Reviewerly/exHarmony huggingface dataset:
Research on Cloud Computing Scheduling Model Based on Support Vector Machine Algorithm

research-article Research on Cloud Computing Scheduling Model Based on Support Vector Machine Algorithm Share on Author: Fen Li Wuhan Qingchuan University, China Wuhan Qingchuan University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 536–540https://doi.org/10.1145/3482632.3482746Online:22 November 2021Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An analysis of the influencing factors of the choice of intelligent management mode for multi-campus using smart system

Multi-campus university has become a basic feature of Chinese universities. Compared with single campus, multi-campus running has its own complexity and particularity, and its management mode has become the core problem that all schools face together and may affect the development, integration effect and management efficiency of schools. This paper mainly discusses the influencing factors of the choice of management mode of multi-campus universities, and evaluates and analyzes the management mode of Hainan University.
Algorithms for monitoring the functioning of nonequilibrium information processing systems

Abstract The authors have developed an algorithm for monitoring the functioning of nonequilibrium systems, which is based on such operations as establishing the range for permissible values of efficiency probabilities of structural elements functioning, establishing permissible values of structural elements amount, plotting the system entropy dependence on structural elements amount and their effectiveness probability, constructing phase space of the system functioning and determination of the boundaries of regions with nonequilibrium stability. Practical testing of the developed algorithm for monitoring the functioning of nonequilibrium systems has shown that this algorithm can be used to solve several practical problems related to functioning monitoring and predicting the state of a wide variety of nonequilibrium systems.
Research on the Construction of Cloud Classroom System

With the development of modern technology, the maturity of Internet technology, and the diversification of mobile devices, it is no longer difficult to disseminate massive scientific knowledge through the Internet. As a useful supplement to offline education, the educational model of cloud classroom is becoming an educational Trends, but there are still some problems in its development process. This research pays attention to these problems and gives its my own solutions for your reference.
Age Estimation Model Using Multi-Class Architecture and Adaptive Regression Models

Age estimation is important in computer vision and surveillance systems. In this paper, we apply a two-stage and multi-class learning-based architecture to perform age estimation. First, the concepts of combining loss functions and domain adaptation are introduced to optimize the extracted features. Then, through a dual classification method, the input image is roughly classified into 10 classes. Then, a variety of regression models are integrated to obtain the final prediction results. Different classes apply different regression models. Simulations show that the proposed algorithm outperforms many well-known methods in age estimation.
Construction and Application of Supply Chain Experiment System Based on The IoT and cloud computing technology

Aiming at the shortcomings of supply chain experiment teaching in colleges and universities, a supply chain experimental system is construct based on the Internet of Things technology (IoT) and cloud computing technology. Using the comprehensive perception and reliable transmission characteristics of IoT, it provides students with massive experimental basic data and real operation data of enterprises, realizes the sharing of information and resources in each link of the supply chain. The scalability and cross-platform characteristics of cloud computing technology provide students with an experimental platform that is not limited by time and space. The reform of experimental teaching is carried out around this system. An experimental teaching mode of “online and offline + inside and outside the laboratory” is proposed. The supply chain experiment content and process is designed based on “order driven and business process-oriented”. An objective and comprehensive experimental assessment mechanism is established. The application of this system not only enables students to fully experience the modern enterprise information management mode, but also reduces the cost of experimental teaching. The flexibility, efficiency, and effect of experimental teaching have been improved remarkably.
Research on the Training Mode of Applied Technical Talents in Private Universities in China Based on Big Data Analysis

In current big data perspective, the "Internet +" school-running model has become an inevitable trend and demand for the development of education and teaching in private colleges. Under this situation, the development of private colleges has continuously increased requirements for the technical talents training. Therefore, the educators in private colleges should fully understand the applied technical talents training, and combine the characteristics of "Internet +" in the context of big data to explore and practice the training model of applied technical talents, so that the training of talents can be satisfied. The effect of this has enabled private colleges to develop well.
Visual Features and Their Own Optical Flow

