Reviewerly/exHarmony huggingface dataset:
Steganography Using Block Pattern Detection in AMBTC Image

In general, AMBTC-based steganography techniques effectively utilize smooth blocks for secret data embedding but keeps complex blocks as it is. It is considered that any modification in the complex blocks may cause a substantiate distortion in an original image. Therefore, conventional techniques do not make any modification in complex blocks. In proposed paper, a technique is disclosed for secret data embedding in complex blocks in addition to smooth blocks. It shows that a cluster of bit-map blocks corresponding to complex blocks of multiple images are formed, and then most frequent 256 bit-map blocks are determined based on histogram analysis. Multiple images can be drawn from any standard image database. The most frequent 256 bit-map are block patterns which are indexed using 8 bits. Now, for secret data embedding, bit-map of each complex block is scanned one-by-one and is compared with the 256 bit-map block patterns. If the bit-map of scanned complex block is matched with any of the 256 block patterns, then first 8 bits of bit-map of scanned complex block is replaced with 8-bit index of matched block pattern and remaining 8 bits of bit-map of scanned complex block are replaced with secret data. Thus, this technique shows a way of embedding secret data in complex blocks using block patterns. Further, for the smooth blocks, present technique utilizes Ou et al. technique which is efficient for secret data embedding in smooth blocks. Results of the proposed scheme show increase in embedding capacity while preserving image quality.
Methods for Detecting Fake Accounts on the Social Network VK

This article addresses the issue of detecting fake user accounts on social networks. The purpose of the study is to find and analyse the distinctive features of fake accounts in comparison with the accounts of real users using the example of the social network VK. The introduction looks at various studies on this topic, including the possibility of identifying fake accounts on other social networks. The experimental part describes the process of collecting information about the accounts of fake users from the social network VK, highlighting features, compiling a dataset for training the classifier. The results show the results of the accuracy of detecting fake accounts and a comparison of the results of various classification methods. In the conclusion, conclusions are presented about the effectiveness of the proposed approach and possibilities.
RETRACTED ARTICLE: Application of embedded computer and improved genetic algorithm in the strategy of community of human destiny: the development of artificial intelligence in the context of Covid-19

Through the COVID-19 epidemic in 2020, the society has deeply realized the inevitability and necessity of building a community that shares the future of mankind. In the face of severely complex international trends and domestic and international economic conditions, artificial intelligence plays an important auxiliary role in the regular prevention and management of COVID-19. In order to effectively correspond to the formalized extensional prevention and control theory, it is essential to use coordination models, rule systems, prevention and control mechanisms, and governance landscapes to build artificial intelligence corresponding systems. This article uses a basic genetic algorithm to realize the robot path plan. This mainly includes the establishment of environmental models, the discovery of chromosomes and the determination of coding methods, the selection and design of fitness functions, and related designs. This paper proposes a new adaptive adjustment mode based on the basic genetic algorithm, which improves the selection and mutation operation, and improves the optimization efficiency of the genetic algorithm. Building an artificial intelligence response system may face various technical risks and governance dilemmas. Only by improving the rule system of artificial intelligence, creating an epidemic prevention and control ecology, conserving the public spirit of the whole people, strengthening the governance of the source of crisis, and further improving the new momentum of economic and social development and public safety. The modernization of governance capabilities can better respond to the current complex situation.
Fringe pattern background and amplitude modulation in DeepDensity: convolutional neural network for local fringe density map estimation

Proposed convolutional neural network based, fast and accurate local fringe density map estimation by DeepDensity was developed to significantly enhance full-field optical measurement techniques, e.g., interferometry, holographic microscopy, fringe projection or moiré techniques. The use of neural networks to determine the final result of the optical measurement may raise legitimate metrological concerns and therefore for the sake of versatility and independence from measurement technique we still recommend the use of fully mathematically sound solutions for both fringe pattern prefiltration and phase retrieval. It is worth to acknowledge that proposed DeepDensity network does not supersede mathematically rigorous phase extraction algorithmic solutions, but it only supports them. For that reason during the neural network learning process it was assumed that the data fed to neural network will be prefiltered so background and amplitude modulation should be successfully minimized. Nevertheless, it is still interesting how sensitive to the prefiltration accuracy is proposed DeepDensity. In this contribution we present a thorough analysis of the DeepDensity numerical capabilities in the case of insufficient fringe pattern (interferogram, hologram, moiregram) background and/or amplitude filtration. The analysis was performed with the use of simulated data and then verified using experimentally recorded fringe pattern.
An MM Algorithm for Estimating the MNL Model with Product Features

