Reviewerly/exHarmony huggingface dataset:
Deepfake Detection Using Xception and LSTM

Deepfakes are fabricated works of art where a person appearing in an earlier photograph or motion picture is modified to exhibit the face of another person.A deep learning-based generative model called a Generative Adversarial Network (GAN) is a model architecture for training generative models to create fake videos.In the context of GANs, the generation model lends significance to points in a predetermined latent space, enabling fresh points pulled from the latent space to be fed to the generator model as input and utilized to produce brand-new and distinctive output instances.Due to this, it is simple to use GANs to build deep fakes that can be used inappropriately in various contexts.Due to the engrossing misuse of deepfakes, there is a need for appropriate detection tools.Deepfake detection requires a large amount of data to train models and test them.They require large datasets that contain thousands of videos, both real and fake at equal ratios to avoid biased results.This paper works with deep learning-based deep fake detection using Convolutional Neural Network(CNN) and Recurrent Neural Network(RNN), CNN utilizing the Xception network, and ytLSTM as RNN.In the algorithm, the spatial features are recognized by the Xception and LSTM identifies the temporal inconsistency between frames.The models are trained against three standard datasets.The results are also validated with standard datasets resulting in deepfake prediction with a minimal computational time and nominal accuracy.
Build Student Forum Website Using UI concepts

An effective website design for a student forum should prioritize ease of use, accessibility, and a visually appealing interface.The website should be designed with the target audience in mind, with a focus on creating a welcoming and inclusive environment for students to connect and engage with each other.The website should have a clear and intuitive navigation structure, with easily accessible links to important pages such as forums, events, and resources.It should also have a responsive design that ensures it is accessible on a variety of devices, including laptops, tablets, and smartphones.In terms of visual design, the website should be visually appealing, with a cohesive color scheme and clean layout.It should also utilize appropriate images and graphics to enhance the user experience and make the site more engaging.Overall, a well-designed website for a student forum should provide a user-friendly and visually appealing platform for students to connect, collaborate, and share ideas with each other.
Structural representative network for remote sensing image captioning

Current encoder-decoder methods for remote sensing image captioning (RSIC) avoids fine-grained structural representation of objects due to the lack of prominent encoding frameworks. This paper proposes a novel structural representative network (SRN) for acquiring fine-grained structures of remote sensing images (RSI) for generating semantically meaningful captions. Initially, we employ SRN on top of the final layers of the convolutional neural network (CNN) for attaining the spatially transformed RSI features. A multi-stage decoder is incorporated into the extracted features of SRN to produce fine-grained meaningful captions. The efficacy of our proposed methodology is exhibited on two RSIC datasets, i.e Sydney-Captions dataset, and the UCM-Captions dataset.
A graph-based approach to video anomaly detection from the perspective of superpixels

Video Anomaly Detection refers to the concept of discovering activities in a video feed that deviate from the usual visible pattern. It is a very well-studied and explored field in the domain of Computer Vision and Deep Learning, in which automated learning-based systems are capable of detecting certain kinds of anomalies at an accuracy greater than 90%. Deep Learning based Artificial Neural Network models, however, suffer from very low interpretability. In order to address and design a possible solution for this issue, this work proposes to shape the given problem by means of graphical models. Given the high flexibility of compositing easily interpretable graphs, a great variety of techniques exist to build a model representing spatial as well as temporal relationships occurring in the given video sequence. The experiments conducted on common anomaly detection benchmark datasets show that significant performance gains can be achieved through simple re-modelling of individual graph components. In contrast to other video anomaly detection approaches, the one presented in this work focuses primarily on the exploration of the possibility to shift the way we currently look at and process videos when trying to detect anomalous events.
Reconstructing Transnational History

Web-based interactive documentaries offer an innovative approach to reconstructing history, allowing users to explore historical content within a specific thematic context freely. This paper explores how transnational shared history, exemplified by World War I (WWI), is integrated into such documentaries, using the interactive documentary A Global Guide to the First World War (2014) as a case study. This documentary, a collaboration between the Guardian, historians, and the Imperial War Museum, employs interactive technologies and transnational historical narratives. It presents seven video chapters that allow audiences to explore WWI from a global perspective, going beyond the traditional single-narrative viewpoint. The interactive sections are based on a well-designed digital world map that provides users with diverse perspectives on the war, facilitating a broader emotional resonance. However, while ensuring authenticity, the documentary's use of authoritative narrative strategies tends to create a sense of alienation among viewers. It underscores the importance of incorporating personal narratives alongside official histories to foster deeper engagement and understanding. The paper argues for a more nuanced approach that balances the authoritative historical narratives with individual perspectives. This study demonstrates that the representation of transnational shared history and multiple perspectives is highly compatible with the interactive technology of web documentaries. The paper concludes that these digital platforms can effectively facilitate global understanding of shared historical events while encouraging critical and dialectical historical reflection.
AN EFFICIENT ATTRIBUTE REVOCATION BASED DATA SHARING FOR RESOURCE LIMITED USER THROUGH XAMPP SERVER

