Reviewerly/exHarmony huggingface dataset:
Energy Efficient Virtual Machine Placement using Flower Pollination Algorithm

The cloud data center hardware is emitting immense amounts of hazardous gases to provide Virtual machine services to users. In cloud datacentres, Virtual Machines (VMs) have to be allocated on Physical machine s(PMs) to provide services. The optimal allocation of VM refers to minimal allocation time and effective utilization of energy. This results in the allocation as an NP hard problem. Within this proposed strategy, a Bio inspired approach is discussed to deal with the problem. The proposed strategy adopts Flower Pollination Algorithm (FPA) for VM allocation and uses Multi-objective optimization techniques. The main objectives are to reduce execution time and minimize energy consumption. The FPA algorithm employs two phases Global pollination which explores global search space and Local pollination that explores local search space. The Dynamic Switching Probability determines the FPA allocation phase therefore it regulates global search space and local search space. The parameters of PMs that are considered for energy-efficient allocation of VM are CPU utilization and RAM. Therefore, Energy efficient virtual machine placement using Flower Pollination Algorithm can reduce greenhouse gases emissions by reducing the utilization of hardware resources
Reform Method of University Legal Education Based on Artificial Intelligence and Wireless Communication

The construction and development of legal education in colleges and universities are an important driving force for the construction of a society ruled by law. Looking at the history of the development of legal education in colleges and universities, it can be said that every major change in it has an important relationship with the changes in the social background. Under the background of the rapid development of wireless communication and artificial intelligence technology at this stage, legal education in colleges and universities has also ushered in a new opportunity for upgrading. The purpose of this paper is at studying the realization path of legal education reform in colleges and universities based on the background of the new era. To consolidate the achievements in this field through active reform and to solve the main problems by constructing an optimized teaching system, in the selection of the survey method, the research uses the questionnaire survey method as the main source of data. According to the questionnaire, 60% of the students indicated that they would take the initiative to study and continue to try new assessment methods to adapt to the autonomous learning of the law. Teachers should try their best to let students fully experience law teaching under wireless communication and AI in their usual teaching.
A Logarithmic Domain Algorithm of the Hodgkin-Huxley Neuron Model for Hardware Implementations

In this paper, we propose a simplified algorithm for hardware implementation of the Hodgkin-Huxley Model (HHM). As a method to simplify the algorithm, the logarithmic domain HHM is derived by taking the logarithm for the HHM equation in the real domain. For seamless conversion to the log domain, the HHM model of the real domain is modified to biased HHM(bHHM), which is an HHM with bias current and voltage, and then the HHM model of the logarithmic domain(LbHHM) is derived from this bHHM. By transforming the HHM into the LbHHM, operations such as multiplication, division, and exponential functions in the real domain are simplified to operations such as addition, subtraction, and simple variables in the logarithmic domain. Since the proposed LbHHM of the logarithmic domain is mathematically equivalent to the HHM of the real domain, the performance of the floating point model is the same. The proposed LbHHM provide a simplified algorithm for hardware implementation.
Prediction of Number of Suicidal People Based on KNN

Population change which may be eventuated by any reason is a prominent fact for society. Both governmental and non-governmental organizations (NGOs) trying to track down the impact of community loss to understand the roots of this problem. In this study, suicide, one of the most evitable reasons for death, has been highlighted. Hereby, it was aimed to visualize the data and predict the decrease in population caused by suicide. World Health Organization's latest data and methods to visualize data and predict suicidal people have been shown in the following sub-titles in this paper. Three different interpretable algorithms were used in the study to compare their results. As a result of prediction algorithms, kNN showed an accuracy of %91.
A High-Performance Cloud-based Remote Sensing Data Reprojection Method

Abstract Remote sensing (RS) data are the cornerstone of the digital earth. At present, the productions from Earth observation satellites are updated frequently with refined data quality. However, traditional methods are implemented mostly based on a single machine. When processing large-scale data in batches, there are limitations in computing power and storage, and the expansion is relatively cumbersome. This demands a more efficient and complex computation platform, such as cloud computation. Specifically, the reprojection procedure, unlike other data processing procedures, is both computationally intense and I/O-intense. This paper proposes a high-performance cloud-based RS data reprojection method called OCRM (optimised cloud-based reprojection method). First, the data process flow of reprojection was optimised by improving the I/O efficiency and computational efficiency. Second, with scheduling and controllers under cloud computation, high-performance massive RS data reprojection was achieved. The overall performance of the RS data reprojection module with the optimised algorithm in a single machine considerably outperformed that of the commercial software. The scheduling module can maximise the use of cloud resources to achieve high-performing reprojection calculations. The results indicate the stable performance of the proposed method, and we are working to expand the structure of OCRM to other computationally intensive remote sensing processes, such as data preprocessing and data mining with deep learning.
PRE-based Privacy-Sensitive Healthcare Data Management for Secure Sharing

