Reviewerly/exHarmony huggingface dataset:
Auction-based Deep Learning Computation Offloading for Truthful Edge Computing: A Myerson Auction Approach

This paper proposes an edge computing model that processes machine learning code offloading based on auction mechanisms. The code offloading is required to carry out training that is processed difficult for mobile devices with limited computing resources. In this system, mobile devices compete for using computing resource units by submitting their bids based on code complexity and data size. We use the Myerson auction model which uses the truthful second-price auction as a baseline, to maximize the seller's revenue while meeting several desirable properties, i.e., individual rationality and incentive compatibility. The simulation results showed that our Myerson auction method overall improved the seller's revenue while satisfying the above characteristics.
VGG deep neural network compression via SVD and CUR decomposition techniques

We all know that VGG deep neural network is one of the most advanced and powerful deep learning models popular used in computer vision. However, the cost of training and serving VGG models sometimes is considerable due to the large sets of parameters. Therefore, in practice, it is necessary to provide constructive methods to compress these models, while keeping the same level of accuracy. In this paper, we study on the use of SVD and CUR decomposition techniques to compress the VGG models, and compare them with the original VGG deep neural networks on the image classification problems. Experimental results, conducted in three image datasets MNIST, FASHION MNIST, and CIFAR10, show that although the number of parameters of the compressed models is much smaller than the number of parameters of the original VGG models, the accuracy performances of the compressed models are competitive to the original ones. Even, the proposed compression with CUR performs better than the one with SVD. Moreover, it is noteworthy to see the training times of all compressed models are obviously faster than the training times of the original VGG models.
Application of Homomorphic Encryption in Blockchain Data Security

With the continuous development of the Internet era, people's demand for network security is increasingly high, information is an essential component of the network, which implies numerous privacy, secrets, but also contains a large number of value, thus generating a multiparty trust issues of data security. Blockchain technology itself is still in the early stages of rapid development, the existing blockchain system in the design and implementation of the use of distributed systems, cryptography, game theory, network protocols and many other disciplines, for learning the principles and practical applications have brought considerable challenges. The blockchain, with its decentralized quality, quickly captures the attention of the public and solves many security problems derived from data security, thus attracting wide attention from people. In recent years, along with the increasing maturity of homomorphic encryption technology and push new, it is more and more people's favour and attention.
Video Search with Sub-Image Keyword Transfer Using Existing Image Archives

This paper presents details of our frame-based Ad-hoc Video Search system with manually assisted querying that will be used for the Video Browser Showdown 2021 (VBS2021). The main contributions of our new system consist of an improved automatic keywording component, better visual feature vectors which have been fine-tuned for the task of image retrieval, and an improved visual presentation of the search results. Additionally, we use a more powerful joint textual/visual search engine based on Lucene, which can perform a search according to the temporal sequence of textual or visual properties of the video frames.
Combating Online Hate: A Comparative Study on Identification of Hate Speech and Offensive Content in Social Media Text

This paper addresses the important issue of rising hate and offensive comments against individuals or communities on social media. Such behaviour has become pervasive in social media where people are easily able to vent out their hatred and reach out to a large number of people, which they may not consider in the physical world. One of the most effective solution for tackling this enigmatic problem is the use of computational techniques to identify such hateful and offensive content and to take action against it. The current work focuses on detecting hate speech and offensive content in Indo-European languages keeping English on the frontline since it is the most widely used language on the Internet. The datasets used for the experiment are obtained from CrowdFlower and FIRE-2019 task on Identifying Hate Speech and Offensive Content in Social Media Text (HASOC). The paper provides a comparative analysis and explores the effectiveness of the TF-IDF approach and various word embedding-based approaches for the classification task on both the datasets. The evaluation measures are accuracy, precision, recall and F1-score.
Steve Reich’s Signature Rhythm and an Introduction to Rhythmic Qualities

Abstract The rhythm of Steve Reich’s Clapping Music (1972) features in so many of his pieces that it can be understood as a rhythmic signature. A theory of rhythmic qualities allows us to identify the signature rhythm’s significant features and relate it to other cyclic rhythms like the Central/West African “standard pattern,” from which it probably originates. Rhythmic qualities derive from the discrete Fourier transform, whose mathematical properties make the theory particularly robust. One property, described by the convolution theorem, predicts the effects of Reich’s diverse rhythmic canons. I apply the theory to Music for Pieces of Wood (1973) and Nagoya Marimbas (1994).
Heterogeneous Mini-Graph Neural Network and Its Application to Fraud Invitation Detection

