Reviewerly/exHarmony huggingface dataset:
A power information security partition storage method based on multidimensional data mining

Aiming at the problems of low security coefficient and low storage efficiency of traditional methods, a power information security partition storage method based on multidimensional data mining is designed. Firstly, the relationship value between power information data is analyzed and determined, and the power information collection is completed with the help of covariance matrix. Then, the membership function of multidimensional power information data is calculated, and the noise reduction of multidimensional power information is completed by calculating Lagrange coefficient. Finally, the multidimensional information data is analyzed by two-dimensional correlation, the multidimensional power information data is layered, the partition structure is optimized, the data of the three regions after stratification are encrypted respectively, so as to complete the secure storage of power information data. Experimental results show that the security factor of power information security partition storage using this method is always higher than 0.9, and the storage efficiency is high.
Two-Stage Self-Supervised Learning for Facial Action Unit Recognition

This paper proposes a two-stage self-supervised method for facial action unit recognition. First, an auto-encoder approach is applied, with an encoder which operates on a small proportion e.g., 40% of images patches. The decoder reconstructs the original image from latent features and learnable mask tokens. After training, the encoder is adapted to the task of AU recognition, yet poor results are observed in certain AU classes. To address the problem, contrastive learning is proposed to learn discriminative features. This method uses images from the VGG-Face2 dataset, which vary in terms of head pose, age and background. Experiments on AU recognition show that the two-stage method strengthens the representation quality. Compared to previous self-supervised methods, the pre-trained encoder achieves the best linear probing result on DISFA dataset, with the F1-score of 53.8%. Fine-tuning experiment is also conducted, and obtains the F1-score of 59.9%, with a roughly 3% gap to existing state-of-the-art method. The two-stage training method is easy to implement and expandable for further research.
Comparison of segmentation of CU-Net,DRU-Net and DU-Net on Cervical Cancer Data Set

Cervical cancer is one of the most prevalent disease in women.Timely detection of cervical cancer can save lives.Automated detection of cervical cancer tools can be devised whose reliability depends on the rate of accuracy of segmentation of Pap Smear images.For segmenting medical images there are variants of U-Nets available. There are various papers in this area of research that deals particularly with the segmentation of overlapped nucleus.This paper guides the researchers of medical image processing to select the U-Net that gives a trustworthy result.Condensed U-Net,DU-Net and DRU-Net are recent developments in the field of U-Nets.Data sets that are used in this paper are secondary data set taken from 2018 Data Science Bowl on which these three U-Nets are implemented.The accuracy,precision,and Recall values are tabulated to provide a comparison on the performance of these three U-Nets on a cervical cancer data set.
Artificial neural networks application in medical images

Diagnostic examinations through analog and numerical images are today the main method of diagnosing various diseases. Numerical images can be obtained with different methods depending on the technique used. Basically, any technique of obtaining medical images is nothing but the connection of a physical process of interaction of radiation with the subject / environment and with the help of the computer, these processes became visible through numerical images. Given the nature of the physical phenomena used and the lack of perfection of detection systems, the result is not perfect but is an estimate/price of “true value” which remains unattainable. Adding to this the human error during a medical examination, the patient's movements, etc., can flow very important artifacts, which must first be understood and analyzed and then using numerical methods, to correct them in the final version of the numerical image. This paper analyzes some numerical methods of numerical image correction such as interpolation and convolution, implemented in MATLAB program. In particular the interpolation technique is applied using Artificial Neural Networks (ANN), feedforward.
Damage assessment of mobile communication facilities subjected to major earthquakes

Communication systems play a critical role in emergency response during disasters. In this study, we proposed a methodology for assessing damage to mobile communication facilities subjected to major earthquakes, with consideration of both ground shaking and power outage effects. Fragility curves were employed to evaluate the damage to base stations due to ground shaking. The power transmission relationship among electrical substations was established to analyze the impact of power outage on base stations. Integration of the two evaluations yielded the total number of affected base stations and the distribution of malfunctioning base stations in a city or county. The 2016 Meinong earthquake and the 2018 Hualien earthquake were selected as case studies to verify the analytical results. The predictions exhibited acceptable agreement with disaster investigation reports from the two earthquake events. The proposed methodology enables a prompt evaluation of the seismic impact on mobile communication facilities, which could help governments in allocating rehabilitation resources during emergency response or disaster mitigation planning.
Recurrent Adversarial Video Prediction Network