Symmetries, invariances and conservation equations have always been an invaluable guide in Science to model natural phenomena through simple yet effective relations. For instance, in computer vision, translation equivariance is typically a built-in property of neural architectures that are used to solve visual tasks; networks with computational layers implementing such a property are known as Convolutional Neural Networks (CNNs). This kind of mathematical symmetry, as well as many others that have been recently studied, are typically generated by some underlying group of transformations (translations in the case of CNNs, rotations, etc.) and are particularly suitable to process highly structured data such as molecules or chemical compounds which are known to possess those specific symmetries. When dealing with video streams, common built-in equivariances are able to handle only a small fraction of the broad spectrum of transformations encoded in the visual stimulus and, therefore, the corresponding neural architectures have to resort to a huge amount of supervision in order to achieve good generalization capabilities. In the paper we formulate a theory on the development of visual features that is based on the idea that movement itself provides trajectories on which to impose consistency. We introduce the principle of Material Point Invariance which states that each visual feature is invariant with respect to the associated optical flow, so that features and corresponding velocities are an indissoluble pair. Then, we discuss the interaction of features and velocities and show that certain motion invariance traits could be regarded as a generalization of the classical concept of affordance. These analyses of feature-velocity interactions and their invariance properties leads to a visual field theory which expresses the dynamical constraints of motion coherence and might lead to discover the joint evolution of the visual features along with the associated optical flows.
Design and Implementation of a Low-Cost Mini Heliostat Solar Tracking System in West Africa

This paper proposes the design and implementation of a low-cost automatic mini heliostat solar tracker system. The tracking system is designed as open loop control based on astronomical equations. The experimental device consists of a 0.3 × 0.3 m2 reflector, 3-mm thick with an Arduino card and two stepper motors. The prototype tracking system has been tested during three days. The influence of the different parameters like number of the day, time, heliostat position and references of the tracking axis was analysed. The results obtained show some improvement with RMSE of 0.050 and 0.085, respectively, for the altitude and azimuth. This system can be used for small applications in rural areas
Development of a Smart Multifunctional Visitor-Identification System

Our article establishes a mechanism to remind residents whether there are guests visiting through related electronic devices for range finding, identification, etc.A vibrating bracelet (or ring) and a flashing light bar are worn on the resident’s wrist (or neck), that is, to remind the resident by means of vibration and light bar flashing. Other related hardware aspects include LED dot matrix modules, voice broadcast, and infrared door locks; LED dot matrix modules are used to display the current date, time, and visitor information, and the voice broadcast will play the speakers of the visiting guests. The LED dot matrix module can provide information about visiting guests for the hearing impaired or residents with reduced mobility, and the voice broadcast can provide information about the visiting guests for the visually impaired or residents with reduced mobility. If the resident completes the identification of the visiting guest and wants to open the door lock, he can use infrared light wave to open the door lock. This is a convenient and safe way for residents with limited mobility or the elderly.
Improved Contrast Enhancement Technique for Dark Images Using Color Channels

Image enhancement is the key stage in many image processing applications. Particularly, the techniques for enhancement of dark images require the in-depth decomposition of image channels. In this paper, an effective technique to enhance dark images captured in lowlight or degraded due to scattering and absorption is proposed. The proposed dark image enhancement technique relies on color correction, improved value channel, and histogram equalization. The proposed method is based on five major principles. The first step is to precisely match the colors in the visual source images (dark images). Next, in the second step, the color transformation from RGB to HSV is applied and its value (V) channel is extracted. In the third stage, adaptive enhancement is performed considering the quality of illumination and fusion is utilized to obtain enhanced channel V. In the fourth step, these channels are repeated as a single channel and histogram equalization is carried out for contrast adjustment. In the final steps, enhanced image is created by converting HSV color space into RGB color space. Experimental findings indicate that the proposed approach subjectively and objectively outperforms other state-of-the-art methods.
Reasoning About Iteration and Recursion Uniformly Based on Big-Step Semantics

A reliable technique for deductive program verification should be proven sound with respect to the semantics of the programming language. For each different language, the construction of a separate soundness proof is often a laborious undertaking. In language-independent program verification, common aspects of computer programs are addressed to enable sound reasoning for all languages. In this work, we propose a solution for the sound reasoning about iteration and recursion based on the big-step operational semantics of any programming language. We give inductive proofs on the soundness and relative completeness of our reasoning technique. We illustrate the technique at simplified programming languages of the imperative and functional paradigms, with diverse features. We also mechanize all formal results in the Coq proof assistant.
Adaptive Incremental Kalman Predictor with Unknown Time-varying Parameters

In practical application, adaptive prediction and control problems with unknown time-varying parameters are common, such as industrial manipulator control, hydrological system adaptive prediction, power load adaptive prediction and oil production dynamic prediction. In this paper, for the system under poor observation condition and with unknown time-varying parameters, the incremental equation is introduced to eliminate the unknown measurement error of the system, and then the adaptive incremental Kalman filtering algorithm is used to estimate the unknown time-varying parameters. On this basis, the observation prediction and control are realized. In this paper, taking the water level rise forecast as an example, two kinds of adaptive incremental filtering algorithms are used to estimate the unknown time-varying parameters, and the simulation comparative analysis of the corresponding incremental prediction is given.
Emptying blocks: the Hazardous Incentive Scheme Behind Blockchain Fee Formation.

