Reviewerly/exHarmony huggingface dataset:
Network Cooperation with Progressive Disambiguation for Partial Label Learning

Partial Label Learning (PLL) aims to train a classifier when each training instance is associated with a set of candidate labels, among which only one is correct but is not accessible during the training phase. The common strategy dealing with such ambiguous labeling information is to disambiguate the candidate label sets. Nonetheless, existing methods ignore the disambiguation difficulty of instances and adopt the single-trend training mechanism. The former would lead to the vulnerability of models to the false positive labels and the latter may arouse error accumulation problem. To remedy these two drawbacks, this paper proposes a novel approach termed "Network Cooperation with Progressive Disambiguation" (NCPD) for PLL. Specifically, we devise a progressive disambiguation strategy of which the disambiguation operations are performed on simple instances firstly and then gradually on more complicated ones. Therefore, the negative impacts brought by the false positive labels of complicated instances can be effectively mitigated as the disambiguation ability of the model has been strengthened via learning from the simple instances. Moreover, by employing artificial neural networks as the backbone, we utilize a network cooperation mechanism which trains two networks collaboratively by letting them interact with each other. As two networks have different disambiguation ability, such interaction is beneficial for both networks to reduce their respective disambiguation errors, and thus is much better than the existing algorithms with single-trend training process. Extensive experimental results on various benchmark and practical datasets demonstrate the superiority of our NCPD approach to other state-of-the-art PLL methods.
Capsule Network based End-to-end System for Detection of Replay Attacks

Automatic speaker verification systems are prone to various spoofing attacks. The convolutional neural networks are found to be effective for detection of spoofing attacks. However, they lack spatial information and relationship of low-level features with the pooling layer. On the other hand, capsule networks use vectors to record spatial information and the probability of presence simultaneously. They are known to be effective for detection of forged images and videos. In this work, we study capsule networks for replay attack detection. We consider different input features to capsule network and study on recent ASVspoof 2019 physical access corpus. The studies suggest the proposed capsule network based system performs effectively and the performance is comparable to state-of-the-art single systems for replay attack detection.
Accelerating Deep Learning Tasks with Optimized GPU-assisted Image Decoding

In computer vision deep learning (DL) tasks, most of the input image datasets are stored in the JPEG format. These JPEG datasets need to be decoded before DL tasks are performed on them. We observe two problems in the current JPEG decoding procedures for DL tasks: (1) the decoding of image entropy data in the decoder is performed sequentially, and this sequential decoding repeats with the DL iterations, which takes significant time; (2) Current parallel decoding methods under-utilize the massive hardware threads on GPUs. To reduce the image decoding time, we introduce a pre-scan mechanism to avoid the repeated image scanning in DL tasks. Our pre-scan generates boundary markers for entropy data so that the decoding can be performed in parallel. To cooperate with the existing dataset storage and caching systems, we propose two modes of the pre-scan mechanism: a compatible mode and a fast mode. The compatible mode does not change the image file structure so pre-scanned files can be stored back to disk for subsequent DL tasks. In comparison, the fast mode crafts a JPEG image into a binary format suitable for parallel decoding, which can be processed directly on the GPU. Since the GPU has thousands of hardware threads, we propose a fine-grained parallel decoding method on the pre-scanned dataset. The fine-grained parallelism utilizes the GPU effectively, and achieves speedups of around 1.5× over existing GPU-assisted image decoding libraries on real-world DL tasks.
Experience of Teaching a Course on Software Engineering Principles Without a Project

This paper summarizes an experience in designing and delivering a course "Software Development Essentials", a second year computer science course designed to teach software engineering skills without a project component. This paper describes the motivation, design, and implementation of the course.
Performance evaluation of a solution for composite service selection problem with multiple consumers

In recent years, building business applications from independently developed services has become one of the current trends in service computing. To satisfy clients’ requirements, service composition is performed to compose the various capabilities of available services. With the proliferation of services having similar functionalities, assessing the quality of a given composition is a paramount factor to decide which services must be selected, or to choose whether a given composition can provide the requested QoS (Quality of Service). However, due to the fluctuating conditions in the dynamic cloud computing environment, the QoS and the performances of the services become unreliable, and therefore call into the question the accuracy of the composition QoS. To tackle this issue, different methods that analyse the QoS have been developed, making it possible to help the designers to first, understand the system behaviour when providers and consumers, are interacting, thus allowing to optimize the system by identifying performance bottlenecks within a specified deployment environment. In this study, the particular problem of many consumers that are competing to acquire services with same functionalities but with different QoS, is considered. For this purpose, assuming a dynamic environment, different models based on Discrete Time Markov Chain are developed to implement several policies of the system. A theoretical modelling of the problem is proposed including analytical results obtained using the Xborne tool. For each model, the average time to reach a given QoS for a community of consumers is reported. Such a performance metric allows the designer to predict the system behaviour in a dynamic environment.
Research on the Innovation and Reform of Art Education and Teaching in the Era of Big Data

