Reviewerly/exHarmony huggingface dataset:
An Overview of Face Manipulation Detection

Due to the power of editing tools, new types of fake faces are being created and synthesized, which has attracted great attention on social media. It is reasonable to acknowledge that one human cannot distinguish whether the face is manipulated from the real faces. Therefore, the detection of face manipulation becomes a critical issue in digital media forensics. This paper provides an overview of recent deep learning detection models for face manipulation. Some public dataset used for face manipulation detection is introduced. On this basis, the challenges for the research and the potential future directions are analyzed and discussed.
Private Reinforcement Learning with PAC and Regret Guarantees

Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL). We first provide a meaningful privacy formulation using the notion of joint differential privacy (JDP)–a strong variant of differential privacy for settings where each user receives their own sets of output (e.g., policy recommendations). We then develop a private optimism-based learning algorithm that simultaneously achieves strong PAC and regret bounds, and enjoys a JDP guarantee. Our algorithm only pays for a moderate privacy cost on exploration: in comparison to the non-private bounds, the privacy parameter only appears in lower-order terms. Finally, we present lower bounds on sample complexity and regret for reinforcement learning subject to JDP.
Anti-Drone System: A Visual-based Drone Detection using Neural Networks

A system that secures an area from trespassing drones that might bring threat is in demand these days since drones became easily-available to the public and it became easier to operate. This paper proposes an anti-drone system that uses visual sensing to detect drones. A Faster R-CNN (Region-based Convolutional Neural Network) with ResNet-101 (Residual Neural Network-101) networks are used in this paper using a dataset from the SafeShore project. The network's accuracy is 93.40% and it has successfully detected drones in the simulation that has been done.
Situated AR Simulations of a Lantern Festival Using a Smartphone and LiDAR-Based 3D Models

A lantern festival was 3D-scanned to elucidate its unique complexity and cultural identity in terms of Intangible Cultural Heritage (ICH). Three augmented reality (AR) instancing scenarios were applied to the converted scanned data from an interaction to the entire site; a forward additive instancing and interactions with a pre-defined model layout. The novelty and contributions of this study are three-fold: documentation, development of an AR app for situated tasks, and AR verification. We presented ready-made and customized smartphone apps for AR verification to extend the model’s elaboration of different site contexts. Both were applied to assess their feasibility in the restructuring and management of the scene. The apps were implemented under a homogeneous and heterogeneous combination of contexts, originating from an as-built event description to a remote site as a sustainable cultural effort. A second reconstruction of screenshots in an AR loop process of interaction, reconstruction, and confirmation verification was also made to study the manipulated result in 3D prints.
Improved wavelet packet denoising algorithm using fuzzy threshold and correlation analysis for chaotic signals

The research of an effective denoising algorithm for the actual obtained signals with chaotic characteristics is of great interest to all fields of the subject. To show the true chaotic state and analyze the dynamic characteristics of the chaotic system more accurately, an improved denoising algorithm using wavelet packet is proposed in this paper. Wavelet packet decomposition has an optimal sub-band tree structure, which can be used for local analysis of chaotic signals. Based on the correlation function value differences of wavelet packet coefficients, the algorithm determines the optimal decomposition layer, while the optimal wavelet packet basis is obtained with logarithmic energy entropy as the cost function. Furthermore, on the one hand, it makes efforts to divide wavelet packet coefficients into approximate parts, fuzzy parts and detail parts. On the other, it carries out singular spectrum analysis, the fuzzy threshold and the correlation analysis for the select of these three different types of coefficients in order to retain the dynamic performance of chaotic signals in the greatest extent. To verify the effectiveness of the algorithm, the Lorenz chaotic model is employed to analyzed. Simulation results verify the practicability of the improved denoising algorithm, which can also be well applied to various chaotic signals denoising with different noise levels.
Class imbalance in gradient boosting classification algorithms: Application to experimental stroke data

Imbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Imbalanced data hinder the performance of conventional classification methods which aim to improve the overall accuracy of the model without accounting for uneven distribution of the classes. To rectify this, the data can be resampled by oversampling the positive (minority) class until the classes are approximately equally represented. After that, a prediction model such as gradient boosting algorithm can be fitted with greater confidence. This classification method allows for non-linear relationships and deep interactive effects while focusing on difficult areas by iterative shifting towards problematic observations. In this study, we demonstrate application of these methods to medical data and develop a practical framework for evaluation of features contributing into the probability of stroke.
Analysis of Machine Learning Approaches for Sentiment Analysis of Twitter Data