As a demand for non-face-to-face medical services increase, huge amounts of electronic healthcare data are created and managed online. Since the healthcare data include privacy-sensitive information, security technologies have received considerable attentions from healthcare workers and researchers. On the other hand, sharing for healthcare data is another major issue. Moreover, to manage a large volume of data, efficient security schemes are necessary. To solve privacy, sharing and efficiency problems simultaneously, a new cryptography system is required. In this paper, we propose a proxy re-encryption based healthcare data management model. In the storage process of this model, since data creator encrypts data under system manager's public key and stores them in database, only the system manager can open them. Meanwhile, when someone requires data access, if the system manager approves, the encrypted data is transformed to the data that can be decrypted by the requestor using the proxy re-encryption. As the data undergoes only one encryption process, the operation time will be reduced, and the raw healthcare data will not be revealed. Therefore, it keeps the secrecy of privacy-sensitive healthcare data and delivers data safely.
Evaluation of Accounting Data of Water Company Based on Combination Model

Driven by the dual effects of artificial intelligence and accounting transformation, it has become one of the research hotspots of accounting intelligence in China to replace accountants with computers to make professional judgments and automatically evaluate accounting. On the basis of summarizing and analyzing the research status in the field of intelligent accounting, the realization path of intelligent accounting in water companies is put forward in this paper. By introducing the improved Apriori learning algorithm based on Boolean mapping matrix, intelligent data mining is carried out for evaluation of water accounting. With the help of the improved attribute inductive learning algorithm, the corresponding relationship between the original water consumption attribute and the water right transaction is mined and output. In addition, with the help of forward reasoning in inference engine technology, the computer intelligent data evaluation function is realized. Finally, the functional architecture of accounting system of the water company is designed, which provides reference for the development and application of intelligent accounting system, and explores a new path for the integration of accounting and intelligent algorithms.
Index Based DNA Sequence Compression Algorithm

A research in computational biology relies heavily on storage and manipulation of vast amount of data. Deoxyribonucleic Acid (DNA) sequences make up the majority of biological data. It needs 3221225472 bytes of memory to save a single person's DNA data. In addition, its size is gradually growing overtime with more number of sequences being added to public databases. Only effective DNA sequence compression can assist to increase the storage medium's capacity and make the sequences too compact to transmit via a channel. In this paper, key principles of several existing lossless DNA sequence compression algorithms are studied and based on it, an Index Based DNA Sequence Compression Algorithm (IBDNASCA) is proposed. The proposed algorithm uses two files namely, work file and index file. Initially, Run Length Encoding (RLE) method is applied to the bases. Here, the drawback is that, bases with less number of repetitions occupy more storage space. To pull out this problem, an index file is used and bases of run length less than or equal to five are written in the index file. Further, a modified RLE method is applied in the work file, and an improved Huffman coding method is applied in the index file to further improve the storage capacity. Standard datasets taken from different sources of the GenBank database is chosen to experiment the algorithm. Keys factors, such as compression ratio, compression gain and time taken to compress and decompress the sequences are considered. Experimental results depict that the proposed algorithm outperforms and proves its efficiency when compared with the existing algorithms.
Application of Data Association Mining Technology in Chinese International Online Guiding Platform Considering Complexity of Data

Based on complex data mining, all frequent itemsets are obtained by using the complete itemset prefix tree. According to the calculated confidence and support, the strong association rules of Chinese and international languages are extracted from the frequent itemsets, and the security of compressed language data is considered., further processing the set of strong association rules to obtain the set of candidate rules, MOOC discussed in the existing major educational technologies, pointed out that the core positioning of the global Chinese online communication service platform is to aggregate high-quality teachers and teaching resources for international Chinese education, thereby attracting Existing and potential Chinese students enter.
Expressing logical disagreement from within

