Reviewerly/exHarmony huggingface dataset:
Deepfake Detection Approaches Using Deep Learning: A Systematic Review

Deepfake algorithms can make forged pictures and videos that people cannot differentiate them from true ones. The suggestion of technology that locate and prove the truth of virtual visual media is as a result essential. Deepfakes generates realistic forged images or videos of targeted persons by swapping their faces another person saying or doing things that are not really done by them and public start trusting in such forged videos, as it is not identifiable with the normal human eye. This paper offers a survey of tools and algorithms used to make deepfakes and, additional significantly, methods to locate deepfakes. We present huge discussions on challenges, studies, advances and strategies associated to deepfake. By reviewing the history of deepfakes and cutting-edge deepfake detection strategies, this gives a comprehensive assessment of deepfake techniques and helps the development of latest and more robust strategies to deal with an increasing number of tough deepfakes.
Quest for the Golden Approach

Background: Given the invisibility and unpredictability of distributed crowdtesting processes, there is a large number of duplicate reports, and detecting these duplicate reports is an important task to help save testing effort. Although, many approaches have been proposed to automatically detect the duplicates, the comparison among them and the practical guidelines to adopt these approaches in crowdtesting remain vague.
A Mutli-Task Mutlimodal Framework for Tweet Classification Based on CNN (Grand Challenge)

The paper describes the system description and design for our solution submitted for the MeToo BigMM Grand Challenge (BMGC). The challenge involves building a multimodal framework for predicting the linguistic aspect of tweets such as relevance, stance, hate speech, sarcasm and dialogue acts. As a whole, there are 10 different categories that are to be predicted. For the challenge we try several different approaches based on Bidirectional LSTM with attention and Convolutional Neural Networks. We describe in detail our insights behind the various steps involved from the preprocessing to defining the model architecture. We further provide a detailed analysis of the results obtained from the challenge. Overall our team ranked 4th on the final leaderboard of the challenge.
Non-Linearities in Gaussian Processes with Integral Observations

Gaussian processes (GP) can be used for inferring latent continuous functions also based on aggregate observations corresponding to integrals of the function, for example to learn daily rate of new infections in a population based on cumulative observations collected only weekly. We extend these approaches to cases where the observations correspond to aggregates of arbitrary non-linear transformations of a GP. Such models are needed, for example, when the latent function of interest is known to be non-negative or bounded. We present a solution based on Markov chain Monte Carlo with numerical integration for aggregation, and demonstrate it in binned Poisson regression and in non-invasive detection of fouling using ultrasound waves.
MERL

Network embedding models aim to learn low-dimensional representations for nodes and/or edges in graphs. For social networks, learning edge representations is especially beneficial as we need to describe or explain the relationships, activities, and interactions between users. Existing approaches that learn stand-alone node embeddings, and represent edges as pairs of node embeddings, are limited in their applicability because nodes participate in multiple relationships, which should be considered. In addition, social networks often contain multiple types of edges, which yields multi-view contexts that need to be considered in the representation. In this paper, we propose a new methodology, MERL, that (1) captures asymmetry in multiple views by learning well-defined edge representations that are responsive to the difference between the source and destination node roles, and (2) incorporates textual communications to identify multiple source of social signals (e.g. strength and affinity) that moderate the impact of different views between users. Our experiments show that MERL outperforms alternative state-of-the-art embedding models on link prediction and multilabel classification tasks across multiple views in social network datasets. We further analyze the learned embeddings of MERL and demonstrate they are more correlated with the existence of view-based edges compared to previous methods.
Feature Selection on Data Stream via Multi-Cluster Structure Preservation

The modern data arrive continuously in a rapid and time-varying stream, which appears to generate unstable associations on the data structure. However, most of the existing methods focus on dealing with the static data, and they cannot fully take them into the structure construction. To address this issue, we propose an online unsupervised Feature Selection method via Multi-Cluster structure Preservation (FSMCP for short). FSMCP weighs all features by minimizing the differences between the Multi-Cluster structures in the original and the selected feature space. The structure integrates the three-level associations, i.e., the individual-level associations, the aggregation-level associations, and the streaming-level associations. To provide informative features in time, FSMCP check and update the associations as soon as new instances arrive. In comparison with the baseline methods, FSMCP holds better efficiency than offline methods, while still providing almost similar or even better quantitative feature subsets. It outperforms the existing online methods with average NMI improvement of 10.33%.
Simultaneous Intent Prediction and State Estimation Using an Intent-Driven Intrinsic Coordinate Model