Effectively detecting the fraudulent invitations is valuable for many online Internet enterprises such as iQIYI to promote good products and improve the user experience. However, it remains highly non-trivial to address, which mainly lies in two challenging data characteristics. First, the invitation graph structure is globally large yet locally small, as a large number of invitations usually occur in a very small local graph, making the global and local consistency difficult to achieve simultaneously. Secondly, the user associations are heterogeneous and diverse, as the user associations are from multiple different data resources, making the effects of multiple user associations difficult to use effectively. To this end, this paper proposes a novel heterogeneous graph neural network HmGnn, to detect fraudulent invitations at iQIYI platform. To the best of our knowledge, this is the first attempt to study fraud invitation detection via graph neural networks. HmGnn handles the homogeneity and heterogeneity of networks simultaneously. Specifically, the proposal constructively introduces links between homogenous mini-graphs based on the similarity of mini-graphs, facilitating the impact of local mini-graphs to the global graph structure. In addition, this paper presents a heterogeneous attention convolution network to accurately optimize the contribution of multiple heterogeneous user associations. Extensive experiments conducted on real-world business data validate the excellent effectiveness and improvement on risk management of our method.
Fully Convolutional Network Variations and Method on Small Dataset

Fully Convolutional Network (FCN) labels each pixel in an image with its category by up-sampling convolutional layer to the exact shape of input image. This paper presents a detailed evaluation on Fully Convolutional Network variations and method on small dataset. The paper mainly discusses three FCN models based on VGG16, containing FCN-32s, FCN-16s and FCN-8s, which are different in their up-sample multiple and process of fusing skipped layers. FCN based on ResNet and vanilla Convolutional Neural Network (CNN) are discussed as well for comparative experiment. Because of the small dataset, FCN method is quite different from the general, therefore arguments containing kernel size and up-sample method are tuned to increase accuracy for each kind of model. Arguments with highest accuracy are picked for comparative experiment among different kinds of model, which are FCN based on VGG16, ResNet and vanilla CNN. Mean Intersection over Union (mIoU) metric is computed as well to contrast segmentation performance among models and among classes. Loss, accuracy and mIoU after 300 epochs of training are compared. optimize processes of models are recorded to evaluate converge trend. Among all models implemented in our experiment, FCN-8s stands out, reaching the accuracy of 86.79% after 300 epochs, only by training a small dataset including 367 train images and 101 test images.
Construction of a Logical-Algebraic Corrector to Increase the Adaptive Properties of the ΣΠ-Neuron

In this paper, we consider the problem of constructing a correction algorithm with the aim of increasing the adaptive properties of the ΣΠ-neuron, relying solely on the structure of the ΣΠ-neuron itself. To build the corrector, the logical-algebraic method of data analysis is used. Comparison of the advantages of the neural network approach and the logical-algebraic method suggests that a combined approach to the organization of the neural network improves its efficiency and allows one to build a set of rules that reveal hidden patterns in a given subject area, thus improving the quality of the recognition system.
Dependable Neural Networks for Safety Critical Tasks

Neural Networks are being integrated into safety critical systems, e.g., perception systems for autonomous vehicles, which require trained networks to perform safely in novel scenarios. It is challenging to verify neural networks because their decisions are not explainable, they cannot be exhaustively tested, and finite test samples cannot capture the variation across all operating conditions. Existing work seeks to train models robust to new scenarios via domain adaptation, style transfer, or few-shot learning. But these techniques fail to predict how a trained model will perform when the operating conditions differ from the testing conditions. We propose a metric, Machine Learning (ML) Dependability, that measures the network’s probability of success in specified operating conditions which need not be the testing conditions. In addition, we propose the metrics Task Undependability and Harmful Undependability to distinguish network failures by their consequences. We evaluate the performance of a Neural Network agent trained using Reinforcement Learning in a simulated robot manipulation task. Our results demonstrate that we can accurately predict the ML Dependability, Task Undependability, and Harmful Undependability for operating conditions that are significantly different from the testing conditions. Finally, we design a Safety Function, using harmful failures identified during testing, that reduces harmful failures, in one example, by a factor of 700 while maintaining a high probability of success.
Show or suppress? Managing input uncertainty in machine learning model explanations