With the continuous development of contemporary social economy, the Internet is widely used in various industries, which provides a strong basis for all walks of life in the new era. At the same time, the field of education industry has also received great attention, the most important of which is the art education industry. In the field of fine arts, art teachers need to give up the traditional art teaching mode taught in the past. As a relatively basic industry of education industry, art education major will meet the new development under the situation of big data information and data. For example, the creation of fine arts focuses on the public aesthetic and observation objects; more unique and independent artistic creation concepts such as projection, photography and copying need to be re-created; The improvement of the teaching mode of traditional historical concept makes the new art form and the field of information data mode integrate, and then shows the advantages of information-based data art education, which lays a good foundation for the cultivation of high-quality talents in China's art education. In the era of big data information technology, we can find the method suitable for the current art education field, change the original traditional teaching mode, open up a new road for the art education industry, and make great progress in the field of art education. This paper gives some suggestions to the art education industry in the information age, and explores the relationship between the information age and art education from the two aspects of the characteristics of the art education field in the information age.
Configurational Entropy for Optimizing the Encryption of Digital Elevation Model Based on Chaos System and Linear Prediction

A digital elevation model (DEM) digitally records information about terrain variations and has found many applications in different fields of geosciences. To protect such digital information, encryption is one technique. Numerous encryption algorithms have been developed and can be used for DEM. A good encryption algorithm should change both the compositional and configurational information of a DEM in the encryption process. However, current methods do not fully take into full consideration pixel structures when measuring the complexity of an encrypted DEM (e.g., using Shannon entropy and correlation). Therefore, this study first proposes that configurational entropy capturing both compositional and configurational information can be used to optimize encryption from the perspective of the Second Law of Thermodynamics. Subsequently, an encryption algorithm based on the integration of the chaos system and linear prediction is designed, where the one with the maximum absolute configurational entropy difference compared to the original DEM is selected. Two experimental DEMs are encrypted for 10 times. The experimental results and security analysis show that the proposed algorithm is effective and that configurational entropy can help optimize the encryption and can provide guidelines for evaluating the encrypted DEM.
Face recognition systems based on Neural Compute Stick 2, CPU, GPU comparison

The most common approaches to face recognition are considered, the latest developments in this area are analyzed, an experiment is carried out on a face recognition system on the Neural Compute Stick 2, the conditions for its necessity are substantiated. The system also was tested on CPU and GPU, as well as system models separately to determine the conditions for increasing performance. The main result is the determination of the need for Neural Compute Stick 2 on personal computers, as well as the optimal conditions for the performance of the face recognition system. The need to use the Neural Compute Stick 2 only for low-performance devices is revealed. The tasks of further research are defined.
Exploring the Mineralogy at Lonar Crater with Hyperspectral Remote Sensing

The earth crust is made up of variety of minerals. These minerals are having very significant applications in our day today life. The various studies, characterizing physical, chemical, electrical, structural properties, have been carried out on the Lonar crater for studying mineralogy, surface morphology and geology but has not been done by remote sensing technology. So, the proposed work focused on exploring the mineralogy at the Lonar crater by using high resolution hyperspectral imageries. The spectral reflectance of minerals was characterized by using FieldSpec4 spectroradiometer. The minerals at Lonar crater were explored by performing preprocessing and spectral analysis. The techniques used in the work are Spectral Angle Mapper and Spectral Feature Fitting. The results of the work marked the presence of pigeonite and augite at Lonar crater which indicates that this crater is the result of extrusive volcanic activity. Also, the presence of augite underneath basaltic igneous rocks as the rock type of Lonar crater. The salinity of the Lonar lake is proved by the presence of mirabilite and salt. Thus, the important results of this work are presence of minerals quartz, actinol, jarosite, pigeonite, augite, albite, mirabilite and scolecite. The significance of these minerals related to the crater is discussed here. It also validated the existence of these minerals which were identified through the previous geological, chemical, physical, electrical and magnetic studies and the minerals pyrite, chlorite, richter, illite, limonite, allanite, amphibolite and margarite were also identified.
Competency test clustering through the application of Principal Component Analysis (PCA) and the K-Means algorithm

