Reviewerly/exHarmony huggingface dataset:
Robust Watermarking Techniques against Compression Attack

Abstract Digital watermarking is a way to protect digital images from malicious attacks. Compression attack is one of the most common attacks on images uploaded to social media. Social media, such as Facebook and Twitter, have implemented a compression method for all types of media before being successfully uploaded to their server. It is designed to reduce the network bandwidth and storage needed to store each media on their server. However, the compression method used tends to tarnish the image properties of the image itself, which can be used to identify the image itself. This causes another problem, namely ownership and copyright issues. The use of digital watermarks was therefore proposed for this research to avoid the problem identified. The selected digital watermarking techniques, which are DCT-based Pyramid Transform (DCTPT), Two-Step Sudoku Method using LSB (TSSLSB), Semi-fragile Spatial Watermarking based on Local Binary Pattern Operator (SSWLBP) and Hybrid Schur and Singular Value Decomposition (HSSVD), theoretically must be able to withstand compression attacks by social media. These techniques have been simulated using the default MATLAB image dataset, but the use of photos taken by smartphones is a must for us to obtain useful metadata for real-world implementations. Some comprehensive analyses of the watermarking algorithms were done, and the findings were recorded in this paper.
The Application of Computer Network Technology in the Sharing of Enterprise Financial Files

Financial Sharing breaks through the boundaries of time and space, and realizes the centralized management and information sharing of enterprise finance. The purpose of this paper is to study the application of computer network technology in enterprise financial file sharing. Firstly, we have a deep understanding of the definition of shared services and financial shared services. Secondly, we have discussed the characteristics of enterprise accounting electronic file management under the financial sharing mode from the aspects of convenience, real-time and efficiency. After studying the cloud computing model and data security architecture, we have established the enterprise financial file sharing information system. The experimental results show that all data blocks of the data file are assumed to be average Stored on different nodes of the same rack, when the number of nodes is 1, the construction of hierarchical hash tree can also be regarded as the file hash tree of data files that hash all data blocks of data files, which is the same as the construction of hierarchical hash tree without. When the data file is deleted, modified and inserted, the construction of hierarchical hash tree is more time-saving than that of unstructured hash tree.
Multi-Scale PointPillars 3D Object Detection Network

Point cloud object detection is a key step in many 3D applications, such as autonomous driving and housekeeping robots. In this paper, we present MPNet, a Multi-Scale PointPillars Network for 3D object detection using point clouds. This paper aims at high accuracy and efficient speed. The proposed neural network is mainly composed of three part, a feature extracting part, a region proposal network, and finally a detection head. We use multi-scale encoders to concurrently extract features by PointNet, and then treat the 3D feature map as a pseudo image with many channels. Inspired by feature pyramid networks (FPN) and deformable convolution, our region proposal network achieves high accuracy while keeps high speed. MPNet combines features with different receptive field, and accelerates model by using 2D convolution, balancing the accuracy and speed well. Experiments on KITTI car detection benchmark show that our MPNet captures better location information on the bird's eye view. It ranks the third place in the Bird's Eye View Leaderboard. Meanwhile, it is the most efficient model compared within the top 10 models in the Bird's Eye View leaderboard.
Design of Calligraphy Online Course in Primary and Secondary Schools Based on K-Means Algorithm

This article focuses on how to use the descriptive data aggregation technology with K-Means algorithm to analyze the test scores in the process of analyzing the quality of teaching in elementary and middle schools based on the subject test. The data, in turn, provide a scientific basis for the effective evaluation of subject teaching quality.
Quantitative Analysis of Goal Oriented Requirements Models

An information system is developed and embedded into a dairy activity to satisfy requirements of people in the activity. Because the system is expected to improve the activity, we have to predict the extent of such improvement. Goal oriented requirements models are useful to represent the relationships among systems and people in a business or life activity. We have been proposed extended goal oriented requirements models to predict how well the system improves the activity of the people. In this talk, we briefly introduce typical goal oriented requirements models, and their extensions suitable for quantitative analysis. Finally, we show several issues of future challenges.
The Bitcoin-Network Protocol from a Forensic Perspective

