Reviewerly/exHarmony huggingface dataset:
Pornographic Image Recognition Based on Multi-Instance Deep Learning

Focus on the application requirements of social Internet (SI) pornographic image (PronIM) recognition, this paper designs a generalized region-based PronIM recognition algorithm. Firstly, based on uniform grid division and linear projection, a multi-instance automatic modeling method is designed. Secondly,a instance embedding module based on channel attention CNN is designed. Third, a bag pooling module based on spatial attention CNN is designed. Finally, the bag-level representations are fed into a multiple-layer perceptron (MLP) to predict the probability that the input image is a PronIM. Experimental results based on the collected image set show that the accuracy can reaches 97.8%, and its performance is superior to other algorithms.
Secure Partitioning of Composite Cloud Applications

The security of Cloud applications is always a major concern for application developers and operators. Protecting their users’ data confidentiality requires methods to avoid leakage from vulnerable software and unreliable cloud providers. Recently, hardware-based technologies emerged in the Cloud setting to isolate applications from the privileged access of cloud providers. One of those technologies is the Separation Kernel which aims at isolating safely the software components of applications. In this article, we propose a declarative methodology supported by a running prototype to determine the partitioning of a Cloud multi-component application in order to allow its placement on a Separation Kernel. We employ information-flow security techniques to determine how to partition the application, and showcase the methodology and prototype over a motivating scenario from an IoT application deployed to a central Cloud.
Research on Evaluation Index of Game Concentration Based on Multi-Scale Fuzzy Entropy Using Eeg

Concentration has important role to play in cognition, By finding an effective physiological characteristic index to represent the level of concentration are significant for concentration training and improve the experience. This paper proposes multi-scale fuzzy entropy (MFE) as an EEG feature to evaluate the level of concentration. First, a game platform was established to stimulate the participants' concentration, and physical indicators that reflected the participants' emotional tension were set. Secondly, a portable single-channel EEG device was used to collect the participants' EEG signals during the game. Hilbert-Huang transform, MFE and grey relational analysis were used for data processing, and a questionnaire survey was conducted on the behavioral performance of the participants. The experimental data and analysis results show that under different game speeds and different scale factors, MFE maintains a high correlation with physical indicators representing emotional stress in games, with a median of about 70%. MFE can reflect the participants' relatively short-scale emotional tension and long-scale overall game's concentration during the game, and can simultaneously represent the player's immersion and attention during the game.
Activation Function Modulation in Generative Triangular Recurrent Neural Networks

Autonomous generation of time series is challenging because the network must capture short-term features while tracking long-term time dependencies. This paper introduces the modulation of the activation function slopes of the upper-lower triangular recurrent neural networks (ULTRNNs) for dynamic variation of memory through a secondary recurrent network with its own independent states. A zigzag propagation algorithm for weight updates is proposed that accounts for the dynamic interaction of the states between the ULTRNN and the secondary network. A novel training method is proposed that distributes the eigenvalues of the closed-loop system around the unit circle in the complex z-plane to ensure that the network behaves as a nonlinear oscillator with an output that neither collapses nor saturates but continues to emulate the target. Examples encompassing the Lorenz series, Santa Fe laser data, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">kolam</i> patterns, electrocardiogram (ECG) signals, stock pricing data, and smart grid data are presented to demonstrate that the proposed approach is highly effective in the generative modeling of complex periodic, chaotic, and nonstationary time series. The qualitative and quantitative performance of the ULTRNN obtained with the proposed activation-function modulation technique is comparable to that of state-of-the-art techniques including feedforward networks and generative adversarial networks, but with far fewer trainable parameters and shorter computation times.
TAFL: Task-Adaptive Few-shot Learning for KPIs Prediction

Key performance indicators play a critical role in network monitoring and surveillance. However, explosive growth of KPIs render data collection and labeling extremely challenging. Meta-learning, which utilizes the prior knowledge of related tasks, can effectively use few samples to train the model on the target tasks and quickly adapt to them. However, when it comes to KPIs prediction, it is difficult to apply meta-learning to such problems due to the significant differences in task distribution. In this paper, we propose a task-adaptive few-shot learning (TAFL) framework for KPI prediction, which uses meta-learning to learn model initialization parameters for quickly adapting to target tasks. Specifically, we train similar tasks together by introducing expert knowledge, which enhances the robustness of the model to different tasks. Experimental results on multiple real-world and synthetic datasets demonstrate the effectiveness of the proposed method.
AI Replication of Musical Styles Points the Way to An Exclusive Rights Regime