Multiple cryptocurrencies suffer a bottleneck effect: blocks are limited in size and the protocols limit their expected arrival rates. On the other hand, this congestion is creates incentives to set transaction fees. We show that this incentive structure suffers from moral hazard, where miners have incentives induce congestion to increase fees.We present this result using two approaches: Auction Theory and Reinforcement Learning. While Game Theory studies strategic behaviour between rational players, Machine Learning is based on blind players finding optimal strategies by \textit{brute force} iteration of trials. The Auction Theory model presented in this paper is a multiunit discriminatory (or pay-as-bid) auction with single unit demand. We add to the standard model the element of supply reduction, characterize the symmetric equilibrium and present how to expand it as the number of players grows asymptotically. The ML part focuses on Q-learning, a well-known application of reinforcement learning algorithms.The main finding has significant policy implications: decentralization, one of the core strengths of proof-of-work protocols, doesn't necessarily apply to block-level incentives.
Mutations in Hopfield Neural Network

Equivalent transformations or mutations in models of statistical mechanics on weighted graphs are one of the most effective methods of research. The star-triangle transformations are known for the Ising model, the electric network model, and the percolation model. Another class of transformations is defined by the form of serial or parallel connection. In this note, we consider the mutations of the graph defining the Hopfield model. The main application question is to find equivalent networks that exhibit the same asymptotic behavior. This question can be of significant importance in optimizing the network topology. From a philosophical point of view, this question is close to the study of the post-priori equivalence of the functioning of networks with different knowledge.
Improving Rule Based and Equivalent Decision Simplifications for Bloat Control in Genetic Programming Using a Dynamic Operator

Bloat is a common issue regarding Genetic Programming (GP), specially noted in Symbolic Regression (SR) problems. Due to this, GP tends to generate a huge amount of ineffective code that could be avoided or removed. Code editing is one of many approaches to avoid bloat. The objective in this strategy is to mutate or remove subtrees which do not contribute to the final solution. Two known methods of redundant code removal, the Rule Based Simplification (RBS) and Equivalent Decision Simplification (EDS) are extended in a new operator presented in this paper, called Dynamic Operator with RBS and EDS (DORE). This operator gives the algebraic simplification table used by RBS the potential to learn from reductions performed by EDS. An initial benchmark highlighted how the RBS table can grow as much as 86% with DORE, and reducing the time spent on simplification by 16.83%. Experiments with the other three SR problems were performed showing a considerable improvement on fitness of the generated programs, besides a slight reduction in the population of the average tree size.
Feature Extraction Amazon Customer Review to Determine Topic on Smartphone Domain

The growth of information affects social development. It makes long distance become shorter so that it is not a problem, it also changes someone of doing business activity through internal media or often called as electronic commercial or more popular with the name of e-commerce. Information about a particular product is called a review, whereas information about certain products obtained from other customer is customer review. Review is useful for consumers and manufacturing industries because determine consumer decisions in choosing a particular product. To determine a sentence that contains a particular feature of extraction on a sentence can be seen from words that contain product features directly is explicit, but there are some words that indirectly product feature or show characteristic of features is implicit. This paper aims to extract product features both explicit and implicit features to a review sentence on the mobile phone domain. The review format used is free text from the amazon e-commerce website but it raises ambiguous words to the product features, therefore takes dummy data to separate the word on product features. The method used to extract the feature is called SLTM (Sentence Level Topic Model) in previous [7] on online review. The dummy dataset, the system performance to extract the explicit feature is 76% and the implicit feature is 92.59%. While in the dataset amazon customer review, system performance to extract explicit features of 88.24% and implicit features of 60%.
Algorithm for Predicting the Speed of Gesture Interaction with the User Interface of a Mobile Software Application