Abstract Mining the intrinsic information of sequential data to predict the future data has a promising research prospect. Considering the temporal features of sequential data, existing approaches generally adopt recurrent neural network and its variants for the prediction. However, for sequences with complex structure, such as video frame sequence, these approaches cannot guarantee to obtain promising prediction results. In this paper, to address the above issue, we propose a novel architecture, called recurrent adversarial video prediction network (RAVPN), which can not only extract the temporal and spatial features of video sequences, but also optimize the generator and discriminator based on the adversarial strategy. Specifically, we use sliding windows with length t +1 and set the ( t + 1)-th frame as the label of its previous t frames. The generator takes the first t frames as input and tries to generate the ( t + 1)-th frame, while the discriminator distinguishes whether a sample is real or fake to boost the performance of the generator. Experimental results show that our novel RAVPN can obtain a promising performance on video prediction tasks compared with other deep sequence prediction models.
Simulation of Entanglement Generation and Distribution - Towards Practical Quantum Networks

Quantum entanglement is an essential resource for quantum networks, thus generation and distribution of entanglement between remote network nodes are two important tasks faced by practical implementation of quantum networks. Given that the number and scale of real quantum network testbeds are still limited we use simulation to study entanglement generation and distribution, aiming at providing insight and guidance for quantum network construction. We extend the functionality of the open-source Simulator of QUantum Network Communication (SeQUeNCe) developed by our group to support representation of quantum state under Fock basis, and use it to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. Regarding entanglement distribution, based on the so-called continuous generation scheme, we propose an adaptive scheme that uses history request information to better guide the choice of randomly generated quantum links before receiving future requests. We demonstrate the concept and power of our adaptive scheme, and explore relevant quantum memory allocation scenarios through simulation.
A Study on Scalar Multiplication Parallel Processing for X25519 Decryption of 5G Core Network SIDF Function for mMTC IoT Environment

When 5G telecommunication becomes a standardized and widely used communication medium, it must be implemented in coherence with certain 5G network standards and requirements. One such requirement is a Subscription Concealed Identifier called SUCI. SUCI prevents the exposure of international mobile subscriber identity (IMSI), which was a vulnerability in previous generation mobile telecommunication networks. Unlike IMSI, SUCI is encrypted and transmitted using a symmetric key cryptographic algorithm, to prevent the aforementioned vulnerabilities. However, for the first terminal to be encrypted, it is necessary to exchange a key with the home network, and this key exchange for SUCI encryption is performed through the Elliptic Curve Integrated Encryption Scheme (ECIES) key exchange algorithm, which is a public-key encryption scheme. However, ECIES uses more computing resources compared to a symmetric key cryptographic algorithm. Additionally, for 5G Subscriber Identity Deconcealing Function (SIDF) to satisfy the massive machine-type communication (mMTC) requirements of 5G, it is necessary to decrypt at least a million SUCIs within a short time. This puts a great burden on the 5G home network to provide the mMTC service for IoT. Therefore, in this paper, we propose a method of constructing 5G SIDF in an mMTC IoT environment. A key method of the proposed 5G SIDF configuration is the use of GPUs. This proposal was aimed at reducing the load in the mMTC environment by performing parallel processing of all cryptographic operations performed in the SIDF using a GPU. In particular, we focused on parallelization of public-key encryption algorithms. In addition, we also compared the method proposed in this paper through a survey of various 5G security products.
DeepOLA: Online Aggregation for Deeply Nested Queries

With the advent of data-driven operating model, deriving useful insights from big data analysis has become very important. But the ever-increasing volume of data has made obtaining insights at "rates resonant with the pace of human thought" [4] more challenging. Online Aggregation (OLA) [3] is a technique that tries to counter this by incrementally improving the query result estimates and allowing the user to observe the query progress as well as control its execution on the fly. OLA provides the user with an approximate estimate of the query result as soon as it has processed a small portion (hereafter referred to as a partition) of the data. The user based on their latency-error trade-off requirements can choose to stop the execution of the query.
Fault Analysis of a 110kV Oil Immersed Current Transformer

In view of an explosion accident of 110 kV oil immersed current transformer, the current transformer was disassembled, inspected and restored, and the explosion accident caused by the disconnection of the grounding of the end screen of the current transformer was confirmed. Therefore, the corresponding treatment and preventive measures are put forward to provide technical reference for equipment manufacturers, maintenance and operation and maintenance personnel to analyze and deal with the defects of oil immersed current transformer.
A modified Incremental Density Based Clustering Algorithm