For the past few years, sentiment analysis has been growing rapidly and with the abundance of computation power and plethora of machine learning algorithms, sentiment analysis has found numerous applications and acceptance as research area in machine learning. This paper covers analysis of sentiment analysis dealing with different aspects of its applications such as customer reviews, product reviews, film reviews, emotion detection, market research or many more such areas. To conduct sentiment analysis, data is extracted from various social media platforms like Twitter, Facebook etc. The data available on these social media platforms is primarily unstructured, therefore to analyze this data it must be pre-processed, feature vector identified and further implementation of models to trained and tested on different algorithms. There are several algorithms such as SVM, Naïve Bayes, K -means, KNN, decision tree, random forest and other algorithms, which are used to evaluate and hybrid to improve the efficiency and accuracy of the model.
Learned image and video compression with deep neural networks

This tutorial aims at reviewing the recent progress in the deep learning based data compression, including image compression and video compression. In the past years, deep learning techniques have been successfully applied to a large number of computer vision and image processing tasks. However, for the data compression task, the traditional approaches (i.e., block based motion estimation and motion compensation, etc.) are still widely employed in the mainstream codecs. Considering the powerful representation capability, it is possible to improve the data compression performance by employing the advanced deep learning technologies. To this end, deep leaning based compression approaches have recently received significant attention from both academia and industry in the field of computer vision and image/video compression. In this tutorial, we will introduce the related deep learning techniques for image compression and video compression. Specifically, in this tutorial, we will first introduce the basic pipeline for the traditional codecs, such as JPEG, H.264 and HEVC. Then, we will discuss the common network architectures for visual data compression and analyse different learning based entropy models. Based on these techniques, we will describe several widely used end-to-end optimized frameworks for visual data compression. In summary, our tutorial will cover both the traditional data coding techniques and the popular learning based visual data compression algorithms, which will help the audiences with different backgrounds learn the recent progresses in this emerging research area.
Build Your First Neural Network with Google Colab

We work through a complete deep learning example with Python’s TensorFlow 2.x library in the Google Colab cloud service. We also demonstrate how to link your Google Drive with the Colab cloud service.
BNnet: A Deep Neural Network for the Identification of Satire and Fake Bangla News

Misleading and fake news in rapidly increasing online news portals in Bangladesh has become a major concern to both the government and public lately, as a substantial amount of incidents have taken place in different cities due to unwarranted rumors over the last couple of years. However, the overall progress of research and innovation in detecting fake and satire Bangla news is yet unsatisfactory considering the prospects it would bring to the decision-makers of Bangladesh. In this study, we have amalgamated both fake and real Bangla news from quite a pool of online news portals and applied a total of seven prominent machine learning algorithms to identify real and fake Bangla news, proposing a Deep Neural Network (DNN) architecture. Using a total of five evaluation metrics: Accuracy, Precision, Recall, F1 score, and AUC, we have discovered that DNN model yields the best result with an accuracy and AUC score of 0.90 respectively while Decision Tree performs the worst.
Dataset Condensation with Gradient Matching

As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing them and training models on them becomes more expensive. This paper proposes a training set synthesis technique for \emph{data-efficient} learning, called \emph{Dataset Condensation}, that learns to condense a large dataset into a small set of informative samples for training deep neural networks from scratch. We formulate this goal as a gradient matching problem between the gradients of a deep neural network trained on the original data and our synthetic data. We rigorously evaluate its performance in several computer vision benchmarks and demonstrate that it significantly outperforms the state-of-the-art methods. Finally we explore the use of our method in continual learning and neural architecture search and show that it achieves promising gains on a tight budget of memory and computations.
Chaotic Hooke-Jeeves Algorithm using Cubic map with MATLAB code

This paper describes and provides MATLAB code for a hybrid algorithm that finds the global minimum for n-dimensional unconstrained optimization problems using Cubic chaotic map in stage-1 and Hooke-Jeeves method in stage-2. Eight benchmark functions are considered for evaluating the MATLAB code. The results that are obtained are tabulated for different dimensions such as 10D, 50D, 100D and 500D.
Analysis of interference microscopy in the spatial frequency domain