Abstract Against the backdrop of the frequent comparison of theories of truth in the literature on semantic paradoxes with regard to which inferences and metainferences are deemed valid, this paper develops a novel approach to defining a binary predicate for representing the valid inferences and metainferences of a theory within the theory itself under the assumption that the theory is defined with a classical meta-theory. The aim with the approach is to obtain a tool which facilitates the comparison between a theory and its competitors within the theory itself, thereby expressing the disagreement between the theories within the theories. After discussing what we can and should require of an object-linguistic representation of a theory for that purpose, this paper proposes to restrict the representation of valid metainferences to locally valid metainferences, a requirement which turns out to be $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ω</mml:mi> </mml:math> -consistent and conservative over classical first-order arithmetic. This approach is then applied to four theories definable on strong Kleene models using a labelled nested sequent calculus.
Deep Learning for Real-Time Face Mask Detection

The corona virus (COVID19) pandemic requires immediate action to avoid adverse effects on local health and the global economy. Due to the effects of COVID19, most of the people lives have been reversed. In the absence of effective antivirals and inadequate medical resources, UN agencies propose a number of measures to regulate infection rates and prevent the limited medical resources from being exhausted. Wearing a face mask is a type of the non-pharmaceutical intervention techniques that can block the primary care of viral droplets ejected by an infected person. According to government basics, it is important for everyone in every country to wear a mask. The government recommends wearing a mask, but many do not. Mask detection is very important in this situation. To contribute to community health, this study aims to develop highly accurate and timely techniques for detecting non-face masks in public and encouraging people to use them. It is said that "Increase the number of people who wear masks correctly and reduce the number of infected people". Starting with MobileNet V2 as a baseline, we used the concept of transfer learning to fuse high levels of linguistic data during mask recognition. For the face detection module, we used Caffe Model in conjunction with OpenCV's DNN module. The anticipated model's remarkable performance makes it ideal for live video police work equipment that detects face masks in real - time.
Elliptic Curve Pairings

Elliptic curve pairings are a powerful tool and a popular way to construct zero-knowledge proofs, which are beginning to be used in blockchains as a way to provide privacy in the transaction ledger.
Towards efficient quantized inference for convolutional neural network on edge system

Abstract Convolutional Neural Networks (CNN) have made splendid achievements in various object detection tasks. To extend the applications of CNN detection models, the implementation of model inference on edge platforms, such as ASIC, FPGA and other embedded systems, has been intensively investigated in recent years. However, the huge model size and its enormous overhead constrain the deployment of detection model on edge platform which always has limited computational capability. Quantized inference of CNN model is one of the efficient approach to running model on edge platform. In this paper, we develop a hardware-friendly quantized inference scheme of detection model that is used for efficient inference on embedded FPGA systems. The proposed method contains several techniques that are able to optimize the quantized inference of detection model on FPGA device. The experimental results demonstrate that not only make the detection model quantized inference more efficient but also maintain the accuracy of object detection.
A Study on Model Compression Methods for SRGAN

The construction of SR algorithms by using deep learning model such as super-resolution generative adversarial networks (SRGAN) have become larger and complicated model architectures with requiring a vast amount of memory capacity. However, it is difficult to operate deep learning models which have millions of parameters at the mobile devices. Thus, in this paper, we present a study on lightweight neural network using network pruning method. Through our extensive experiments, pruned network can show similar performance to the original SRGAN model with substantially reduced model size.
Quantization training with two-level bit width

As the DNN model becomes more complex, the number of parameters constituting the model increases and requires a large amount of computation. Recently, a quantization technique that reduces the memory of the model and enables efficient computation has been studied. In this paper, we propose Fake Single Precision Training (FST) to increase accuracy by using a high bit range for weight and a low bit range for activation output with a certain probability. FST improved the accuracy of the model by applying the features of Google's Quantization Aware Training and FaceBook's Quant Noise method.
Self-Supervised Video Representation Learning with Motion-Contrastive Perception

Visual-only self-supervised learning has achieved significant improvement in video representation learning. Existing related methods encourage models to learn video representations by utilizing contrastive learning or designing specific pretext tasks. However, some models are likely to focus on the background, which is unimportant for learning video representations. To alleviate this problem, we propose a new view called long-range residual frame to obtain more motion-specific information. Based on this, we propose the Motion-Contrastive Perception Network (MCPNet), which consists of two branches, namely, Motion Information Perception (MIP) and Contrastive Instance Perception (CIP), to learn generic video representations by focusing on the changing areas in videos. Specifically, the MIP branch aims to learn fine-grained motion features, and the CIP branch performs contrastive learning to learn overall semantics information for each instance. Experiments on two benchmark datasets UCF-101 and HMDB-51 show that our method outperforms current state-of-the-art visual-only self-supervised approaches.
The weight distribution of hulls of binary codes from incidence matrices of complete graphs