Network forensics is challenging within most police investigation. Adding a cryptocurrency to network forensics makes it an even more complex challenge. One of the cryptocurrencies that can show up during network forensics is Bitcoin. Bitcoin gained popularity over the last years among criminals as an alternative to fiat currencies. Because of this increasing popularity, the use of bitcoins by criminals can be found in more and more police investigations. The bitcoin is a cryptocurrency that completely depends on its participating computers. These computers communicate with the bitcoin network protocol to make everyone aware of the latest changes. The bitcoin network protocol uses a message paradigm to send and receive information between participants. To be able to investigate a protocol like the bitcoin network protocol an investigator needs to have specific knowledge to gain investigative insights regarding the network information that was collected. While there are many (academic) papers written about the bitcoin ledger, very little information is available to investigator to acquire the knowledge to investigate the network protocol. This chapter focuses on the knowledge gap that a police investigator might have when encountering bitcoin network protocol. By conducting an experiment in which the network traffic of a bitcoin client, receiving a small amount bitcoins, the relevant information was investigated. After the experiment was completed the collected data is processed following the phases of the generic process model for network forensics. This chapter identified four bitcoin messages that were marked as possible messages that contain relevant information. After analysing three out the four messages turned out to be relevant. These messages will allow the investigator to identify the following information: (i) Identify the software that was used for communicating with the bitcoin network; (ii) The use of a Bloom filter enables an investigator to test bitcoin addresses to determine if a bitcoin client is interested in them; (iii) With the help of the Bloom filter and open source information the transaction message could be determined; (iv) From the messages of the sending party the unique transaction identifier was calculated, enabling the investigator to retrieve details from this transaction from the block chain. With the help of the Bloom filter and transaction messages it’s possible to determine to a degree close to certainty the transaction sent and received by bitcoin clients. The experiment has a limited amount of messages that were investigated. There might be more information available in the messages that did not get the attention in this experiment, also the bitcoin client used had no history of previous payments which likely has resulted in less network information and less pollution of historic information within the messages. For future experiments or research challenges can be found in investigating heavily used bitcoin clients or the bitcoin messages that did not get the attention within the experiment.
The Politics of Pandemics

The COVID-19 pandemic has colored the politics of 2020 from international to domestic, and the responses by countries have been politicized and limited by various actors. Regimes, both democratic and not, are using the chaotic pandemic environment to consolidate power under the executive, control the masses through decree, and shifting towards national and power bloc supply chains from the international supply chain that has been for all nations in the era of globalization and immediately after. This chapter will provide insight into how various nation-states are using nationalism to combat the pandemic, including the United States, United Kingdom, Chile, Russia, and Hungary. The chapter highlights the availability of the internet and social media platforms to spread mis- and dis-information that can hinder the work of a legitimate government attempting to respond earnestly and effectively to the pandemic.
An Enhanced Depth Approximation Model for Haze Removal Using Single Image

Contrast of images which are captured in poor weather (i.e., hazy, foggy, rainy and cloudy) degrades due to optical and physical properties of light and atmospheric particles. This degradation lessens the performance of computer vision assisted systems. The weight of degradation at a pixel depends upon depth of the pixel from camera. Therefore, accurate depth estimation of a pixel is essential to improve quality of image. This paper presents color attenuation prior-based depth approximation model to approximate depth of a pixel from the camera using a single degraded image. The proposed method observed that the depth of a pixel from the camera is directly proportional to the difference of the saturation from the sum of brightness and hue. Visual quality and quantitative metrics are used to compare results of the proposed method with prominent existing methods in literature.
Refining Rule Bases for Intelligent Systems: Managing Redundancy and Circularity

Intelligent systems are technologically advanced machines that perceive and respond to the world around them. They can take many forms: facial recognition programs, personalized shopping suggestions, healthcare tools, etc. Research in intelligent systems faces numerous challenges, many of which relate to automatic reasoning. Intelligent systems’ knowledge bases are founded on facts and rules. Rules updates are essential to ensure that the system adapts to its environment evolution. In this paper, we aim to facilitate the automation of rule bases management by eliminating redundancies and handling circularity. This research work is part of the proposition of an approach for automating the management of rule bases. Our method is based on dependency relationships that may exist between the rules. The experimentation results show that our proposition succeeded in eliminating redundancies and detecting a great number of cycles.
Supervised framework for top-down color interest point detection