Abstract The implementation of the Competency Test at the LSP institution in higher education is an effort to ensure that students have abilities in certain fields according to predetermined competency standards. Education providers are required to always strive to improve the quality and quality of education with the aim that the student’s academic performance will always improve. From the results of observations made in the research location, it was found a problem with the high number of failures in the implementation of the competency test. This study aims to conduct cluster analysis of the data from the implementation of competency tests using Machine Learning techniques through the application of Principal Component Analysis (PCA) and K-Means Algorithm, through several stages in the form of data collection, data cleaning, data transformation, data modeling and experimentation. This study resulted in grouping the results of competency tests which were divided into 3 clusters, namely cluster 1 as much as 38%, cluster 2 as much as 32% and cluster 3 as much as 30%.
Constant-Time Gaussian Filtering for Acceleration of Structure Similarity

In this paper, we propose an acceleration method of structural similarity (SSIM) and its multi-scaled version, called MS-SSIM. The calculation process of SSIM and MS-SSIM includes multiple Gaussian filters, and the cost of the filter is dominant for the entire process; thus, to accelerate SSIM/MS-SSIM, we replace Gaussian filtering using convolution with sliding DCT. Gaussian filter based on sliding DCT is faster than the usual convolution method. Besides, its computational complexity does not depend on the filter window length. Also, naive implementations of SSIM and MS-SSIM scan image many times for the pixel-wise operation; however, these operations can be incorporated into Gaussian filtering. Thus, we optimize the processing pipeline to achieve high cache-efficiency. As a result, the proposed SSIM computation was accelerated by 6.36 times and MS-SSIM by 8.11 times faster than the conventional approach.
Quantifying non-Markovianity via conditional mutual information

In this paper, we study measures of quantum non-Markovianity based on the conditional mutual information. We obtain such measures by considering multiple parts of the total environment such that the conditional mutual information can be defined in this multipartite setup. The benefit of this approach is that the conditional mutual information is closely related to recovery maps and Markov chains; we also point out its relations with the change of distinguishability. We study along the way the properties of leaked information which is the conditional mutual information that can be back flowed, and we use this leaked information to show that the correlated environment is necessary for nonlocal memory effect.
Estimating Mutual Information in Prosody Representation for Emotional Prosody Transfer in Speech Synthesis

An end-to-end prosody transfer system aims to transfer the speech prosody from one speaker to another speaker. One major application is the generation of emotional speech with a new speaker's voice. The end-to-end system uses an intermediate representation of prosody, which encompasses both speaker and emotion related information. The present study tackles the problem of estimating the mutual information between emotion and speaker-related factors in the prosody representation. A mutual information neural estimator (MINE) which could measure the mutual information between high-dimensional continuous prosody embedding and discrete speaker/emotion label is applied. The experimental results show that: 1) the prosody representation generated by the end-to-end system indeed contains both emotion and speaker information; 2) The mutual information would be determined by the type of input acoustic features to the reference encoder; 3) normalization for the log F0 feature is very effective in increasing emotion-related information in the prosody representation; 4) adversarial learning can be applied to reduce speaker information in the prosody representation. These results are useful to the further development of an optimal and practical emotional prosody transfer systems.
Seismic phase picking in China Seismic Array using a deep convolutional neuron network*

Seismic phase picking is the preliminary work of earthquake location and body-wave travel time tomography. Manual picking is considered as the most accurate way to access the arrival times but time consuming. Many automatic picking methods were proposed in the past decades, but their precisions are not as high as human experts especially for events with low ratio of signal to noise and later arrivals. As the increasing deployment of large seismic array, the existing methods can not meet the requirements of quick and accurate phase picking. In this study, we applied a phase picking algorithm developed on the base of deep convolutional neuron network (PickNet) to pick seismic phase arrivals in ChinArray-Phase III. The comparison of picking error of PickNet and the traditional method shows that PickNet is capable of picking more precise phases and can be applied in a large dense array. The raw picked travel-time data shows a large variation deviated from the traveltime curves. The absolute location residual is a key criteria for travel-time data selection. Besides, we proposed a flowchart to determine the accurate location of the single-station earthquake via dense seismic array and phase arrival picked by PickNet. This research expands the phase arrival dataset and improves the location accuracy of single-station earthquake.
To Carve out Private Cloud with Total Functionality