Cluster analysis, generally known as clustering, is a technique for separating data into groups (clusters) of similar objects. Except if the system is completely retrained, traditional clustering classifiers will be unable to learn new information and knowledge (attributes, examples, or classes). Only incremental learning, which outperforms when new data objects are introduced into an existing database, can solve this problem. These evolutionary strategies are applied to dynamic databases by updating the data. We’ll choose to study the Incremental Density-Based Spatial Clustering of Applications with Noise algorithm because of its capacity to discover arbitrary clusters and identify noise. In this study, a modified version of the Incremental Density Based Clustering Algorithm using an Adaptive Median Filtering Technique was used. The difference between our previous proposed AMF-IDBSCAN and the proposed algorithm developed in this work is in the evaluation performance stage. The key idea consists of a database change in the case of introducing new data items to an existing database in order to improve performance. We conducted several experiments on benchmark and synthetic data collected from the University of California Irvine repository in terms of the Generalized Dunn Index, Davies Bouldin Index, and change of time (milliseconds) with the increment of data in the original database. Experiments with datasets of various sizes and dimensions show that the proposed algorithm enhances clustering when compared to several current incremental well-known techniques.
Solving the search-LWE problem over projected lattices

The learning with errors (LWE) problem is one of the hard problems assuring the security of modern lattice-based cryptography. Kannan's embedding can reduce Search-LWE, the search version of LWE, to a specific case of the shortest vector problem (SVP). Lattice basis reduction is a powerful instrument for solving lattice problems including SVP. We propose a new way for efficiently solving Search-LWE. While a whole basis is reduced in a standard way, ours reduces only a projected basis. To realize our strategy, we also provide an algorithm for reducing projected bases, based on DeepBKZ that is an enhancement of the block Korkine–Zolotarev (BKZ) algorithm. Moreover, we show implementation results for solving some instances within the Darmstadt LWE challenge.
DGIC: A Distributed Graph Inference Computing Framework Suitable For Encoder-Decoder GNN

research-article Share on DGIC: A Distributed Graph Inference Computing Framework Suitable For Encoder-Decoder GNN Authors: Zeting Pan National University of Defense Technology, China National University of Defense Technology, ChinaView Profile , Junsheng Chang National University of Defense Technology, China National University of Defense Technology, ChinaView Profile Authors Info & Claims ICIAI 2022: 2022 the 6th International Conference on Innovation in Artificial Intelligence (ICIAI)March 2022 Pages 148–153https://doi.org/10.1145/3529466.3529493Published:04 June 2022Publication History 0citation78DownloadsMetricsTotal Citations0Total Downloads78Last 12 Months78Last 6 weeks8 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
Representative Query Results by Voting

Traditional query answering returns all answers T to a given query. When T is large, the user may be interested in viewing only a smaller subset S of T. Previous work has focused on finding subsets S that are diverse, i.e., such that all items s,s' in S are very different one from another. This paper focuses on a complementary problem, namely finding subsets that are highly representative of the entire set of query results.
Fall Accident Detection System for Bicycle Riders using Support Vector Machines

Bicycle riders have a very high impact on accident injuries when compared to motorists. Indian road accident report-2019 shows an increase in the fatal rate of 14% accidents to bicyclists when compared to 2018. Accidents occurring in urban areas at night may contribute to a delay in reporting the accidents to emergency centers. Therefore, a fast system development is essential to detect and notify the accidents to the nearest health care centers. This article proposes a compact design of hardware to fulfill the necessity. This hardware module is responsible to measure the features related to bicycle riding and fall accidents. For measuring the features, a magnetic, angular rate, gravity (MARG) sensor-based system is used. It consists of an accelerometer, gyroscope, and magnetometer for measuring the signals like acceleration, angular velocity, etc. Here, the popular machine learning technique called the support vector machines (SVM) algorithm is used for the detection of fall accidents. Simulation results are compared with the existing system to illustrate the improved accuracy of 95% of the fall accidents for bicycle riders.
Data-driven performance evaluation of a low-cost seismograph

The design and implementation of a performance protocol for local seismicity monitoring, is presented. A low-cost seismograph was installed in an area of high seismic activity in Evgiros, Lefkada Island, Greece, collocated with a high resolution 24-bit digitizer equipped with a broadband seismometer. A testing list of 28 local events with different epicenters and magnitudes has been compiled while acquisition data from the conventional seismograph and the proposed one were analyzed. Stochastic data analysis was used to compare these recordings on the same test site with different logging devices. The obtained results showed a satisfactory outcome in the performance of the proposed low-cost seismograph. Even though noise was present, P and S waves were clearly recorded with distinct amplitudes and therefore this arrival time difference, when compared with the one originating from the conventional seismograph, was found to be insignificant. Moreover, through a known magnitude equation, it manages to calculate earthquake’s local magnitude with a deviation of ± 0.4, a result that can be further improved. Lastly, spectral content analysis revealed almost identical waveforms with equivalent relative frequency distributions between the devices being compared.
Controlled Intentional Degradation in Analytical Video Systems