Attribute-based encryption effectively achieves fine-grained access control.It is an ideal access control strategy for cloud computing and the Internet of Things, two new computing environments with strong privacy and large-scale dynamics.Using ciphertext policy attributed-based encryption, we present an effective and demonstrated secure cloud data sharing strategy (ABEDS-RR) in this paper.A semi-trusted proxy party is needed for the scheme to use a conversion key to change some of the ciphertext.When compared to most current schemes, only the user's private key and conversion key must be altered when changing a user's attributes.The plan can be extended to include outsourcing decryption to better adapt to situations requiring limited computing capacity, such as ad hoc networks, smart sensors, and mobile devices.By co-generating user keys between two entities, the plan also solves key escrow issues and adds weight attributes to the ability to express attributes.
Autonomous face mask detection using single shot multibox detector, and ResNet-50 with identity retrieval through face matching using deep siamese neural network

The COVID-19 pandemic poses a global health challenge. The World Health Organization states that face masks are proven to be effective, especially in public areas. Real-time monitoring of face masks is challenging and exhaustive for humans. To reduce human effort and to provide an enforcement mechanism, an autonomous system has been proposed to detect non-masked people and retrieve their identity using computer vision. The proposed method introduces a novel and efficient method that involves fine-tuning the pre-trained ResNet-50 model with a new head layer for classification between masked and non-masked people. The classifier is trained using adaptive momentum optimization algorithm with decaying learning rate and binary cross-entropy loss. Data augmentation and dropout regularization are employed to achieve best convergence. During real-time application of our classifier on videos, a Caffe face detector model based on Single Shot MultiBox Detector is used to extract the face regions of interest from each frame, on which the trained classifier is applied for detecting the non-masked people. The faces of these people are then captured, which is passed on to a deep siamese neural network, based on VGG-Face model for face matching. The captured faces are compared with the reference images from the database, by extracting the features and calculating cosine distance. If the faces match, the details of that person are retrieved from the database and displayed on the web application. The proposed method has secured best results where the trained classifier has achieved 99.74% accuracy, and the identity retrieval model achieved 98.24% accuracy.
PMDG: Privacy for Multi-perspective Process Mining Through Data Generalization

Anonymization of event logs facilitates process mining while protecting sensitive information of process stakeholders. Existing techniques, however, focus on the privatization of the control-flow. Other process perspectives, such as roles, resources, and objects are neglected or subject to randomization, which breaks the dependencies between the perspectives. Hence, existing techniques are not suited for advanced process mining tasks, e.g., social network mining or predictive monitoring . To address this gap, we propose PMDG, a framework to ensure privacy for multi-perspective process mining through data generalization. It provides group-based privacy guarantees for an event log, while preserving the characteristic dependencies between the control-flow and further process perspectives. Unlike existing privatization techniques that rely on data suppression or noise insertion, PMDG adopts data generalization: a technique where the activities and attribute values referenced in events are generalized into more abstract ones, to obtain equivalence classes that are sufficiently large from a privacy point of view. We demonstrate empirically that PMDG outperforms state-of-the-art anonymization techniques, when mining handovers and predicting outcomes.
Information Preserving and Edge Smoothening of Fetal Heart Chamber Using SRDCF with Total Variation

Speckle is an inherent coarse noise that degrades medical ultrasound images.A point is created by the interference of the return wave in the sensor hole.In each resolution cell, several primary scatterers replicate the wave impinging on the sensor.In addition, the obtained images are corrupted by random grain patterns, making image interpretation difficult.Stains may contain useful diagnostic information.The smoothness of spotting depends on the application and knowledge of the doctor.In this work, preprocessing of fetal heart ultrasound images using a Spatially Regularized Discriminative Correlation Filter (SRDCF) with total variation is introduced to preserve fetal heart information and smooth edges.Then qualitatively and quantitatively compare the performance of the proposed filter with conventional methods.
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal

The k-space data generated from magnetic resonance imaging (MRI) is only a finite sampling of underlying signals. Therefore, MRI images often suffer from low spatial resolution and Gibbs ringing artifacts. Previous studies tackled these two problems separately, where super resolution methods tend to enhance Gibbs artifacts, whereas Gibbs ringing removal methods tend to blur the images. It is also a challenge that high resolution ground truth is hard to obtain in clinical MRI. In this paper, we propose an unsupervised learning framework for both MRI super resolution and Gibbs artifacts removal without using high resolution ground truth. Furthermore, we propose regularization methods to improve the model’s generalizability across out-of-distribution MRI images. We evaluated our proposed methods with other state-of-the-art methods on eight MRI datasets with various contrasts and anatomical structures. Our method not only achieves the best SR performance but also significantly reduces the Gibbs artifacts. Our method also demonstrates good generalizability across different datasets, which is beneficial to clinical applications where training data are usually scarce and biased.
POLAFFINI: Efficient Feature-Based Polyaffine Initialization for Improved Non-linear Image Registration

This paper presents an efficient feature-based approach to initialize non-linear image registration. Today, nonlinear image registration is dominated by methods relying on intensity-based similarity measures. A good estimate of the initial transformation is essential, both for traditional iterative algorithms and for recent one-shot deep learning (DL)-based alternatives. The established approach to estimate this starting point is to perform affine registration, but this may be insufficient due to its parsimonious, global, and non-bending nature. We propose an improved initialization method that takes advantage of recent advances in DL-based segmentation techniques able to instantly estimate fine-grained regional delineations with state-of-the-art accuracies. Those segmentations are used to produce local, anatomically grounded, feature-based affine matchings using iteration-free closed-form expressions. Estimated local affine transformations are then fused, with the log-Euclidean polyaffine framework, into an overall dense diffeomorphic transformation. We show that, compared to its affine counterpart, the proposed initialization leads to significantly better alignment for both traditional and DL-based non-linear registration algorithms. The proposed approach is also more robust and significantly faster than commonly used affine registration algorithms such as FSL FLIRT.
Chinese Anti-epidemic Slogan: The Relationship with CP and PP and the Influence from Culture

As a common means of political propaganda in China, slogans effectively convey information in a short and eye-catching form.Since the outbreak of COVID-19 in 2019, slogans have played a major role in alleviating influence of the epidemic.This paper will explore the language characteristics of China's "anti-epidemic slogan" with the theory of Cooperation Principle and Politeness Principle.At the same time, combined with the current situation of Chinese society, it will analyze the "hardcore slogan", a controversial topic, and comprehensively and objectively state why this is necessar.Therefore, it will be able to emphasize the importance of the Chinese traditional core of culture and grass-root culture to language used in propaganda, while helping the outside world to view China's "hardcore slogan" more objectively.
Implementation of artificial intelligence in agriculture for optimisation- Literature review

In daily life, the role of agriculture is crucial.The population is growing rapidly, and with it, is the need for food and work.Farmers employed conventional techniques, but they were unable to meet these demands.As a result, new automated techniques were introduced.These innovative techniques supplied the world's food needs while simultaneously creating jobs for billions of people.With the help of artificial intelligence (AI), agriculture has drastically reformed.This technique has safeguarded crop yields from a variety of conditions including climate change, population increase, job troubles, and food security concerns.The purpose of this literature review research is to examine the different uses of artificial intelligence in agricultural, such as irrigation, weeding and spraying, using sensors and other techniques implanted in robots and drones.These technologies reduce the use of water, pesticides and herbicides, preserve soil fertility and aid in the effective use of labour to increase production and quality.This report analyses the work of several scholars in order to provide a concise summary of the present state of automation in agriculture.
Analysing the Hate Speech Within the Framework of Political Discourse

The author considers hate speech within the broader context of the right to freedom of expression with specific reference to political speech. The European Court of Human Rights in its jurisprudence takes the stance that the protections for freedom of expression extend to content that might be offensive, shocking, and disturbing to someone. It is also well accepted in comparative case law and doctrine that political speech has a privileged position in terms of legal protection when it comes to the greater degree of criticism. On the other hand, it is extremely important to protect individuals and collectives from exposure to hate speech since it does not achieve the objectives of the right to freedom of expression in any way. However, it is clear at first glance that in a large number of cases, there is an intertwining of hate speech with speech to which the law provides legal protection. Content related to racial, negationism, revisionism, religious, ethnic, etc. issues is a legitimate and integral part of political discourse, while a very small space separates them from slipping into the field of hate speech. Although the historical, cultural, sociological, and psychological context is important for the qualification of certain content as hate speech, the author seeks to analyze the basics of the definition of hate speech through a comparative legal approach (UN and other international and regional organizations) to offer a framework for distinguishing hate speech from other permitted content which would be applicable in general, appreciating all the possible variables that affect the qualification of hate speech.
MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference

The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices. A typical dilemma is: in order to provide complex intelligent services (e.g. smart city), we need inference results of multiple ML models, but the cost budget (e.g. GPU memory) is not enough to run all of them. In this work, we study underlying relationships among black-box ML models and propose a novel learning task: model linking, which aims to bridge the knowledge of different black-box models by learning mappings (dubbed model links) between their output spaces. We propose the design of model links which supports linking heterogeneous black-box ML models. Also, in order to address the distribution discrepancy challenge, we present adaptation and aggregation methods of model links. Based on our proposed model links, we developed a scheduling algorithm, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLink</i> . Through collaborative multi-model inference enabled by model links, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLink</i> can improve the accuracy of obtained inference results under the cost budget. We evaluated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLink</i> on a multi-modal dataset with seven different ML models and two real-world video analytics systems with six ML models and 3,264 hours of video. Experimental results show that our proposed model links can be effectively built among various black-box models. Under the budget of GPU memory, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLink</i> can save 66.7% inference computations while preserving 94% inference accuracy, which outperforms multi-task learning, deep reinforcement learning-based scheduler and frame filtering baselines.
A Conceptually Oriented Approach to Semantic Composition in RRG

One of the major concerns of Role and Reference Grammar (RRG) has always been the development of a theory of semantic composition for both state and activity predicates. Over the last thirty years, there have been a variety of proposals aimed at the formulation of a semantic representation system that accounts for the interface between syntax and semantics. Proposals to enrich the RRG semantic representation system involve notations such as the use of internal variables, lexical functions and qualia structure, among others, in an effort to link syntax to conceptual or semantic meaning. Nevertheless, another solution could lie in a more ontologically oriented model of semantic representation that provides a conceptual framework for the formalization of meaning construction. This chapter outlines and describes a semantic representation system in the form of conceptual logical structures (CLSs), based on the FunGramKB ontology.
Another fraud case highlights benefits of routine, independent audits

Yet another case of fraud in the nonprofit sector has made headlines recently, this time in Florida, highlighting the importance of regularly scheduled third‐party audits in detecting and deterring financial misfeasance.
Russian Education in the Pandemic

The paper examines the functioning and change of educational technology in the COVID-19 pandemic in Russia at the level of general and higher education. The authors provide examples of cases from different regions of Russia. Additionally, the authors consider different aspects of the readiness of Russian education to the challenges of online learning, the macro challenge of school education, educational problems of educational organizations, and the complexity of constructing a psychological and motivational atmosphere in the virtual classroom. Thus, the authors consider the readiness of Russian education for the challenges of online learning, including the situation when schools had to move classes from a face-to-face format to distance learning. The unexpected transition to distance learning occurred differently in various subjects of Russia. The authors consider several groups of problems evident in the sudden change of the school regime: macro problems, problems of educational organizations, and problems of families and students. The authors studied pedagogical problems of educational organizations, including appropriate psychological and motivational climate for the successful absorption of knowledge by students. Moreover, the authors analyzed objective and subjective problems in the organization of distance learning in the conditions of general and higher education system.
Pengujian Aplikasi Doctor to Doctor Menggunakan Metode Black Box Testing

The existence of smartphones has an impact on mobile application development which aims to help humans in their daily activities. The Doctor to Doctor (D2D) application is an application that helps doctors get the latest information about health. To maintain the quality of the application so that it is comfortable to use by users, it is necessary to carry out tests with the aim of finding errors in the application. D2D application testing is done manually and automatically based on functional tests. This test uses an automated testing application, namely Katalon Studio. Tests were carried out on 16 of the 42 functions in the D2D application. In this study, we will compare the level of effectiveness of manual and automated testing as well as the advantages and disadvantages of manual and automated testing. Based on the results of the tests that have been carried out, the percentage of successful D2D application testing was 86.89% indicating that 7 of the 16 functions were still not running properly. From the results obtained in terms of time, automated testing using Katalon Studio is not effective enough compared to manual testing.