Cloud storage as well as computation both are consistently suffering from security leakage. The main problem of the cloud users is to keep secure their sensitive data. The market of cloud fell off due to security breaches in various attacks on cloud in 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. Myriad of users are there who must adopt cloud services but they are still in ambivalence whether to cloudify or not being scary of security breaches. Since cloud users have to pay for each unit of time per computation and per bit per unit time of storage then why not to develop your own private cloud if storage and computation are already available. In this research paper the design and development of a fully secured and private cloud is proposed to get rid from the ongoing and long-lasting problem of security and to store the data within the boundary of parent organization.
Hiding sensitive association rules using the optimal electromagnetic optimization method and a dynamic bit vector data structure

Hiding the association rules is one of the methods used to protect sensitive information in data-mining processes. Its goal is to transform the original dataset so that the support for, or the reliability of, sensitive rules is reduced below the minimum threshold. Then these sensitive rules cannot be exploited, while the rules that are non-sensitive can still be exploited normally. Many methods have been proposed for hiding the association rules. However, most of these methods are very slow and consume a large amount of storage space. Consequently, they are not suitable when mining large datasets. Recently, the electromagnetic field optimization (EFO4ARH) method was proposed, and it was found to hide the sensitive association rules better than the other methods. To increase mining efficiency further, this paper proposes a new workaround called EFODBV4ARH. This technique applies a dynamic bit vector data structure in combination with the electromagnetic field optimization method. Experimental results indicate that EFODBV4ARH is significantly more efficient than EFO4ARH.
A Blockchain-based Privacy-Preserving Recommendation Mechanism

Recommendation system is widely used to predict users' interests and provide targeted products for them, which effectively facilitates users in the era of big data where information overload problem is prevalent. Unfortunately, massive data closely related to users' privacy is in high demand to produce more accurate predictions. In this case, the collection and transmission of such data is communication costly; to process and analyze such data is of high possibility to compromise users' privacy. In this paper, we propose a privacy-preserving recommendation mechanism based on blockchain, which well addresses these problems. Leveraging the inherent advantages of blockchain, we establish a completely distributed model mitigating the risk of privacy disclosure caused by central data storage. Moreover, we combine Inter-Planetary File System with blockchain to greatly improve the communication efficiency. We also introduce local sensitive hashing and local differential privacy into proposed mechanism to reduce the computation load and provide a strong privacy guarantee. The experimental results demonstrate that the proposed mechanism shows better performance on privacy preservation while maintaining desirable recommendation accuracy when compared with the baseline.
Quantum Computer Architecture From Non-Conventional Physical Simulation Up To Encryption Cracking, Machine Learning Application, And More

Beginning with the concern of simulation of nonclassical physical theories (deal with tiny particles) that share one common feature (quantum mechanics). As well as, Shor's algorithm has given the advantage of new technology. moreover, Moore's law predicts the technology that is based on classical physics could be reached its limitations; finally, the efficiency of the performance of implementing the applications in this innovation, In particular, the drug processing that affects the human life especially in these days with coronavirus pandemic, all these facts pushed the technology to produce, develop, and use the quantum computer in various field of life, this paper discusses this fact and get the conclusion of advantage and disadvantage of this emerging technology.
A NONLOCAL LOW-RANK REGULARIZATION METHOD FOR FRACTAL IMAGE CODING

Fractal coding has been widely used as an image compression technique in many image processing problems in the past few decades. On the other hand side, most of the natural images have the characteristic of nonlocal self-similarity that motivates low-rank representations of them. We would employ both the fractal image coding and the nonlocal self-similarity priors to achieve image compression in image denoising problems. Specifically, we propose a new image denoising model consisting of three terms: a patch-based nonlocal low-rank prior, a data-fidelity term describing the closeness of the underlying image to the given noisy image, and a quadratic term measuring the closeness of the underlying image to a fractal image. Numerical results demonstrate the superior performance of the proposed model in terms of peak-signal-to-noise ratio, structural similarity index and mean absolute error.
New Quality Metrics for Dynamic Graph Drawing

In this paper, we present new quality metrics for dynamic graph drawings. Namely, we present a new framework for change faithfulness metrics for dynamic graph drawings, which compare the ground truth change in dynamic graphs and the geometric change in drawings. More specifically, we present two specific instances, cluster change faithfulness metrics and distance change faithfulness metrics. We first validate the effectiveness of our new metrics using deformation experiments. Then we compare various graph drawing algorithms using our metrics. Our experiments confirm that the best cluster (resp. distance) faithful graph drawing algorithms are also cluster (resp. distance) change faithful.