Feature attribution is widely used in interpretable machine learning to explain how influential each measured input feature value is for an output inference. However, measurements can be uncertain, and it is unclear how the awareness of input uncertainty can affect the trust in explanations. We propose and study two approaches to help users to manage their perception of uncertainty in a model explanation: 1) transparently show uncertainty in feature attributions to allow users to reflect on, and 2) suppress attribution to features with uncertain measurements and shift attribution to other features by regularizing with an uncertainty penalty. Through simulation experiments, qualitative interviews, and quantitative user evaluations, we identified the benefits of moderately suppressing attribution uncertainty, and concerns regarding showing attribution uncertainty. This work adds to the understanding of handling and communicating uncertainty for model interpretability.
Object detection algorithm based on improved Yolov3-tiny network in traffic scenes

The object detection based on deep learning is an important application in the field of vehicle environment perception, which has been a hot topic in recent years. We propose a novel improved Yolov3-tiny to implement more accurate object detection for the objects in traffic scenes. We employ K-means algorithm to cluster the common objects in traffic scenes to obtain a suitable size and numbers of anchor box. In addition, we modify modifying detection scale and the backbone network structure of Yolov3-tiny, improving the detection accuracy for small object. The stereo vision is also introduced to improve the accuracy of boundary location. Experiments results demonstrate that the improved yolo-tiny has higher accuracy than the original algorithm and it also meet the requirement of real-time performance.
Two Efficient Constructions for Biometric-Based Signature in Identity-Based Setting Using Bilinear Pairings

It is always an attractive topic for researchers that biometric traits are used to represent the identities of users in identity-based cryptography due to their unique and inherent properties. Fuzzy identity-based signature (FIBS) and biometric identity-based signature (BIO-IBS) are both promising cryptographic primitives, where a signature generated with a private key of an identity can be verified by another identity if and only if the distance between these two identities is within a certain threshold. In this paper, we design two novel constructions for biometric-based signature in identity-based setting over elliptic curve groups, namely BioFIBS and Bio-IBS. The combination of elliptic curve cryptography (ECC) technique with biometric-based IBS integrates the advantages of both so that the proposed schemes are performed in a more efficient and practical manner for resource-constrained applications. This is confirmed by the comparison with the existing schemes in terms of computational and communicational efficiency. In addition, we prove that both our schemes satisfy the security requirements including correctness and unforgeability that is guaranteed in the random oracle model against the adaptively chosen message and identity attack under the computational Diffie-Hellman assumption.
Automated Detection of SQL Injection Attack on Blockchain-Based Database

In the era of computing, where the data are stored in a cloud or distributed environment, the privacy of data is one of the challenging tasks. The attacks like denial of service attacks (DoS), insider attack compromised the security of the system. In this chapter, the authors discussed a blockchain-based database, where data are encrypted and stored. The Web API is used as an interface for the storage and sharing of data in the blockchain system. There are several types of attacks that are performed by the adversary on the database to destroy the vulnerability of the system. Here, the authors are mainly focused on the SQL injection attack which is performed by the adversary on Web API. To cope with this problem, they present the case study based on the Snort and Moloch for automated detection of SQL attack, network analysis, and testing of the system.
On sampling error in genetic programming

Abstract The initial population in genetic programming (GP) should form a representative sample of all possible solutions (the search space). While large populations accurately approximate the distribution of possible solutions, small populations tend to incorporate a sampling error. This paper analyzes how the size of a GP population affects the sampling error and contributes to answering the question of how to size initial GP populations. First, we present a probabilistic model of the expected number of subtrees for GP populations initialized with full, grow, or ramped half-and-half. Second, based on our frequency model, we present a model that estimates the sampling error for a given GP population size. We validate our models empirically and show that, compared to smaller population sizes, our recommended population sizes largely reduce the sampling error of measured fitness values. Increasing the population sizes even more, however, does not considerably reduce the sampling error of fitness values. Last, we recommend population sizes for some widely used benchmark problem instances that result in a low sampling error. A low sampling error at initialization is necessary (but not sufficient) for a reliable search since lowering the sampling error means that the overall random variations in a random sample are reduced. Our results indicate that sampling error is a severe problem for GP, making large initial population sizes necessary to obtain a low sampling error. Our model allows practitioners of GP to determine a minimum initial population size so that the sampling error is lower than a threshold, given a confidence level.
A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