We characterise the weights of all codewords of the hull of the binary code spanned by rows of an incidence matrix Bn of the complete graph Kn for n ≥ 4 and n even. Among others, we show that every codeword of the hull is the sum of an even number of rows of Bn and that its weight depends on the number of rows required to span it. We also determine the weight enumerator and the MacWilliams identity for the hull.
Penalized maximum likelihood estimator for finite multivariate skew normal mixtures

In practice, multivariate skew normal mixture (MSNM) models provide a more flexible framework than multivariate normal mixture models, especially for heterogeneous and asymmetric data. For MSNM models, the maximum likelihood estimator often leads to a statistical inference referred to as “badness” under certain properties, because of the unboundedness of the likelihood function and the divergence of shape parameters. We consider two penalties for the log-likelihood function to counter these issues simultaneously in MSNM models. We show that the penalized maximum likelihood estimator is strongly consistent when the putative order of the mixture is equal to or larger than the true order. We also provide penalized expectation-maximization-type algorithms to compute penalized estimates. Finite sample performance is examined through simulations, real data applications, and comparison with existing methods.
An Improvised Method for Anomaly Detection in social media using Deep Learning

Recently, social media has arisen not only as a personal communication media, but also, as a media to communicate opinions about products and services or even political and general events among its users. Due to its widespread and popularity, there is a massive amount of user reviews or opinions produced and shared daily. Twitter is one of the most widely used social media micro blogging sites. In this paper, a deep learning-based approach is developed to detect the anomalies in social media using text mining. The emotional classification is considered as a part of the model that classifies emotional anomalies present in the text. Classification of such text is conducted via proper training and testing of the classifier.
Deep multi-query video retrieval

Video retrieval methods have been developed for a single query. Multi-query video retrieval problem has not been investigated yet. In this study, an efficient and fast multi-query video retrieval framework is developed. Query videos are assumed to be related to more than one semantic. The framework supports an arbitrary number of video queries. The method is built upon using binary video hash codes. As a result, it is fast and requires a lower storage space. Database and query hash codes are generated by a deep hashing method that not only generates hash codes but also predicts query labels when they are chosen outside the database. The retrieval is based on the Pareto front multi-objective optimization method. Re-ranking performed on the retrieved videos by using non-binary deep features increases the retrieval accuracy considerably. Simulations carried out on two multi-label video databases show that the proposed method is efficient and fast in terms of retrieval accuracy and time.
Design of Permanent Magnet Synchronous Motor Power-assisted System Based on Field Oriented Control Algorithm

Abstract Aiming at the integrated surgical robot, a set of power-assisted control system is designed to overcome the transmission resistance between master and slave hands, so that the operator feels light and comfortable during operation. The system includes permanent magnet synchronous motor, driver, controller, torque sensor, upper computer. The field oriented control algorithm is discussed and is used to control the permanent magnet synchronous motor. STM32 is used as the main control chip, DRV8301 is used as the driving chip, and the upper computer software, which has human-computer interaction interface and real-time torque signal display interface, is written by C#. A non-contact torque sensor and corresponding signal processing module is designed. The experimental results show that with the operator’s start-stop and reversing action, the q-axis current of the motor changes rapidly, and the amplitude of steady-state changes is small. This means that the designed system has the advantages of stable assist torque and fast response.
Study on the Reconstruction of Pressure Field in Sloshing Simulation Using Super-Resolution Convolutional Neural Network

Deep-learning-based Super-Resolution (SR) methods were evaluated to reconstruct pressure fields with a high resolution from low-resolution images taken from a coarse grid simulation. In addition to a canonical SRCNN(super-resolution convolutional neural network) model, two modified models from SRCNN, adding an activation function (ReLU or Sigmoid function) to the output layer, were considered in the present study. High resolution images obtained by three models were more vivid and reliable qualitatively, compared with a conventional super-resolution method of bicubic interpolation. A quantitative comparison of statistical similarity showed that SRCNN model with Sigmoid function achieved best performance with less dependency on original resolution of input images.