Reviewerly/exHarmony huggingface dataset:
Deep Residual Learning based Discriminator for Identifying Deepfakes with Cut-Out Regularization

The recent development of Generative Adversarial Networks (GANs) have greatly eased the generation of deepfake images which are indistinguishable from real images. As a downside of such advancement, it is now easy to impersonate a person leading to identity theft and other malicious outcomes. In such a scenario it becomes imperative to have a robust algorithm in place which can segregate real images from the fake ones. In this study, we suggest a residual connection based convolutional neural network (CNN) architecture for detecting deepfake images and compare the results with the existing transfer learning algorithms for identifying the deepfakes. The data set used in this study is the combination of the Flickr-Faces-HQ (FFHQ) data set (Nvidia) and the deepfakes generated by the Style GAN, which is proposed by Nvidia. The data set consisting of 1,20,000 images is used for training and validating the network, while a separate set of 20,000 real world images are used for testing the performance of the model. In this current work, we test the robustness of three different algorithms - Inception Resnet V2, VGGFace2, and our customized Residual CNN with and without cut-out regularization in identifying real images. The residual architecture-based implementation in combination with cut-out architecture produces the lowest false positives rate at 0.0043% while the Inception Resnet V2 in combination with cut - regularization produces the best accuracy at 99.05%.
Ensemble Machine Learning AdaBoost with NBTree Model for Placement Data Analysis

This paper proposed a novel Ensemble Machine Learning (EML) method for placement data classification analysis. AdaBoost Method with various tree based classifiers are used as the base classifier for classification in placement data set. Here Adaboost is used to improving the nominal class classifier and increase the performance in time complexity and classification accuracy in the student data set with placement status as the nominal class attribute. In this paper, AdaBoost classifier with base classifiers decision stump, NBTree and Random Forest are used to classify the student data and analyse the performance of these classifiers with and without AdaBoost. The results shows that the NBTree shows best accuracy results than decision stump and Random Forest.
A Novel Graphical Password Scheme to Avoid Shoulder-Surfing Attacks in Android Devices

Shoulder surfing is an easy attack, where an unauthorized user tries to gain access to sensitive information. Unfortunately, none of the currently available content-based shoulder-surfing approaches for protected graphical concealed keywords are both trustworthy and professional. This paper proposes an authentication technique that operates on touchscreen devices with an aim to solve the problem of shoulder-surfing assaults over validation. Graphical passwords are required to get access to the system and to assist with its security. A graphical password is a type of password that can be used instead of a textual password to gain access to a system. It is safer than a written password, which is vulnerable to a shoulder-surfing assault. In this study, we used a falsification technique to create a system that is immune to shoulder-surfing assaults. As a result, the hacker will be perplexed when attempting to obtain and misuse the password. Using Android Studio, the proposed scheme was developed into an android application composed of shoulder-surfing resistant user registration and login process. When compared with other graphical password techniques based on images, the proposed the technique of graphical authentication mechanism with numbers/digits is more easy to memorize and has less cognitive strain on the user. Our graphical authentication technique has more complexity and larger password space than the conventional numeric password schemes, making it a superior method for safeguarding against shoulder surfing attacks. To meet customer expectations, this project seeks to design a graphical password by combining processes such as long key press, and acceleration detection mechanism of Android devices.
Moving Out of View

The technological affordances of video-mediated communication (VMC) allow family members to sustain relationships at a distance. In this paper, based on 45 hours of video recorded family video calls in the US and China, we use the methodology of Conversation Analysis to examine one recurrent activity in our corpus: people moving out of camera view and becoming not visible during their video calls. We show how, depending on interactants’ current involvement in the call, temporarily suspending visibility occurs in tandem with suspending participation, but not always. This study makes unique contributions to reconceptualizing the relation between the technological affordances of visibility and people’s mobility and participation in family video calls, furthering the notion of what “open” connections look like in practice.
Analysis of Different College Music Education Management Modes Using Big Data Platform and Grey Theoretical Model