Abstract If high numerical apertures are used in coherence scanning interferometry, an extension of the interference signal’s spectral distribution to lower frequencies can be observed. Depending on the slope of the measured surface interference signal contributions belonging to higher frequencies will vanish. In addition, the high spatial frequency information of a measured surface structure will contribute to the low frequency components of the spectrum of the measured interference signals. These effects can be explained by analyzing both the measuring object as well as the transfer characteristics of the interference microscope in the 3D spatial frequency domain. In this study we analyze the mentioned effects based on Kirchhoff’s diffraction theory in the spatial frequency domain introducing the double foil model. The model explains why the choice of the wavelength, which is used for signal analysis, shows a substantial impact on the reconstructed topography. As a consequence, careful analysis of the 3D transfer function based on the Ewald sphere model enables a better understanding of the measuring process, the lateral resolution capabilities, and the improvement of the measurement results by choosing adequate signal processing parameters.
A low-cost and highly compact FPGA-based encryption/decryption architecture for AES algorithm

Nowadays, the design of ultra-compact area advanced encryption standard (AES) architectures is highly demanded by the electronics industry since many of these architectures are embedded in portable devices, such as smart phones, tablets, etc., in which the area is critically limited. Until now, many approaches have been proposed to create high-processing and compact architectures. However, the area consumption is still a factor to be improved. In this paper, a highly compact encryption/decryption architecture, which is implemented in a low-cost FPGA, to efficiently simulate the AES algorithm, is proposed. Specifically, an optimized Galois Field Multiplier, which is the most demanding operation in terms of area consumption and processing speed, involved in Mix-Columns and Inverse Mix-Columns transformations, is presented. Therefore, the optimization of the proposed GF (28) multiplier by two has allowed to us create an ultra-compact Mix-Columns circuit since this circuit involves large number of multiplications. In addition, the design involves a routing circuit which allowed the proposed architecture to perform encryption or decryption by using common modules. The results demonstrate that the proposed digital circuit expends fewer LUTs and fewer registers when compared with the most compact encryption/decryption architectures reported to date.
Computing longest palindromic substring after single-character or block-wise edits

Palindromes are important objects in strings which have been extensively studied from combinatorial, algorithmic, and bioinformatics points of views. It is known that the length of the longest palindromic substrings (LPSs) of a given string T of length n can be computed in O(n) time by Manacher's algorithm [12]. In this paper, we consider the problem of finding the LPS after the string is edited. We present an algorithm that uses O(n) time and space for preprocessing, and answers the length of the LPSs in O(log⁡(min⁡{σ,log⁡n})) time after a single character substitution, insertion, or deletion, where σ denotes the number of distinct characters appearing in T. We also propose an algorithm that uses O(n) time and space for preprocessing, and answers the length of the LPSs in O(ℓ+log⁡log⁡n) time, after an existing substring in T is replaced by a string of arbitrary length ℓ.
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning

Compared to conventional zero-shot learning (ZSL) where recognising unseen classes is the primary or only aim, the goal of generalized zero-shot learning (GZSL) is to recognise both seen and unseen classes. Most GZSL methods typically learn to synthesise visual representations from semantic information on the unseen classes. However, these types of models are prone to overfitting the seen classes, resulting in distribution overlap between the generated features of the seen and unseen classes. The overlapping region is filled with uncertainty as the model struggles to determine whether a test case from within the overlap is seen or unseen. Further, these generative methods suffer in scenarios with sparse training samples. The models struggle to learn the distribution of high dimensional visual features and, therefore, fail to capture the most discriminative inter-class features. To address these issues, in this paper, we propose a novel framework that leverages dual variational autoencoders with a triplet loss to learn discriminative latent features and applies the entropy-based calibration to minimize the uncertainty in the overlapped area between the seen and unseen classes. To calibrate the uncertainty for seen classes, we calculate the entropy over the softmax probability distribution from a general classifier. With this approach, recognising the seen samples within the seen classes is relatively straightforward, and there is less risk that a seen sample will be misclassified into an unseen class in the overlapped region. Extensive experiments on six benchmark datasets demonstrate that the proposed method outperforms state-of-the-art approaches.
Automatic aircraft detection in very-high-resolution satellite imagery using a YOLOv3-based process

Aircraft detection in remote-sensing images is a fundamental task in civil and military applications. Deep learning techniques to achieve end-to-end object detection have attracted the attention of the Earth observation community. One of the primary factors behind the success of deep learning techniques is the utilized data. Several previous studies focused on designing the network infrastructure. Instead, this study pays more attention to the data. With the increasing number of available public datasets, whether directly employing a large number of instances with great variation will lead to a good performance has become a research topic. The ways in which these object instances are collected differ greatly. For example, the image sizes, object sizes in the training images, and geospatial resolution are varied. Therefore, herein, the factors influencing the detection performance, such as the object size, ground sampling distance, and Google zoom view, are investigated. A you-only-look-once-v3-based detection process is proposed for automatic aircraft detection. A nonmaximum suppression algorithm strategy is applied to filter unreliable and redundant bounding boxes detected in the overlapping image blocks. The model generalization ability under different training data combinations is evaluated in several challenging cases. The results prove that more variety of training instances from a greater variety of zoom levels will result in more false alarms. Instead, more variety in the object sizes under a constant zoom level is welcome. A large range of aircraft sizes (i.e., 7 to 77 m in length in this study) can be detected, with a promising F1 score of 0.98.
BBB: A Lightweight Approach to Evaluate Private Blockchains in Clouds