The multinomial logit (MNL) model is a workhorse model for modeling customer demand in many fields including operations, econometrics and marketing. In this work, we present a fast algorithm for solving the likelihood maximization problem for the MNL model with product features. Our algorithm falls under the general framework of minorize-maximize (MM) procedures and we show that it results in an efficient iterative procedure with closed-form updates. We establish a necessary and sufficient condition under which the optimization problem has a unique and bounded solution and establish convergence of our proposed algorithm to the global optimal solution.
Application of Intelligent Information Technology in the Reform of Hybrid Teaching Courses in Colleges and Universities

Abstract With the rapid development of information technology and the deepening of teaching information reform, hybrid teaching is becoming the focus and development direction of current higher vocational education reform. By integrating network information technology into traditional classroom teaching, hybrid teaching overcomes the shortcomings of traditional classroom teaching, gives full play to the advantages of information technology, and improves the quality of teaching. According to the characteristics of Modern educational Information and Technology course in higher vocational colleges, this paper studies the teaching reform of online and offline Modern educational Information and Technology. 50 questionnaires were sent out and the results were analyzed.
Deep Neural Network Based Phishing Classification on a High-Risk URL Dataset

Due to the growing trend of Internetization, the number of connected computers has been increasing day by day. Almost all companies are transferring their main operations from the real world to the cyberworld. Although this increases the marketplace of the firms, it also brings lots of vulnerabilities, such as cyber-attacks, especially with the anonymous structure of the Internet. Phishing is one of the popular attack types which exploits the vulnerabilities to user unawareness. There are some works in the literature that gets help from the rule-based detection systems as a static preventions mechanism, and machine learning-based systems as dynamic prevention mechanisms. In this work, we implemented a deep neural network (DNN) based phishing detection system by analyzing the URL of the suspicious websites. Although in almost all previous researches the used datasets are collected by different resources in which legitimate and phishing websites are clear, in this research, we firstly create a high-risk dataset, which contains only the suspicious websites which are reported to PhishTank website. Experimental research showed that the proposed system gives a very good level of efficiency both in accuracy and execution time manner.
A Vehicle Detection Method Based on Improved YOLOv3

In order to improve the accuracy of vehicle detection, this paper proposes a vehicle detection method based on improved YOLOv3. First, the loss function of the YOLOv3 algorithm is improved by introducing the GIoU and focal loss strategies, meanwhile, the detection accuracy of the network is improved. Secondly, by adopting the method of data augmentation for preprocessing, adding label smoothing strategy improves the generalization ability of the model and further improves the average accuracy of detection. During training, multi-scale training is used, and the cosine decay learning rate decay strategy is used to replace the step learning rate decay strategy, which reduces the negative impact of improper learning rate and decay steps, speeds up model training, and reduces the network training cycle. Experimental results show that the improved method can significantly improve the detection accuracy of the original model in fewer training cycles, has good robustness, and can effectively detect road vehicles.
An Activity Recognition System for Taking Medicine Using In-The-Wild Data to Promote Medication Adherence

Nearly half of people prescribed medication to treat chronic or short-term conditions do not take their medicine as prescribed. This leads to worse treatment outcomes, higher hospital admission rates, increased healthcare costs, and increased morbidity and mortality rates. While some instances of medication non-adherence are a result of problems with the treatment plan or barriers caused by the health care provider, many are instances caused by patient-related factors such as forgetting, running out of medication, and not understanding the required dosages. This presents a clear need for patient-centered systems that can reliably increase medication adherence. To that end, in this work we describe an activity recognition system capable of recognizing when individuals take medication in an unconstrained, real-world environment. Our methodology uses a modified version of the Bagging ensemble method to suit unbalanced data and a classifier trained on the prediction probabilities of the Bagging classifier to identify when individuals took medication during a full-day study. Using this methodology we are able to recognize when individuals took medication with an F-measure of 0.77. Our system is a first step towards developing personal health interfaces that are capable of providing personalized medication adherence interventions.
Sequential Test for a Mixture of Finite Exponential Distribution

Testing the number of components in a finite mixture is considered one of the challenging problems. In this paper, exponential finite mixtures are used to determine the number of components in a finite mixture. A sequential testing procedure is adopted based on the likelihood ratio test (LRT) statistic. The distribution of the test statistic under the null hypothesis is obtained using a resampling technique based on B bootstrap samples. The quantiles of the distribution of the test statistic are evaluated from the B bootstrap samples. The performance of the test is examined through the empirical power and application on two real datasets. The proposed procedure is not only used for testing the number of components but also for estimating the optimal number of components in a finite exponential mixture distribution. The innovation of this paper is the sequential test, which tests the more general hypothesis of a finite exponential mixture of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> components versus a mixture of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> <mo>+</mo> <mn>1</mn> </math> components. The special case of testing an exponential mixture of one component versus two components is the one commonly used in the literature.
Positive Real Properties and Physical Realizability Conditions For a Class of Linear Quantum Systems