While “style” has been considered at the margins of copyright, it may be central to what makes a work original and protectable. At the same time, an artist’s style as established over multiple works seems even more problematic for copyright’s core fixation requirement. Other misappropriation regimes such as rights of publicity or Lanham Act false affiliation/endorsement offer limited avenues to protection of style. Yet, style standing separate from any particular work has clear aesthetic and commercial value. The success of current AI music generators to use machine learning to create an algorithmic engine capable of producing musical works, performances, and recordings recognizably in the style of famous composers and performers indicates that style standing alone can be quantified and fixed. This chapter thus argues that musical style should be protectable on multiple grounds, including, if nothing else, unjust enrichment of tech companies at the expense of human artists.
DNA based Data Encryption using AES Agorithm

In the last two decades, VLSI technology has exploded in popularity, particularly in network security, where information processing cryptography has received a lot of attention. Cryptography provides a safe channel between the sender and the receiver for data transmission. Millions of online transactions, such as trading, banking, e-commerce, and social networking, take place every second around the world, exchanging data between users.As the internet has grown in popularity, so has the number of hackers and cyber attacks on networks.Network security has become a major concern in the modern era, and data protection has become so important that unbreakable encryption technology should be developed to provide data security. This paper presents a new approach to the structure of Deoxyribonucleic Acid(DNA) with four nucleotides called A (Adenine), C (Cytosine), G (Guanine) and T (Thymine) are the elements that exist in the DNA system that is added to both the key and cipher that are merged and transferred in the shape of a protein through a channel that improves protection. The Field Programmable Gate Array (FPGA) architecture and its dynamically changing behavior with partial reconfiguration are ideal for a broad range of applications that can be designed to incorporate custom designs and needs. Implemented on Verilog HDL, synthesized in Xilinx simulator.
Fingerprint Scanner Sanitising Module for Kerala Ration Shops

Fingerprint recognition is a safe and convenient technology, increasingly being used for biometric identification of the intended beneficiaries of welfare programmes. Kerala&#x2019;s pubic distribution system, with Ration Shops being its point of contact, also uses fingerprint scanners for identification of beneficiaries. The outbreak of COVID-19 has adversely affected the safety of fingerprint authentication. Touching the sensors by multiple persons can cause the transmission of viruses. Studies have shown that COVID-19 can survive on common surfaces like wood, plastic, metal, and glass for a minimum of 5 days. Despite all the standard operating procedures, it is a common sight to see people crowding at public spaces like Ration Shops that has increased the risk of transmission of the virus. In this context, the present work aims to create a safe and healthy environment for the consumers of Kerala&#x2019;s ration shops, through a UVC based self-sanitizing system for fingerprint scanners.
PUPRBLMS: Development And Acceptability of a Proposed Library Management System

Managing the library in a university or school is not an easy task especially if it has huge number of books that is open for utilization and borrowing to students. The librarian faces challenges in tasks like tracking of the books on-borrow due to uncontrolled demand more particularly during examination period or research seasons. In this study, the proponents sought to measure the acceptability of the proposed Library Management System capable of handling tasks for library management. The proposed Library Management System was primarily implemented at the Polytechnic University of the Philippines, Ragay, Camarines Sur Branch to address the challenges of the university in terms of library management and was evaluated using a survey questionnaire that was participated by 64 students, 2 library staff, and 10 faculty members for the period of January to March 2020. Evaluation shows that there is an average rate of 4.18 with a verbal interpretation of highly acceptable. Given the evaluation results, it is believed that the proposed library management system can be implemented for library management.
HCSA: A Novel Feature Selection Method based on Improved Crow Search Algorithm

As a simple and efficient swarm intelligence optimization algorithm, Crow Search Algorithm (CSA) has been widely used in many practical fields. But the traditional Crow Search Algorithm also faces the risk of falling into a local optimum when dealing with complex optimization problems, and there exists blind search phenomenon in the optimization process. In order to improve the algorithm’s ability to get rid of local optima and apply it to select the best feature subset, this paper proposes a wrapper feature selection algorithm (HCSA) based on an improved Crow Search Algorithm. Firstly, try to use different methods to initialize the population to obtain the initial population with better quality. Then, the concept of “leader” is introduced to guide the optimization to reduce blind search. The position of the leader is adjusted by the strategy of Salp Swarm Algorithm (SSA). In order to enhance the diversity, chaotic map is added in the position updating equation. In addition, the nonlinear parameter updating strategy is used to dynamically adjust the important parameter AP in the original algorithm during the iteration to achieve an effective balance between exploration and exploitation. The experimental results indicate that HCSA shows good performance in solving the feature selection problem.
Source Code Recommendation with Sequence Learning of Code Functions