Colleges and universities are crucial hubs for talent development, carrying out the vital task of supplying top-notch talent for all spheres of society. The growth of society and the economy is significantly influenced by the quality of education and teaching. A significant factor is the caliber of the instruction. Therefore, it is essential to innovate the educational management model in order to promote the holistic development of college students as well as the orderly development of educational and teaching activities in colleges and universities. The college and university music education model has made progress after years of development. In spite of its successes in reform, there are still a lot of issues that require close attention. The education system is constantly being updated and improved in order to meet modern development needs. It is essential to develop innovative strategies with strong operability from multiple perspectives in response to the actual issues that arise in order to carry out the reform work smoothly and steadily advance the innovation of the management model of music education in colleges and universities. This paper analyzes the teaching impact of the single-line education management model and the credit system education management model and predicts the students’ musical performance based on the gray theoretical model and the big data platform. The experiment revealed that as learning time increases, the teaching effects of the two learning modes increase, but it is evident that the credit system education management mode’s teaching effect is superior. The single-line education management model among them has an average score of 83.3 points, and the credit system education management model has an evaluation score of 86.1 points.
Transformers and deep CNNs for unmanned aerial vehicles detection

Thanks to the maturity of their field, Unmanned Aerial Vehicles (UAVs) have a wide range of applications. Recently, we have witnessed an increase in the usage of multiple UAVs and UAV swarm due to their ability to achieve more complex tasks. Our goal is to use deep learning methods for object detection in order to detect and track a target drone in an image captured by another drone. In this work, we review four popular object detection categories: two-stage (anchor-based) methods, one-stage (anchor-based) methods, anchor free methods and Transformer-based methods. We compare these methods’ performance (COCO benchmark) and detection speed (FPS) for the task of real-time monocular 2D object detection between dual drones. We created a new dataset using footage from different scenes such as cities, villages, forests, highways, and factories. In our dataset, drone target bounding boxes are present at multiple scales. Our experiments show that anchor free and Transformer-based methods have the best performance. As for detection speed, the one-stage methods obtain the best results followed by and anchor free methods.
Fake Review Identification Methods Based on Multidimensional Feature Engineering

Product reviews in electronic platforms are very valuable to potential customers, product manufacturers, and product sellers. Their data contain huge business opportunities. Therefore, this paper analyzes the views, attitudes, and emotions expressed in these reviews. It presents three fake review identification methods based on multidimensional feature engineering. Under the premise of adding product feature extraction and opinion sentence judgment, six feature parameters are defined to identify fake reviews, and a fake review identification model based on multidimensional feature engineering is constructed. Then, the effectiveness of the selected feature engineering is verified. Based on the multidimensional feature engineering model, a fake review identification algorithm based on multidimensional feature engineering of union relationship, an identification algorithm based on weighted multidimensional feature engineering scoring, and an identification algorithm based on weighted multidimensional feature engineering classification are proposed. The execution effects of the three methods are compared. Fake review identification models based on multidimensional feature engineering can effectively filter fake reviews.
An Evaluation of Control Objective for Information Related Technology (COBIT) 4.0 or 4.1: Systematic Literature Review

Control Objective for Information Related Technology (COBIT 4.0 and 4.1) is one of Indonesia's most popular information technology audit frameworks. This article compares the maturity level of implementing information systems in Indonesia, especially government agencies, educational institutions, health institutions, and companies. The method used is a literature study of the research results from 2015-to 2020 related to COBIT 4.0 and COBIT 4.1. This study's results indicate that the conceptual framework of COBIT 4.1 is more dominant than COBIT 4.0. The Plan and Organize (PO) domain and the Delivery and Support (DS) domain are mainly used to focus studies on government agencies and companies. Meanwhile, the maturity level is dominated by the repeatable but intuitive (level 2) and defined process (level 3), which can be repeated and defined. Thus, studies on information system audit using the COBIT 4.0 and 4.1 conceptual frameworks need to be improved in education and health agencies or other agencies that integrate business processes with information technology.
The Relationship between the Degree of Urban Intelligence and the Performance of Enterprises in the City Based on Artificial Intelligence and Soft Computing

With the rapid development of Internet technology, artificial intelligence and soft computing are gradually applied in various fields. Through active transformation, many companies use artificial intelligence, software computing, and other technologies to develop sales channels, improve corporate performance, and obtain greater benefits. However, some traditional enterprises have been gradually eliminated by the market because they have not rapidly transformed and improved their intelligence. Urban intelligence is the trend of urban economic development, and the degree of intelligence in a city is closely related to the number and scale of intelligent enterprises. In the context of artificial intelligence and soft computing, this paper expounds the relationship between enterprise performance and the degree of urban intelligence and analyzes the key factors affecting enterprise performance and the impact of enterprise performance on the degree of urban intelligence. The results show that Internet technologies such as artificial intelligence and soft computing can improve the performance of enterprises, thereby improving the level of intelligence in cities.
Why do fraud victims get blamed? Lerner’s Belief in a Just World and its application to victims of online fraud