Theoretical developments in the field of quantum optics and quantum superconducting electrical circuits involving continuous measurement based feedback control as well as coherent control are an important prerequisites for advances in the domain of quantum technology. Within these perspectives, this paper considers positive real properties for a class of quantum systems whose quantum stochastic differential equation model involves annihilation operators only and then relates them to corresponding bounded real properties and consequently to physical realizability conditions developed earlier by the authors. Based on the positive real properties of these quantum systems, it is anticipated that it is possible to use the Brune algorithm in order to find an electrical circuit that can physically implement these quantum systems. This theory, in the case of one-port circuits, may be useful for the implementation of microwave circuits related to quantum filters found in the field of quantum computing.
Automatic Multiple-Choice and Fill-in-the-Blank Question Generation from Arbitrary Text

In this paper, we propose three novel algorithms to help educators generate questions to evaluate learners’ comprehension of the learning material. First, we propose the Automatic Question Generator algorithm that automatically generates multiple-choice, true-or-false, and fill-in-the-blank questions from any arbitrary text. Second, we propose the Automatic Distractors Generator algorithm that automatically generates multiple wrong but relevant answers for the multiple-choice questions. Third, we propose the Automatic Answers Generator algorithm that automatically generates multiple synonyms to the correct answer of the fill-in-the-blank questions to be accepted as correct answers. The algorithms have been used on finance and business learning material as case studies for evaluation.
LGL-GNN: Learning Global and Local Information for Graph Neural Networks

In this article, we have developed a graph convolutional network model LGL that can learn global and local information at the same time for effective graph classification tasks. Our idea is to concatenate the convolution results of the deep graph convolutional network and the motif-based subgraph convolutional network layer by layer, and give attention weights to global features and local features. We hope that this method can alleviate the over-smoothing problem when the depth of the neural networks increases, and the introduction of motif for local convolution can better learn local neighborhood features with strong connectivity. Finally, our experiments on standard graph classification benchmarks prove the effectiveness of the model.
Measuring the Performance of a Model Semantic Knowledge-Base for Automation of Commonsense Reasoning

Commonsense Reasoning is a subfield of artificial intelligence that deals with ability of a computer to imitate mundane decision making. Though the field existed from decades, there has been very little contribution made to the development of commonsense reasoning. Lack of a well-defined methodology as well as computing facilities for implementing commonsense reasoning have always been obstacles in the development process. Semantic networks can be used to conceptualize and implement a part of commonsense reasoning. This paper presents a study on performance measurement and analysis of one such model semantic network used to build commonsense knowledge-base. Various categories of performance measures have been presented to analyze the practicality of such models for automation of commonsense reasoning. Overall, the analysis is intended to present a practical feasibility study of a model semantic network by considering characteristics of a commonsense knowledge-base.
Convolution Based Multilevel DWT Architecture Using Distributed Arithmetic and FIR Bi-orthogonal Filter for Two-Dimensional Data Analysis

The biomedical signal used in diagnosis of disease needs to be processed using efficient algorithms. These algorithms occupy more memory, and efficient realization is a challenge. The discrete wavelet transform is designed using FPGAs and ASICs. In this paper, a memory centric convolution-based multilevel DWT architecture was designed and implemented using orthogonal and biorthogonal wavelet filters. The proposed design using distributed arithmetic is an efficient realization for convolution-based generic two-dimensional DWT structure. The proposed design reduces the computational complexity by introducing multilevel decomposition schedules and FIR filter. Convolution based generic structure using distributed arithmetic adder (DAA) is found to be reduced area and power. The experiments were carried out using FPGA.
Adaptive Bayesian filtering based restoration of MR images

MR images contain noise and for quantitative clinical diagnosis restoration is essential. For single coil MR images, noise follows Rician distribution when signal to noise ratio (SNR) is low and Gaussian distribution when SNR is high. Rician noise is signal dependent and introduces bias. The work proposes an adaptive Bayesian framework for restoration of 2D magnitude MR images. Restoration is achieved by Rician likelihood as the data attachment term with range and domain Gaussian filters, adaptive to noise as prior in Maximum A´ posterior framework. A good filtering behavior is achieved due to the domain component of the filter and crisp edges are preserved at the same time due to the noise adaptive range component. Rician likelihood aids the image restoration in terms of bias removal. Convergence of the proposed method further highlights the optimal filtering performance. Experiments conducted on publically available Brainweb phantom demonstrate enhanced performance in terms of signal to noise ratio, structural similarity index and overall performance.
Can Zoom video conferencing tool be misused for real‐time cybercrime?