Abstract In this article, Keystroke Level Model and Touchless Hand Gesture Level Model were adapted, as well as Fitts’s law, to work with touch screens of the mobile devices. The article takes into account typical gestures that users most often perform in the process of interaction with touch screens of mobile devices (Tap, Pinch, Swipe, Pan, Drag-and-Drop), as well as time components of gestures (time for direct execution of a gesture, time for mental preparation for the gesture, time for the user to select the necessary grip on the mobile device, time to select the ‘dominant’ hand to perform gestures, the time to select the finger to perform the gesture, time to move the hand away from the touch screen). A method for determining the speed of user gesture interaction has been developed. The problem was formulated and an algorithm was developed. The details of the operation and use of the algorithm are considered. A list of arrays and variables used as initial data for determining the speed of the sequence execution of operations using gesture interaction has been developed. The preparation of the initial data for the operation of the algorithm provides for a research experiment to obtain average values of the temporal components of gestures. The developed algorithm is intended for use at the design stage of mobile software applications that will run on mobile devices with touch screens.
Analysis of Standardized Algorithms for Streaming Cryptographic Convention, Defined in ISO/IEC 18033-4

The fourth part of the international standard ISO/IEC 18033 describes algorithms for streaming encryption. As defined in the standard, streaming encryption is understood to mean an encryption mechanism which uses a key stream to encrypt plaintext in a bit or block manner. There are two types of streaming conversion: a synchronous stream cipher in which a key stream is generated from a secret key (and initialization vector), and an asynchronous stream cipher (self-synchronizing) in which the key stream is generated only from the secret key and some previous ciphertexts (and the initialization vector). Under the keystream, according to ISO/IEC 180334, is understood as the PRS of secret symbols used by the encryption and decryption algorithms of the stream cipher. If part of the key stream is known to the attacker, then any information about the remainder of the key stream should be computationally invalidated by the attacker. These concepts and definitions are discussed in detail in the first chapter of this book. Consider the general models for the defined ciphers in ISO/IEC 18033-4, consisting of a combination of a key stream generator and an output function.
Transfer Learning Approaches for Indonesian Biomedical Entity Recognition

Biomedical Named Entity Recognition (BioNER) could be found in high-quality annotated biomedical dataset of some applications such as medical question answering, clinical documents classification and decision support system. However, high-quality biomedical documents (i.e., PubMed, MPlus) as the main source of BioNER dataset is only available in English while it is lack in Indonesian. Efforts to annotate such documents is also burdensome since it requires extensive work of experts. Transformers based model, i.e. BERT and pretrained multilingual language models lead to an opportunity to perform crosslingual transfer learning from well progressed English BioNER to Indonesian language. This paper investigates XLM-Roberta and M-BERT as pretrained multi-lingual model to perform BioNER for Indonesian biomedical corpora. The model is fine-tuned in English documents before being evaluated in Indonesian biomedical test data. As the results, XLM-Roberta achieves better than M-BERT model in all measurements metrics. The investigations also compare the performance of multilingual with monolingual language model to evaluate the BioNER task and found no significant result difference between both models.
Deep Likelihood Learning for 2-D Orientation Estimation Using a Fourier Filter

Filters for circular manifolds are well suited to estimate the orientation of 2-D objects over time. However, manually deriving measurement models for camera data is generally infeasible. Therefore, we propose loss terms that help train neural networks to output Fourier coefficients for a trigonometric polynomial. The square of the trigonometric polynomial then constitutes the likelihood function used in the filter. Particular focus is put on ensuring that rotational symmetries are properly considered in the likelihood. In an evaluation, we train a network with one of the loss terms on artificial data. The filter shows good estimation quality. While the uncertainty of the filter does not perfectly align with the actual errors, the expected and actual errors are clearly correlated.
Control model of parallel functioning production modules as fuzzy Petri nets

Abstract The article presents the results of research of Data Mining methods with Microsoft SQL Server. Microsoft Clustering algorithm was used for improving the effectiveness of medical prevention and treatment in a cohort of patients with arterial hypertension. There are rationales for monitoring of cardiovascular risk and desire to correct the risk with Data Mining at medical decision support systems. Authors used medical and sociological monitoring data from regional clinical hospital. The segmentation of arterial hypertension patients was performed using Microsoft Clustering algorithm. As a result, a quantitative assessment of the population profile for patients with arterial hypertension was obtained. The authors presented diagrams and profiles of clusters. They were compared. The developed approach is applied for decision support at regional health information management system for reduce of cardiovascular risk.