Purpose Victims of online fraud face a high level of blame from their families, friends, professionals, the broader community and often from themselves. Victims are commonly perceived as stupid, gullible and undeserving of justice. The reasons for this are under-researched, and there are currently no satisfactory explanations of why victim-blaming occurs so frequently in cases of online fraud. This paper aims to propose a potential theoretical explanation for the high level of blame experienced by online fraud victims. Design/methodology/approach Lerner’s Belief in a Just World (BJW) theory is posited as a helpful theoretical explanation for the high level of blame directed towards victims of online fraud. Findings This paper argues that Lerner’s BJW theory is a helpful framework for understanding the blame faced by victims of online fraud because it posits that behavioural responsibility (a trait commonly ascribed to online fraud victims) is central to perceived blameworthiness; and that compensation for a crime determines the level of blame directed towards victims. As victims of online fraud are exceptionally unlikely to receive any type of compensation (whether monetary or otherwise), BJW may help explain the blame directed towards victims. Originality/value Prior scholarship predominantly understands the blame faced by online fraud victims through the lens of Nils Christie’s (1986) “ideal victim” thesis. This paper presents an advance over this existing understanding by illustrating how BJW provides a more detailed explanation for victim blame in online fraud.
Sa'ah: Creative Eco-friendly Mobile Applicatio That Encourages Living Sustainably

In this time and age, with global warming and pollution rising by the minute, encouragement must be given to lessen their effect and protect our planet. Sa&#x0027;ah app was developed with the hope of making the effort of reducing the waste that gets thrown out every day, easier. It targets three categories of waste reductions; reduce, reuse, and recycle. We reduce the waste by suggesting alternatives to reusing items and recycling them as well as the option of lending them out or simply donating them. Sa&#x0027;ah app targets all the netizens by providing a platform for easy access and real time communication between users for instructiveness and bigger support. It solves the problem of having to make more than one connection and more than one action to reach the desired result by giving access to all the solutions needed in just one platform.
Assamese Dialect Identification using Semi-supervised Learning

The novel approach of this paper is the introduction of a novel information blending paradigm. Two basic classifiers are first trained using the shifting delta cepstra (SDC) and prosodic features, respectively. To improve Assamese dialect detection accuracy, the co-training approach is used in semi-supervised learning. This system evaluates four Assamese dialects. The experimental data show that the suggested system performs better than the existing system that used GMM.
Visualizing and Reasoning about Presentable Digital Forensic Evidence with Knowledge Graphs

Making digital evidence presentable is hard due to the intangible and complex nature of digital evidence and the variety of targeted audiences. In this paper, we present Digital Forensic Knowledge Graph (DFKG) for visualizing and reasoning about digital forensic evidence. We first describe the criteria of presentable evidence to ensure authenticity, integrity, validity, credibility, and relevance of evidence. Then we specify DFKG to capture presentable forensic evidence from three perspectives: (1) the background of a criminal case, (2) the reconstructed timeline of a criminal case, and (3) the verifiable digital evidence related to the criminal activity timeline. We also present a case study to illustrate the DFKG-based approach.
Hypothesis Exploration in Multiple Hypothesis Tracking with Multiple Clusters

Finding the most probable posterior hypotheses is a core task in hypothesis-oriented multiple hypothesis tracking (HO-MHT), and also in related tracking methods such as the Poisson multi-Bernoulli mixture (PMBM) filter. The traditional approach is to find the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> best new hypotheses for each parent hypothesis by means of Murty's algorithm. In this paper we instead present an algorithm for finding the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> best hypotheses ranging over all parent hypotheses. The algorithm is developed in the more general context of cluster management, where the goal is to merge several parent clusters, and to find the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> best posterior hypotheses in any such supercluster.
Forensic Intelligence- Combining Artificial Intelligence with Digital Forensics

Digital forensics is the process of preserving, detecting, retrieving, and documenting computer evidence that can be used in a court of law.It's the art and science of extracting data from digital media like a computer, smartphone, server, or network. In traditional forensic investigations, the expertise and previous experience of an expert or any agent play a critical part in the investigation's success, efficiency, and effectiveness. Applying computational intelligence into this investigation system will increase the efficiency and productivity. With the advent of new tools and technologies available, the rate of cyber crime has increased too. The current tools and technology necessitate large-scale human contact, which slows down the process. An Artificial Intelligence (AI) based system has been proposed and investigated in this research, which will optimize the entire digital forensics process.
A Shallow Convolution Neural Network for Predicting Lung Cancer Through Analysis of Histopathological Tissue Images

