters as punctuality, perseverance, and academic performance.
Quantum Nodes: Quantum Computing Applied to 3D Modeling

Quantum Nodes is a Blender add-on that introduces the integration of quantum algorithms into the 3D creation process. Our work focuses on allowing users to experiment both new forms of creation and approaching the concepts of quantum computing through 3D creation.
ICT: A Cryptanalysis Toolbox for Block Cipher Structure With Secret Components

In this paper, we present a new technique for recovering the secret inner components of block cipher structures. This technique does not simply distinguish a block cipher structure from a random permutation but recovers the secret inner components. In addition, our technique is more general than ad hoc structural cryptanalysis for specific structures. A new tool, the Inequality Constraints Table (ICT), is introduced to characterize the constraint relation of the secret inner components. If a complete ICT can be constructed, the secret components will be determined by a recursive algorithm. Based on the fundamental structure, an iterative method is proposed to construct an equivalent structure to simplify the initial guess regarding the secret components. Finally, we apply the new technique to several block cipher structures and obtain the secret component recovery results for the 5-round MISTY structure, 23- and 25- round Skipjack structure. To the best of our knowledge, this is the first time to present the structural cryptanalysis against the 5-round MISTY structure, 23- and 25-round Skipjack structure.
Multilevel Readability Interpretation Against Software Properties: A Data-Centric Approach

Given the wide adoption of the agile software development paradigm, where efficient collaboration as well as effective maintenance are of utmost importance, the need to produce readable source code is evident. To that end, several research efforts aspire to assess the extent to which a software component is readable. Several metrics and evaluation criteria have been proposed; however, they are mostly empirical or rely on experts who are responsible for determining the ground truth and/or set custom thresholds, leading to results that are context-dependent and subjective. In this work, we employ a large set of static analysis metrics along with various coding violations towards interpreting readability as perceived by developers. Unlike already existing approaches, we refrain from using experts and we provide a fully automated and extendible methodology built upon data residing in online code hosting facilities. We perform static analysis at two levels (method and class) and construct a benchmark dataset that includes more than one million methods and classes covering diverse development scenarios. After performing clustering based on source code size, we employ Support Vector Regression in order to interpret the extent to which a software component is readable against the source code properties: cohesion, inheritance, complexity, coupling, and documentation. The evaluation of our methodology indicates that our models effectively interpret readability as perceived by developers against the above mentioned source code properties.
Multilevel Image Thresholding Based on Improved Expectation Maximization (EM) and Differential Evolution Algorithm

Multilevel image thresholding is an essential step in the image segmentation process. Expectation Maximization (EM) is a powerful technique to find thresholds but is sensitive to the initial points. Differential Evolution (DE) is a robust metaheuristic algorithm that can find thresholds rapidly. However, it may be trapped in the local optimums and premature convergence occurs. In this paper, we incorporate EM algorithm to DE and introduce a novel algorithm called EM+DE which overcomes these shortages and can segment images better than EM and DE algorithms. In the proposed method, EM estimates Gaussian Mixture Model (GMM) coefficients of the histogram and DE tries to provide good volunteer solutions to EM algorithm when EM converges in local areas. Finally, DE fits GMM parameters based on Root Mean Square Error (RMSE) to reach the fittest curve. Ten standard test images and six famous metaheuristic algorithms are considered and result on global fitness. PSNR, SSIM, FSIM criteria and the computational time are given. The experimental results prove that the proposed algorithm outperforms the EM and DE as well as EM+ other natural-inspired algorithms in terms of segmentation criteria.
A Study on the Teaching Design of Research on the Teaching Design of Foreign Language Classroom in Colleges and Universities in the Era of Artificial Intelligence

In recent years, with the rapid development of social economy, science and technology have also made great progress. Artificial intelligence and other advanced technologies continue to flow out, and has been widely used in various fields of society. Its application in the field of education compared with the traditional teaching methods have a more significant positive impact. We should make full use of artificial intelligence technology in the field of teaching to give full play to its role and promote the development and reform of education. Basically, we serve people through computing and data. Therefore, according to the teaching design of foreign language classroom in colleges and universities, the new requirements of ideological and political education in China, combined with the results of the blended teaching reform in our school, and taking the main course of Japanese Audio-Visual and Oral (3) as an example, this paper points out that teachers should strengthen their self-cultivation and explore the elements of content education. From the aspect of curriculum standard, we should refine the curriculum and perfect the evaluation standard. It is necessary to establish the resource base of artificial intelligence autonomous learning, explore the instructional design of strengthening the consciousness of autonomous learning, and integrate the ideological and political education ideas with the elements of artificial intelligence education in classroom instructional design.
On Dimension in Graph Convolutional Networks for Distinguishing Random Graph Models

Graph convolutional networks are a popular representation learning method for graphs, wherein an input graph is mapped to a d-dimensional embedding vector, yielding a latent representation. We continue the project of theoretically elucidating the roles of various aspects of GCN architectures by studying the power and limitations of GCNs in distinguishing random graph models based on embedding vectors of sample graphs. In the present work, we show how the embedding dimension affects the set of pairs of models that can be distinguished from one another. We also consider the application of GCNs to multi-hypothesis testing and use channel capacity results to show a lower bound on how the embedding dimension must scale with respect to the number of hypotheses and the signal-to-noise ratio in order to guarantee a probability of error tending to 0.
LSTMVAEF: Vivid Layout via LSTM-Based Variational Autoencoder Framework