For finding desired source codes and articles using existing source code search engines, it is necessary to compare them in the different results to examine which source codes are the best practice for developing the target software. In this paper, we propose a method of source code recommendation based on the prediction of code function. The feature of the proposed method is that by interpreting the context of the processing procedure of the source code, it recommends source code which complements or follow the processing procedures. In the experiment using actual source codes, we evaluate the feasibility of the proposed method.
The Impact of Input Image Data Size on The Training Speed of Convolutional Neural Networks

In using neural networks for classification tasks, the most time-consuming process is repeated training of the network. Due to the development and progress of hardware equipment may only take a few hours for a model that required hundreds of hours to train in the past [1]. To obtain more accurate results or increase prediction speed, multiple repeated experiments are often carried out during the experiment. Due to the different performance of the equipment owned by actual users, how to increase the training speed of the neural network as much as possible without affecting the final accuracy? In the process of using neural networks for classification tasks, especially in learning how to use neural networks, there is a certain importance in the network process. The main purpose of this work is to study the impact of simplified input data on training duration when training on low-performance execution devices while affecting execution performance as little as possible. The effect of reducing the amount of data in a certain dimension on the training time and results were compared during the experiment. This work shows that replacing colour images with grey images for text recognition training can effectively reduce the length of network training, and the impact on accuracy can be ignored in most cases. The results show that reducing the amount of data in the picture when the accuracy requirements are not very strict, although it will cause a certain amount of information loss, has a significant effect on shortening the training time.
On the Topological Disparity Characterization of Square-Pixel Binary Image Data by a Labeled Bipartite Graph

Given an nD digital image I based on cubical n-xel, to fully characterize the degree of internal topological dissimilarity existing in I when using different adjacency relations (mainly, comparing 2n or $$2^n-1$$ adjacency relations) is a relevant issue in current problems of digital image processing relative to shape detection or identification. In this paper, we design and implement a new self-dual representation for a binary 2D image I, called $$\{4,8\}$$ -region adjacency forest of I ( $$\{4,8\}$$ -RAF, for short), that allows a thorough analysis of the differences between the topology of the 4-regions and that of the 8-regions of I. This model can be straightforwardly obtained from the classical region adjacency tree of I and its binary complement image $$I^c$$ , by a suitable region label identification. With these two labeled rooted trees, it is possible: (a) to compute Euler number of the set of foreground (resp. background) pixels with regard to 4-adjacency or 8-adjacency; (b) to identify new local and global measures and descriptors of topological dissimilarity not only for one image but also between two or more images. The parallelization of the algorithms to extract and manipulate these structures is complete, thus producing efficient and unsophisticated codes with a theoretical computing time near the logarithm of the width plus the height of an image. Some toy examples serve to explain the representation and some experiments with gray real images shows the influence of the topological dissimilarity when detecting feature regions, like those returned by the MSER (maximally stable extremal regions) method.
Efficient Client-Side Cross-Platform Compatible Solution for Phishing Prevention

Phishing is a crime where the victim is contacted by an attacker posing as a trustworthy source and lure them into providing sensitive information. This work makes sure that time delay is averted and the attack can be thwarted with fewer computational resources. To solve the above mentioned problem, a web browser add-on that works as a background script on the client side is implemented. All the background scripts made cross platform compatible to make the development easier and more efficient. The contribution in this paper is the new add-on, Off-the-hook-plus. The main objective of this Off-the-hook-plus is to find if the web page that the user visits is a phish or not. The page redirect logs along with the page details are used to find if the site is a phish or not. And then if the site happens to be a phish, the similar looking site which this web page tries to impersonate is given. To make the repeated accesses faster, a whitelist is maintained, where all the safe sites are logged. The add-on is experimentally evaluated and the results are discussed and is found to be effective.
Semi-supervised Multi-Label Feature Selection Using Hessian Energy based on Maximum Relevance and Minimum Redundancy