Cancer remains an ailment that affects thousands of people resulting in the loss of life. Early diagnosis is essential for effective cancer treatment as cancer is not treatable once it has progressed to later stages. An optimized low-computational-cost shallow convolutional neural network-based framework for cancer diagnosis through analysis of histopathological tissue samples of the lung has been proposed. The proposed framework comprises a novel lightweight, efficient and robust diagnostic clas-sification network. The framework was trained on the LC25000 dataset and found to perform with an accuracy of 99.8% on the training set, 98.2% on the validation set, and 98.1 % on the test set. The performance of the proposed network was compared with other state-of-the-art networks and it was found that the proposed model was definitive for diagnosing unseen lung cancer images. The framework is capable of performing better than resource-intensive machine learning algorithms and can be deployed with limited computation and memory resource requirements.
Deep Learning-Based Optimal Transmission of Embedded Images Over Interference Channels

In this paper, we consider the transmission of embedded (or progressive) images over interference channels for the purposes such as surveillance, and special missions of public safety or emergencies. When transmitting embedded images, the joint optimization of source and channel coding for a series of numerous packets has been a challenging problem. Further, the problem is more complicated if the interference is present and the relevant transmit signal power control is also involved with the optimization. This is because the number of ways of jointly optimizing transmit power and a set of channel codes to be assigned to progressive packets is much larger than that of solely optimizing a set of channel codes. For a single communication link, there have been many studies about the optimal allocation of channel codes to progressive packets, but those studies do not provide a solution for the aforementioned optimization problem. We propose a neural network based optimization method to address such issues in interference channels. Specifically, the proposed scheme incorporates a fully-connected layer based neural network, where the input layer takes the distortion-rate characteristics of images to be transmitted, and the estimate of channel propagation effects such as path loss and shadow fading. Then, the network predicts the optimal transmit power and a set of optimal channel codes to be assigned to progressive packets. We demonstrate that a neural network can analyze and learn the highly nonlinear distortion-rate characteristics of images, which are associated with wireless channel fading effects, and can com up with the relevant optimization strategy. Compared to the baseline method that separately optimizes transmit power and a set of channel codes, our scheme offers significantly improved peak-signal-to-noise ratio performance.
Automatic registration of urban high-resolution remote sensing images based on characteristic spatial objects

Automatic registration of high-resolution remote sensing images (HRRSIs) has always been a severe challenge due to the local deformation caused by different shooting angles and illumination conditions. A new method of characteristic spatial objects (CSOs) extraction and matching is proposed to deal with this difficulty. Firstly, the Mask R-CNN model is utilized to extract the CSOs and their positioning points on the images automatically. Then, an encoding method is provided to encode each object with its nearest adjacent 28 objects according to the object category, relative distance, and relative direction. Furthermore, a code matching algorithm is applied to search the most similar object pairs. Finally, the object pairs need to be filtered by position matching to construct the final control points for automatic image registration. The experimental results demonstrate that the registration success rate of the proposed method reaches 88.6% within a maximum average error of 15 pixels, which is 28.6% higher than that of conventional optimization method based on local feature points. It is reasonable to believe that it has made a beneficial contribution to the automatic registration of HRRSIs more accurately and efficiently.
Consistency between the targeted achievement levels of the 2015 revised national curriculum of English and the assessment levels of the College Scholastic Ability Test of English in South Korea

This paper aims to investigate the consistency between the achievement levels stated in the national curriculum of English and the national-level English test. The study first analyzed the target levels of the achievement standards and the basic word list included in the 2015 revised national curriculum according to the CEFR levels. Next, it compared them to those of the College Scholastic Ability Test (CSAT) of English that students take at the completion of public education in South Korea. The examination of the difficulty levels of the CSAT included the English reading passages from 2016 to 2019 academic years and focused on the text readability. The results indicated that the targeted achievement levels presented in the curriculum gradually increased by school levels, reaching CEFR B2 level in all language skills by completing high school education. In contrast, the English reading section of the CSAT included a significant portion of the passages whose difficulty levels were higher than CEFR B2+. The required word knowledge was also found to be about 7,000- to 8,000- word frequency levels. Based on these findings, this paper discusses factors that contribute to the lack of consistency between the national English curriculum and the test with some relevant implications.