Selective attention is one of the dominant properties of the biological visual system to locate regions of interest in the scene. This article presents a local feature detector based on selective attention. Most previous approaches are bottom-up and do not consider prior information for known object categories. They detect feature points using spatial information present in the image. In contrast, this article presents an attention inspired algorithm that encapsulates the second-moment matrix-based detector to identify feature points based on some color information selectively. The intuition is to use skin color as a top-down cue to generate interest points, finding a high similarity to known spectra. It has applications in a variety of real-time applications such as image retrieval, gesture classification, virtual reality, etc. The technique is inspired by the human visual perception to gain cognizance of regions based on selective boosting of colors. The properties of color models are used to form a distinctiveness function to suppress unwanted background clutter. A relationship between interest points and salient colors in the image is computed using partial correlations in color derivative space. The system is evaluated on the MSRA dataset commonly used for saliency detection. The experiments are based on finding distinct regions having an affinity towards skin color. The robustness of the algorithm is tested in a realistic scenario by separate training and testing datasets. Experimental results show a high level of repeatability for different noise variations, image compression, and blur. The simplicity, robustness, and efficiency of the technique to locate color interest points make it appropriate for real-time vision systems.
THE LOGIC OF SEQUENCE FRAMES

Abstract This paper investigates and develops generalizations of two-dimensional modal logics to any finite dimension. These logics are natural extensions of multidimensional systems known from the literature on logics for a priori knowledge. We prove a completeness theorem for propositional n -dimensional modal logics and show them to be decidable by means of a systematic tableau construction.
Optimization Naive Bayes using Particle Swarm Optimization in Volcanic Activities

Abstract This study is a continuation of previous studies that apply Naive Bayes classifier algorithm for predicting the status of volcanoes in Indonesia based on factors of seismicity. There are 5 criteria used in predicting the status of the mountain, namely the status of normal, alert and standby. The results of the study showed that the system accuracy produced was only 79.31%, in other words, it was still at the stage of fair classification . To overcome these weaknesses so that accuracy increases, optimization is done by giving the weight of criteria or attributes using particle swarm optimization . From the results of research by applying the same data using Particle Swarm optimization methods optimization , the accuracy of the resulting system increase of to 95.65%, where the number of particles is initialized 50 and the weight range [0 2].
Convolutional Neural Network with Multi-column Characteristics Extraction for Image Classification

In the last few decades, the constant growth of digital images, as the main source of information representation for scientific applications, has made image classification a challenging task. To achieve high classification yields, different pattern recognition techniques have been proposed, among which are the deep learning methods that today focus their study on image processing and computer vision. In this approach, the most popular architecture for the image classification task is the convolutional neural network (CNN), a network constructed of multiple layers and where each layer models a receptive field of the visual cortex making it much more effective in artificial vision tasks [1]. This paper proposes a convolutional network architecture with a performance-enhancing approach, a hierarchical structure that is easy to build, adaptive, and easy to train with good performance in image classification tasks.
Application of Latent Factor Model on a Restaurant Menu Recommendation System

Abstract A restaurant menu recommendation system is becoming a necessity following the trend of eating out at the restaurants. The task of such a system is to generate a top- N list of menus that may be of interest to a customer, in which the customer previous rating behavior is used as the learning model. In this paper, we apply the SVD (Singular Vector Decomposition) latent factor model as the learning technique of the recommendation system. Beforehand, we implement the mean imputation technique to fill in the missing rating entries so that SVD can also deal with the new customer that has no rating record in the system. Evaluation on a real-world restaurant menu recommendation dataset shows that our recommendation system is able to generate a top-10 list of menu recommendations to a target customer and that the results of the low-rank approximation using SVD are comparable with that of the full-rank.
Representation of Words in Natural Language Processing: A Survey

The article is devoted to research to the state-of-art vector representation of words in natural language processing. Three main types of vector representation of a word are described, namely: static word embeddings, use of deep neural networks for word representation and dynamic) word embeddings based on the context of the text. This is a very actual and much-demanded area in natural language processing, computational linguistics and artificial intelligence at all. Proposed to consider several different models for vector representation of the word (or word embeddings), from the simplest (as a representation of text that describes the occurrence of words within a document or learning the relationship between a pair of words) to the multilayered neural networks and deep bidirectional transformers for language understanding, are described chronologically in relation to the appearance of models. Improvements regarding previous models are described, both the advantages and disadvantages of the presented models and in which cases or tasks it is better to use one or another model.
Improving the Prediction Accuracy of ASD Using Class Imbalance Mitigation Technique