The motion of an object (e.g. ship, jet, pedestrian, bird, drone, etc.) is usually governed by premeditated actions as per an underlying intent, for instance reaching a destination. In this paper, we introduce a novel intent-driven dynamical model based on a continuous-time intrinsic coordinate model. By combining this model with particle filtering, a seamless approach for jointly predicting the destination and estimating the state of a highly manoeuvrable object is developed. We examine the proposed inference technique using real data with different measurement models to demonstrate its efficacy. In particular, we show that the introduced approach can be a flexible and competitive alternative, in terms of prediction and estimation performance, to other existing methods for various measurement models including nonlinear ones.
Digital Images Preprocessing for Optical Character Recognition in Video Frames Reconstructed from Compromising Electromagnetic Emanations from Video Cables

This work presents an analysis of the performance of two different preprocessing techniques for improving the quality of video frames previously reconstructed from compromising electromagnetic emanations produced by video interfaces connecting external monitors. Each technique depends on parameters such as white and black thresholds, kernel size of blur filter, contrast gain, brightness level, among others, and for this reason, an optimization process is applied by implementing an evolution strategy.
Face Recognition with Explanation by Fuzzy Rules and Linguistic Description

In this paper, a new approach to face recognition is proposed. The knowledge represented by fuzzy IF-THEN rules, with type-1 and type-2 fuzzy sets, are employed in order to generate the linguistic description of human faces in digital pictures. Then, an image recognition system can recognize and retrieve a picture (image of a face) or classify face images based on the linguistic description. Such a system is explainable – it can explain its decision based on the fuzzy rules.
Data management plan scorecard

Abstract Data and how that data are organized, accessed, and used, remain central to the advancement of science, as evidenced through calls for reproducibility and demands to enable re‐use. Data management plans describe the roles, responsibilities, and activities for managing data during and after research. This paper presents the development of a data management plan scorecard. The assessment of data management plans provides one measure to evaluate how data are planned to be shared and a first step down the road towards shareable data. The scorecard provides a widely‐applicable, scalable, and simple tool to assess completeness and quality of 15 data elements commonly required in all data management plans.
OggyBug

Context: Motivated by the reduction in operating costs, the use of chatbots to automate customer service has been growing. Chatbots have evolved a lot in terms of technologies used as well as in the different application areas. Problem: As it is a recent technology, there is no tool offers to support chatbot test automation with the possibility of testing context information that happens in the dialogue between the chatbot and the human; the existing tools also lack facilities to integrate different data sources that are used during the tests. Objective: Propose and evaluate a new framework for chatbot testing that considers context information and allows the integration test between different data sources. Method: from the analysis of the lack of existing works reported in the literature, a framework for self-testing of chatbots called OggyBug was proposed, which was used by two chatbots development teams that provided feedback on their use. Results: Construction of the framework called OggyBug that allows implementing, manage and report the results of the execution of automatic tests for chatbots, either through an API or through a web interface, with ease of integrating different sources of information within the automation scripts. After collecting the feedback from the teams that used the framework, we can observe the ease in defining scenarios and repeating the execution of the tests. Conclusion: Testing in context information proved to be important to verify or define the information of the conversation session. The configuration of integration tests proved to be complex, due to the need to configure web services in the chatbot's actions.
Multilevel metric rank match for person re-identification

Metric learning is one of the important ways to improve the person re-identification (ReID) accurate, of which triplet loss is the most effect metric learning method. However, triplet loss only ranks the extracted feature at the end of the network, in this paper, we propose a multilevel metric rank match (MMRM) method, which ranks the extracted feature on multilevel of the network. At each rank level, the extracted features are ranked to find the hard sample pairs and the backward dissemination triplet loss. Each rank level has different penalize value to adjust the network, in which the value is bigger with the deeper level of the whole network. Experiment results on CUHK03, Market1501 and DukeMTMC datasets indicate that The MMRM algorithm can outperform the previous state-of-the-arts.
How to choose a neural network architecture? – A modulation classification example