Recognition of age and gender has become a significant part of the biometric system, protection, and treatment. It is widely used for people to access age-related content. It is used by social media in the distribution of layered advertising and promotions to expand its scope. Application of face detection has grown to a great extent that we should upgrade it using various methods to achieve more accurate results. In this paper, we have developed a lightweight deep Convolution neural network model for real-time age and gender prediction. For making the training dataset more diverse, Wiki, UTKFace, and Adience datasets have been merged into one containing 18728 images. Using this vast mixed dataset, we have achieved accuracy of 48.59% and 80.76% for age and gender respectively. Further, the model is tested in real-time. Different experimental investigations on the prepared dataset show that with most recent approaches, our model provides competitive prediction accuracy.
Survey of Performance Measurement Indicators for Lossless Compression Technique based on the Objectives

The tremendous growth of data in the digital world has led to the need for efficient techniques for storing and transmitting data. Performing the Compression action aims to reduce the size of the data either permanently or temporarily. Not only size is a consideration but also to save time in digital processing, for example when transmitting or archiving data. Benefits and compression processes are needed to optimize storage media and bandwidth, so many techniques are developed to achieve certain goals and needs. To analyze the compression results based on the goal which will affect the main parameters that will be This main objective was carried out to address current needs in maintaining data originality, coding, classification and Performance Measurement. All in all, this paper provides insights into various developing issues and diverse research objectives in various fields and topics that are promising for future development and clarify the indicators that are the main norms for conducting research in the scope of lossless compression.
Predictive Models for Popularity of Solo and Group Singers in Spotify Using Decision Tree

Technology has revolutionized our world over the years, and music is one of them. Spotify had become one of the music streaming applications that offers the latest songs in every year. Users can download Spotify from Play Store, App Store, and the official website. Spotify is a music streaming platform that provides a Freemium service, which means it is available for free use and a paid premium. Users can browse the most famous artists or songs easily according to their ratings. The artist, lyric, and some of the audio features have a significant impact on making the song's popularity. The purpose of this research is to predict a soloist or group singers using a decision tree algorithm. We used weighted by information gain method to determine the most crucial feature in processing singers' popularity. Our model will help the solo singer, group singers, and song composers make their songs accessible. The best performance result is the Decision Tree Algorithm with an Accuracy of 89.5% on cross-validation and 92.3% on independent data.
Up and Down Residual Blocks for Convolutional Generative Adversarial Networks

Most recent existing image generation methods have made great progress in creating high-quality images, mainly focusing on improving the generator or discriminator of convolutional generative adversarial networks (GANs). In this paper, we propose up and down residual blocks for convolutional GANs, dubbed upResBlock and downResBlock respectively. This structure is based on deconvolutions, strided convolutions, and residual blocks. With the upResBlock module for the generator of convolutional GANs, our method can further enhance the generative power of the feature extraction while synthesizing image details for the specified size. With the downResBlock module for discriminator combined with upResBlock for generator, the proposed method can speed up the back propagation of gradient and doesn't suffer from the vanishing or exploding gradients problems, generating more realistic images as well. Extensive experiments demonstrate that the proposed up and down residual blocks can help convolutional GANs in generating photo-realistic images. In addition, our method shows its universality for the improvement of existing methods.
GNSS Intentional Interference Mitigation via Average KF Innovation and Pseudo Track Updation

GNSS based navigation is vulnerable to intentional interferences like jamming, meaconing, and spoofing. In the jamming attack, there is no position estimate from the navigation filter. Whereas, in the case of meaconing and spoofing attacks, the navigation track is misguided by projecting the false measurements onto the GNSS receiver. In the above two cases, the measurements are either corrupted by jamming or spoofed. Therefore, to address this problem, this paper presents a novel pseudo-state update and pseudo-covariance update equations for the Kalman filter (KF) at a given epoch to improve the performance of GNSS track. Simulation results reveals that proposed method enhanced the navigation accuracy (by including two adders, two flip flops and a control unit) compared to other traditional methods. Further, it is evident from the results that the proposed approach provided improved performance in track continuity and position root mean square error(PRMSE) compared to existing techniques.