The efficiency of prediction models generated by machine learning algorithms depends on the dataset used. To produce a good prediction model a well-balanced data is required. A well-balanced data set is one that has an equal distribution of samples belonging to various classes under study. Real-world datasets in various domains like health care, fraudulent behavior identification, risk management are class imbalanced. For example, in many publicly available health care data sets the number of samples having certain disease symptoms will be very few compared to healthy samples. Such a biased data set will reduce the accuracy of the prediction model generated by traditional classifiers. Hence, it becomes essential to transforming the given data set to a well-balanced one before applying any machine learning algorithms. In this paper, we analyze the autism prediction accuracy of the six machine learning algorithms such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). These algorithms are applied to the Autism Screening Adult Data Set collected from the UCI repository, the accuracy and Cohen’s Kappa statistic measures are studied. As this data set is not well balanced, a prominent SMOTE algorithm is used to balance it. A comparison of the prediction accuracy shows that logistic regression performs better in a well-balanced data set.
CV-MDI-QKD with coherent state: beyond one-mode Gaussian attacks

Abstract A general security proof of continuous variable (CV) measurement device independent (MDI) quantum key distribution (QKD) should not be automatically reduced to the analysis of one-mode Gaussian attacks (in particular, independent entangling-cloner attacks). To stress this point, the present work provides a very simple (almost trivial) argument, showing that there are an infinite number of two-mode Gaussian attacks that cannot be reduced to or simulated by one-mode Gaussian attacks. This result further confirms that the security analysis of CV-MDI-QKD must generally involve a careful minimization over two-mode attacks as originally performed in (Pirandola et al , 2015 Nature Photon. 9 , 397–402; arXiv: 1312.4104 (2013)).
Impact of Internet-Wide Scanning on IoT Data Communication in Wireless LANs

In recent years, Internet-wide scanning has attracted interest as a countermeasure against Internet-of-Things (IoT) security problems that can detect security holes. Despite the usefulness of Internet-wide scanning, it suffers from the problem of network congestion that occurs when numerous port scanning packets are used. Network congestion becomes increasingly severe in networks with limited bandwidth such as wireless networks. Therefore, port-scanning packets must be sent at long intervals when scanning such networks. In contrast, an inappropriate reduction of scanning speed increases the time required to complete scanning, making it difficult to scan IoT devices comprehensively before cyber-attackers identify vulnerabilities. However, setting appropriate scanning speeds is a challenge owing to the lack of a method for quantifying the impact of scanning traffic on the target network. In this study, we construct a novel analytical model to quantify the impact of scanning traffic on IoT data throughput in a target wireless LAN. The validity of our proposed model is evaluated through Monte Carlo simulations whose results demonstrate that our model is effective in approximating the impact of scanning traffic on IoT data traffic. This study contributes to overcoming a key shortcoming of Internet-wide scanning as a critical factor in efficient probing.
Survey Paper on Reversible Arithmetic Unit Based on Programmable Gate Structure

Power crisis is a vital problem in today’s world. In recent years, the growing market of electronic systems suffers from power dissipation and delay removal problem. Bennett et al. proved that the one-to-one mapping between the inputs and outputs of reversible circuit drastically reduces the power consumption and delay consumed of a circuit. There are four major design parameters of reversible circuits. First is the gate count which is the number of gate are used in the circuit. Second is the quantum delay. Third is the number of ancilla inputs which are constant inputs which are used to maintain the reversibility of the device. Fourth is the number of garbage outputs i.e. output signals which are not used as inputs to other gates and are only there to maintain reversibility. In this paper the survey of design central processing unit based on reversible gate and parameter.
Design of Class Management System Based on Naive Bayes Algorithm

This paper starts with the current needs of class management in colleges and universities, analyzes the basic structure of class management information systems in colleges and universities, gives the basic content of system module design and database design, and explains the main problems in system implementation.
Testing of games through software agents managed by artificial neural networks

Abstract With the development of artificial neural networks, their use in different fields is increasing to optimize different activities. The availability of rich programming languages, various existing machine learning software libraries, along with the virtualization technologies has made intelligent technologies accessible not only to large corporations but also to individual programmers. Game development is a complex process involving many specialists, experts in various fields. Single-game testing is an iterative process that covers different game development opportunities, different player abilities, as well as player interaction when working in a team. The question is, is it possible to use intelligent software agents in the game testing process in order to optimize the human, time and financial resources needed? This article introduces our attempt to create an intelligent autonomous agent that plays the Brawl Stars game, managed by an artificial neural network. Initially, the agent is trained by collecting frames from a game that is being played by an experienced player. The frames are then processed and analyzed, and a neural network is trained, which directs the agent to conduct an autonomous game. The developed agent was used in two experiments that confirmed the great potential for the use of artificial neural networks in game testing.