The lack of training data is still a challenge in the Document Layout Analysis task (DLA). Synthetic data is an effective way to tackle this challenge. In this paper, we propose an LSTM-based Variational Autoencoder framework (LSTMVAF) to synthesize layouts for DLA. Compared with the previous method, our method can generate more complicated layouts and only need training data from DLA without extra annotation. We use LSTM models as basic models to learn the potential representing of class and position information of elements within a page. It is worth mentioning that we design a weight adaptation strategy to help model train faster. The experiment shows our model can generate more vivid layouts that only need a few real document pages.
New Rules for Attracting Experts to Control and Supervisory Activities in the Field of Education

From July 1, 2021, new rules of control and supervision activities come into force, in the framework of the control activities of which experts are involved.&#x0D; The authors of the article, having studied the regulatory legal acts and analyzed the scientific literature, explain the essence of the examination and the expert, their rights during inspections, as well as the new rules for the certification of experts.
Epistemological Transformations of Science in Digital Age

The purpose of the article is to analyse the epistemological transformations of science associated with processes of its digitalisation. Scientific novelty of the work lies in explication and analysis of the main epistemological features of digital science. The article presents a definition of the digital science notion. As a result, it is shown that in digital science, means and objects of research, being closely connected with and dependent on each other, change the subject field of science and epistemological strategies.
Privacy-Preserving Optimal Recovering for the Nearly Exhausted Payment Channels

Payment Channel Network (PCN) is one of the most promising technologies for scaling the capacity of blockchain-based cryptocurrencies and improving the quality of blockchain-based services. However, during the use of PCNs, a significant portion of the payment channels gradually become exhausted, which triggers additional consumption of on-chain resources and makes PCNs less useful. This is a fundamental problem for blockchain-based cryptocurrencies, worthy of a thorough investigation.In this paper, we propose OPRE, a protocol for OPtimal off-chain REcovering of payment channels, to solve this problem. It is optimal in that it recovers the maximum number of nearly exhausted channels in the PCN. Furthermore, we consider users’ privacy concerns and design a privacy-preserving version of this protocol, so that users’ balance information does not need to be revealed. This protocol maintains optimality in recovering payment channels while providing cryptographically strong privacy guarantee. In addition to the theoretical design and analysis, we also implement OPRE and experimentally evaluate its performance. The results show that the OPRE protocol is both efficient and effective.
Chinese Event Detection Based on Event Ontology and Siamese Network

Event detection (ED) aims to identify the events in raw text. Most existing methods for ED include two steps: locating triggers and classifying them into correct event types. However, such methods require a lot of labor costs to annotate triggers and face the problems of word-trigger mismatch and polysemy, especially in Chinese. To address these challenges, we propose a novel method for ED based on event ontology and Siamese network. First, event ontology is introduced as priori event-based knowledge base, which provides enormous different event types with event class specification. And then, textual specifications of event class elements (such as actions, person, places and objects) could be obtained from event ontology, thus event detection is transformed into the problem of judging which event class specifications are similar to the raw text to be detected through the Siamese network, which solves the problem of high cost of trigger annotation and word-trigger mismatch. Extensive experiments with comprehensive analyses illustrate the effectiveness of proposed method. The source code of this paper can be obtained from https://github.com/nicahead/event_detection .
Design of Convenient Structured Light Projection System for Microscopic Measurement

This paper presents a convenient structured light projection system for microscopic measurement, which is used to design the structured-light pattern on the computer, you can freely edit the pattern structure, color, geometric size, motion mode, projection brightness, contrast and other attributes. An optional path system with zoom function is designed, which can output 10 micron wide structured-light fringe. The vision system is established by using camera and microscope, which is used to shoot the pattern of structured-light, and the 3D morphology is reconstructed by analyzing the pattern of structured-light. The experimental results show that the system can project small feature-size structured-light patterns and is very suitable for 3D morphology measurement of micro objects from tens of microns to hundreds of microns.
Analysis on the Application of Computer Virtual Reality Technology in College Physical Education Teaching

In recent years, the rapid development of science and technology in China, the emergence of computer virtual reality technology for college physical education teaching model has brought new opportunities. Therefore, on the basis of fully exploring the characteristics of computer virtual reality technology, this paper takes the feasibility and fit point of computer virtual reality technology in physical education teaching in colleges and universities as the starting point and courseware making, so as to provide an innovative path for the development of college physical education teaching.
Your P Values Are Wrong

Typical solar photovoltaic (PV) industry methodologies for calculating energy production probability of exceedance values (P values) for PV power plants do not provide reliable or actionable results for risk management purposes. By excluding common sources of major plant underperformance, such as availability and curtailment, and by ignoring asymmetries and non-linearities in system performance, typical P value methodologies practically guarantee an overly optimistic prediction of PV plant performance. While we suggest ways to improve the accuracy and usefulness of P values, we emphasize that long-term PV plant owners should prioritize contractual management of tail risks over predictive analyses.
Mitigation Of Virtual Machine (VM) Using Cloud Computing

Cloud computing enables users to move their calculations to a distributed environment, where additional resources may be used to finish execution more rapidly. Virtualization refers to the ability to run numerous virtual machines (VM) on a single physical system. VM switches one physical computer to another during the migration process. Offline migration stops the operation until the virtual machine can continue on the destination system, but live migration continues uninterrupted. Live migration is one in which the VM seems to be responding all of the time from the perspective of the clients. A virtual machine (VM) is a computer system that is emulated. Virtual machine migration is a helpful tool in cloud computing for moving Operating System instances across different physical machines. It is used to balance loads, manage faults, perform low-level system maintenance, and minimize energy usage. For VM migration, several approaches and parameters are available. The different virtual machine migration strategies are discussed in this study.