Abstract Video conferencing tool Zoom has boomed during this COVID pandemic situation. Although Zoom has been very useful for facilitating work‐from‐home and study‐from‐home arrangements, its misuse can result in cybercrime cases. The world has already seen the adverse effect of a Zoom‐related cybercrime, Zoombombing. In this article, we discuss how Zoom can also be misused for launching real‐time cybercrime, such as piracy and real‐time pornography. The end‐to‐end encryption proposed to be used by Zoom can further complicate these cybercrime issues. The existing mechanisms may not be sufficient in dealing with these real‐time issues. New mechanisms need to be found. This article is categorized under: Digital and Multimedia Science &gt; Multimedia Forensics
Advisability Analysis of Gold Exploration Based on Remote Sensing and Geographic Information Systems (Case Study: Banyuwangi Regency)

Abstract Economically, gold is a mining material that is widely enjoyed by the community and used as jeweleries, cosmetics, currency blends, and investment media. Banyuwangi Regency is one of the regions in Indonesia which has potential of gold resources by proving that there are several gold mining companies that have been managed since 2004. It is designated as a Metal Mineral Mining Area and a State Reserved Area by the Minister of Energy and Mineral Resources. This study aims to analyze and identify the advisability of gold exploration activities in Banyuwangi Regency. The method used in this study uses scoring and weighting method and combines using overlay method on land cover data and the potential gold mineral distribution data. The results of this study are in form of regional distribution which has the advisability of gold exploration and in accordance with the Mining Area Map by the Department of Energy and Mineral Resources. Areas worthy of being explored in gold and in accordance with maps of mining areas are scattered in the Districts of Pesanggaran, Siliragung, Glenmore, and Kalibaru. The area which has the largest area of exploration advisability results is in Pesanggaran subdistrict, which is an area of 44,894 hectares.
A Study on the Extraction of Customer Satisfaction Factors Based on the Customer Satisfaction Model Using Text Review and Preview

Through the era of big data, the predictive power for customers has greatly improved. This helps consumers efficiently search for satisfactory products within a huge amount of product information. Already in commerce, quantifiable customer's product ratings and textual customer reviews enable consumers to recognize the quality of the product through evaluations of customers who have already purchased it, even if not purchased it. However, ratings and customer reviews were averaging the overall assessment of other customers. Thus, this paper tried to explore ways to personalized information by further embodying customer reviews. In order to segment the quality of the product, the custom satisfaction model proposed by Kano is framed. Customer reviews were analyzed through Latent Dirichlet Allocation(LDA) to classify the quality of the products recognized by the customer. After that, it was digitized through sentiment analysis and entered into the customer satisfaction model. Through this, Commerce expected that it would be possible to develop a more specific strategy for the quality perceived by the customer.
Obfuscating Circuits Via Composite-Order Graded Encoding

We present a candidate obfuscator based on composite-order graded encoding schemes (GES), which are a generalization of multilinear maps. Our obfuscator operates on circuits directly without converting them into formulas or branching programs as was done in previous solutions. As a result, the time and size complexity of the obfuscated program, measured by the number of GES elements, is directly proportional to the circuit complexity of the program being obfuscated. This improves upon previous constructions whose complexity was related to the formula or branching program size. Known instantiations of Graded Encoding Schemes allow us to obfuscate circuit classes of polynomial degree, which include for example families of circuits of logarithmic depth. We prove that our obfuscator is secure against a class of generic algebraic attacks, formulated by a generic graded encoding model. We further consider a more robust model which provides more power to the adversary and extend our results to this setting as well. As a secondary contribution, we define a new simple notion of algebraic security (which was implicit in previous works) and show that it captures standard security relative to an ideal GES oracle.
Analysis of Data Transforming Technology for Malware Detection

As AI technology advances and its use increases, efforts to incorporate machine learning for malware detection are increasing. However, for malware learning, a standardized data set is required. Because malware is unstructured data, it cannot be directly learned. In order to solve this problem, many studies have attempted to convert unstructured data into structured data. In this study, the features and limitations of each were analyzed by investigating and analyzing the method of converting unstructured data proposed in each study into structured data. As a result, most of the data conversion techniques suggest conversion mechanisms, but the scope of each technique has not been determined. The resulting data set is not suitable for use as training data because it has infinite properties.