With the development of the Internet, instances are not limited to a single label. “High-dimensional and multiple labels” poses a huge challenge to the use of data information. Multi-label feature selection technology has attracted much attention as one of the important dimensionality reduction methods. The labeling cost of the data is too large, which leads to the widespread application of semi-supervised multi-label feature selection in feature selection. However, the existing semi-supervised multi-label sparse feature selection algorithm considers only the correlation between features and labels, without taking into account redundancy among features; moreover, most of them capture the local structure of the data by using Laplacian regularization. But the Laplacian regularization method is not good at inferring the popular structure. Therefore, a novel Semi-supervised Multi-Label Feature Selection algorithm using Hessian Energy based on maximum relevance and minimum redundancy (S2MFSHMRMR) is proposed in this study. Firstly, Hessian regularization and Hilbert-Schmidt independence criterion (HSIC) are respectively used to reflect the inherent local geometric characteristics of the data and to solve the problem of the relevance between features and multiple labels. Then, a new custom redundancy regularization method is proposed, and the redundancy between features is measured by GMM-based Bhattacharyya distance. In addition, the soft labels required for the Bhattacharyya distance are obtained by using the multi-label label propagation method. Finally, a closed-form method based on the matrix Lagrangian multiplier method is proposed to minimize the objective function. Experiments are performed to verify the effectiveness of the method. The proposed algorithm S2MFSHMRMR is compared with five algorithms on six Mulan data sets. Experimental results show that the proposed method is very competitive.
PowerDet: Efficient and Lightweight Object Detection for Electric Power Open Scenes

In recent years, with the expansion of the electric network, maintenance cost of electric power transmission and transformer substation equipment has become increasingly greater. Moreover, it requires a lot of manpower as well. At present, there are certain schemes which leverage artificial intelligence techniques to detect equipment flaws or errors automatically. Nevertheless, there are problems with the schemes in open electric power scenes, e.g., only able to detect single category, low detection accuracy of multi-scale objects, and difficulty in deploying models on mobile devices. To address the challenges mentioned above, we propose an object detection model, named PowerDet. It is able to detect 9 different types of power facilities efficiently with low cost. To verify the effectiveness of PowerDet, we collect an open scene facility entity dataset and conduct a series of experiments. Experimental results demonstrate that PowerDet achieves 86.8% AP50 on the dataset, which outperforms the state-of-the-art. The lightweight version of PowerDet, i.e., PowerDet-Lite, can achieve real-time inference on mainstream mobile devices.
Performance Assessment of Wheelchair Driving in a Virtual Environment Using Head Movements

The present work consists of the integration of a motion tracking device using an inertial sensor to control a motorized wheelchair with neck flexion movements and incorporating the device into a training environment to use the wheelchair in Virtual Reality. Training makes it possible to ensure a better performance when using this technology on a daily basis, avoiding accidents and any type of discomfort. The target audience is people with disabilities that require a motorized wheelchair, but are unable to use the traditional control present in this device (joystick), due to impairment of the upper limbs. The implementation of the software for the control module was made easy for users to operate and simple to integrate with additional devices, if necessary. From initial experiments with healthy people, it was possible to prove the relevance of the proposal and the necessity of future improvements for the project.
Metaverse-Empowered Music and Dance: Experience of Emotion and Scene Unification

Music and dance are closely related and symbiotic. On the one hand, dance often requires music accompaniment. On the other hand, dance can enrich the melody and style of music. The emergence of the metaverse has taken the experience of music and dance to a new level. This paper studies the three-dimensional situational experience of music and dance in Virtual Reality (VR) empowered by metaverse to feel the beauty of situational integration. After the spherical video is projected onto a two-dimensional plane to form a panoramic video, the two-dimensional panoramic video needs to be converted into a spherical video for users to watch. Therefore, it is more reasonable to take spherical video distortion as the distortion measure of panoramic video coding. In this paper, spherical video distortion is taken as the measurement standard of video quality, and the panoramic video coding technology is optimized. Furthermore, the corresponding weights are introduced to change the distortion ratio of different interpolation regions in the calculation process of rate distortion cost, and a rate distortion optimization technology based on spherical distortion measurement is proposed. The equal weight feature of spherical pixel is realized on two-dimensional plane, which improves the coding efficiency of panoramic video. Experimental results show that compared with the three benchmarks, the proposed algorithm can achieve 1.6157% bit saving on average and achieve a good Quality of Experience (QoE) when other processes are the same.