Evaluating Blockchain performance is not an easy task. It is difficult to compare different systems, since the evaluation is often incomprehensible and conducted in different environments with distinct workloads. Only a handful of prior tools were proposed, e.g., BLOCKBENCH and HFBench. Unfortunately, these tools have several limitations. We first identify these limitations. Second, motivated by our observations, we then present a benchmarking tool, Boston Blockchain Benchmarking (BBB). BBB is configurable, extensible, and easy-touse. In particular, BBB can be used to test Blockchain from a networking perspective, a feature that we have not observed in prior tools. Similar to BLOCKBENCH, we focus on the private Blockchain. Concretely, we integrate our tool with Mininet, and provide a simple mechanism to test how network properties (e.g., latency, bandwidth, package loss rate) affect the performance of the chosen Blockchain. We present our preliminary result of evaluating Ethereum. We stress that the architecture of BBB is general, and could be extended to other Blockchain systems. BBB is extremely lightweight and can be used on your laptop to test a small network. Such a feature allows quick evaluation of the Blockchain and speeds up innovation and development.
OPPORTUNITIES AND RESULTS TO INCREASE THE EFFECTIVENESS OF MULTIMEDIA TEACHING IN HIGHER EDUCATION

Theoretical analysis of the development of education in modern conditions, the direction of a competent approach and the widespread use of information technology in high school and university raise the need to study the development traditions of teaching methods based on modern forms, methods and tools (including multimedia technology).The basic methodology of holistic development is based on multimedia technologies. As there are a number of issues that students need to address in the field of their interests and perspectives, the prospects of multimedia education and its application to higher education lead to a high level of authority among students.In recent years, new areas and types of work have begun to emerge much faster, and the process of updating the content of higher education, more precisely, higher education, has not been able to catch up with them: there are delays in the organization of work on renewal. Development agrees with this situation because it allows the education system to focus only on cultural practices that have remained relatively stable, rather than on every change in the situation. Problems begin to emerge when education begins to resist change or does not respond to it as needed.The analysis of world educational development experiences shows three main traditions of development: the development of carriculum theory, the concept of education permanente, and the idea of competence clefs.The following results have been achieved by advancing the carriculum theory from the three main traditions of the development of world education mentioned above.
An Examination of the Algorithmic Accountability Act of 2019

The Algorithmic Accountability Act of 2019, sponsored by Senators Cory Booker (D-NJ) and Ron Wyden (D-OR), with a House equivalent sponsored by Rep. Yvette Clarke (D-NY), requires companies to assess their automatic decision systems for risks to “privacy and security of personal information” and risks of “inaccurate, unfair, biased, or discriminatory decisions.” They must also “reasonably address” the results of their assessments. The bill empowers the Federal Trade Commission (FTC) to resolve by regulation the crucial details of these requirements. It is not likely to pass Congress on its own, but it might become part of a new national privacy law currently under consideration in Congress. If passed, it would apply to the AI systems that platforms increasingly deploy to detect and counter hate speech, terrorist material and disinformation campaigns and would require the platforms to conduct fairness assessments of these AI systems and fix issues of bias uncovered in these studies. Even without a legislative mandate, however, platforms should rigorously review algorithmic content moderation systems for fairness and accuracy and should establish and maintain effective, easy-to-use complaint mechanisms whereby people wrongly labeled as purveyors of harmful material can obtain redress.
On the Transfer of Disentangled Representations in Realistic Settings

Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and real-world impact remain questionable. We introduce a new high-resolution dataset with over 1M simulated images and 1k annotated real-world images of the same setup. In contrast to previous work, this new dataset exhibits correlations, a complex underlying structure, and allows to evaluate transfer to unseen simulated and real-world settings where the encoder i) remains in distribution or ii) is out of distribution. We propose new architectures in order to scale disentangled representation learning to realistic high-resolution settings and conduct a large-scale empirical study of disentangled representations on this dataset. We observe that disentanglement is a good predictor for out-of-distribution (OOD) task performance.