It is increasingly affordable for governments to collect video data of public locations. This video can be used for a range of broadly valuable analytical tasks, such as counting traffic, measuring commerce, or detecting accidents. Governments also have a range of policy goals --- preserving privacy, reducing bandwidth use, and legal compliance --- that may be obtained by degrading the video at some potential cost to analytical accuracy. Ideally, public administrators could employ controlled intentional video degradation to achieve policy goals while still obtaining the required analytical accuracy. Unfortunately, the optimal amount of induced degradation is data- and query-dependent, and so is difficult to determine even when public policy preferences are well-known. We propose a video degradation-accuracy profiling model for the problem of controlling the appropriate amount of degradation. It offers administrators a profile that illustrates the tradeoff between increased analytical accuracy and increased amounts of degradation. Computing the true tradeoff curves requires full access to the non-degraded video stream, so a primary technical contribution of this work lies in methods for accurately approximating the curves with only limited information. In addition, we propose a profile repair policy to further improve tradeoff curves' accuracy. We describe our prototype system, Smokescreen, plus experiments on two video datasets, two detection models and four aggregate query types. Compared with competing methods, we show our upper bound estimation of analytical error is up to 155% tighter, and Smokescreen enables 88% more accurate tradeoffs.
Timing leakage to break SM2 signature algorithm

SM2 digital signature algorithm (SM2-DSA) is a Chinese public key cryptography standard, which is also ISO/IEC standard. However, seldom publications show the evaluation results for combination analysis of side-channel and lattice theory. The combination attack is powerful and has been confirmed on ECDSA schemes. We target SM2-DSA to evaluate security with respect to the combinated analysis. Given the most significant zero bits, we provide the detailed deduction of inequations for lattice construction. We confirmed the validity of our deduction and also confirmed security bounder where a successful lattice attack requires at least 6 bits leakage of nonce. Our results show a greatly difference from the results gained under the least significant bits leakage. We then provide a comprehensive analysis on parameters selection of lattice reduction. Our results can be a guide for both security evaluation and secure implementation design.
RETRACTED: Design and Development of Person Re-Identification system based spatial features

Abstract Primary intention of this research is to design and develop an effectual approach for re-identifying the persons. The proposed approach involves four stages named human identification, object tracking, feature extraction, and matching. The four stages will be implemented by matching the probe videos and gallery videos. Initially, the videos will be subjected to the human identification phase. First stage will be the identification of human depending on the spatial features. It is important to consider that person re-identification will be carried out using the video surveillance data which can be probe data or gallery data. For human identification, the spatial features will be extracted and subjected to gait based Bayesian network for identifying the humans. Then, the object tracking phase will be carried out for tracking the objects from the videos. Here, the Neighborhood Search Algorithm will be used for tracking the objects. Then, the tracked objects will be given to the feature extraction process in which the features like mean, variance, speed, and motion deviations will be extracted. Finally, in matching phase, the matching will be performed between the probe videos and gallery videos and the person re-identification will be done using the extracted features based on the Bayesian probability-based matching. Finally, the matched outputs will be utilized for re-identifying the persons. The implementation of the proposed approach will be done using the Grid and Caviar Data sets. The performance of the proposed technique will be evaluated using three metrics, such as accuracy, sensitivity and specificity and will be compared with that of existing methods.
Automatic construction of a dictionary of variant forms of Chinese characters

Abstract Many Chinese characters have more than one form of writing owing to complex nature of creation and long evolvement history of writing. Most existing Chinese dictionaries list these variant forms but do not explain in a systematic way why a specific character is a variant form of another, and only list a few older key bibliographies, many of which are themselves dictionaries of various forms. In this article we present a new theory and practice of how to determine whether a Chinese character is a variant of another, and show how we can deduce a dictionary of variant characters automatically from a corpus of ancient Chinese texts totaling 2.3 billion characters with artificial intelligence techniques. Results show that in over 74,000 instances of identified variant character groups, more than 20,000 new instances are found by our algorithm. We have then compiled all the instances into a dictionary and call it Dictionary of Chinese Variant Words ( 異體字詞典 , Yiti Zi Cidian ). The key insight of our theory is to find synonymous words with variant characters. The dictionary has already been put online for several years and everyone can freely access and edit it like the way they do on Wikipedia.