Which neural network architecture should be used for my problem? This is a common question that is encountered nowadays. Having searched a slew of papers that have been published over the last few years in the cross domain of machine learning and wireless communications, the authors found that several researchers working in this multi-disciplinary field continue to have the same question. In this regard, we make an attempt to provide a guide for choosing neural networks using an example application from the field of wireless communications, specifically we consider modulation classification. While deep learning was used to address modulation classification quite extensively using real world data, none of these papers give intuition about the neural network architectures that must be chosen to get good classification performance. During our study and experiments, we realized that this simple example with simple wireless channel models can be used as a reference to understand how to choose the appropriate deep learning models, specifically neural network models, based on the system model for the problem under consideration. In this paper, we provide numerical results to support the intuition that arises for various cases.
0.4 mW, 0.27 pJ/bit true random number generator using jitter, metastability and current starved topology

This study introduces the design of true random number generator (TRNG) using jitter, metastability and current starved topology. The proposed design consisted of a current starved inverter-based ring oscillator (RO) with a high-frequency divider block (designed by T-FF followed by D-FF to address setup and hold time issues), jitter extraction and metastable block followed by two sampling blocks. The design avails fewer amenities to yield the reduction in hardware and enhances the degree of randomness. The post-layout simulation of the proposed work was performed using a 180 nm CMOS technology environment in the Cadence Virtuoso tool. The speed and power dissipation achieved are 1.5 Gbps and 0.4 mW, respectively, with an efficiency of 0.27 pJ/bit. The effect of temperature and variation in supply voltages (by 10% around its nominal value) is also investigated on the generated random numbers through parametric analysis. For validation of randomness, the generated random signals are first sampled and then converted in binary format using MATLAB and finally verified by Kolmogorov–Smirnov and Chi-square test for the uniformity and independency. The validity of the proposed work is done by NIST 800.22 statistical test suite. The proposed TRNG design achieved a very high-pass efficiency.
Innovations in Education—The Development of a New Pedagogical Technology of a Combinational Type, Focused on the Development of Personality of Students

The paper considers the problem of adolescent and young students&rsquo; personalities&rsquo; development in the conditions of face-to-face and distance school learning. The scientific novelty of the study is the proposed classification of pedagogical technologies according to the degree of their novelty: modernized technology, combinational technology, progressive technology, pedagogical technology of the combinational type, the essence of which is shown in its title. A specific model of pedagogical technology of the combinational type &ldquo;Teaching in cooperation, in a team using flipped classroom&rdquo; was developed. The developed technology was implemented during the classes of the humanities and natural-mathematical cycles. Nine hundred and eight adolescent and young students (divided into experimental and control groups) and 32 teachers participated in the experimental work. To analyze the differences between the experimental and control groups in terms of the student&rsquo;s personality manifestation, several criteria and indicators were considered. For mathematical and statistical processing of the obtained results, the multifunctional statistical Fisher&rsquo;s F-test was used. Analysis of the implementation of the developed technology in the conditions of face-to-face and distance learning showed a positive dynamics of experience formation of the student&rsquo;s personality manifestation. This allowed the authors to consider the developed pedagogical means effective.
Neverlang and FeatureIDE just married

Language development is inherently complex. With the support of a suitable language development environment most computer scientists could develop their own domain-specific language (DSL) with relative ease. Yet, when the DSL is the result of a configuration over a language product line (LPL)---a special software product line (SPL) of compilers/interpreters and corresponding IDE services---they fail to provide adequate support. An environment for LPL engineering should facilitate the underlying process involving three distinct roles: a language engineer developing the LPL, a language deployer configuring a language product, and a language user using the language product. Neither IDEs nor SPLE environments can cater all three roles and fully support the LPL engineering process with distributed, incremental development, configuration, and deployment of language variants. In this paper, we present an LPL engineering process for the distributed, incremental development of LPLs and an integrated language product line development environment supporting this process, catering the three roles, and ensuring the consistency among all artifacts of the LPL: language components implementing a language feature, the feature model, language configurations and the resulting language products. To create such an environment, we married the Neverlang language workbench and AiDE its LPL engineering environment with the FeatureIDE SPL engineering environment. While Neverlang supports the development of LPLs and deployment of language products, AiDE generates the feature model for the LPL under development, whereas FeatureIDE handles the feature configuration. We illustrate the applicability of the LPL engineering process and the suitability of our development environment for the three roles by showcasing its application for teaching programming with a growable language. In there, an LPL for Javascript was developed/refactored, 15 increasingly complex language products were configured/updated and finally deployed.
Deep Spatio-Temporal Multiple Domain Fusion Network for Urban Anomalies Detection

Multiple domain fusion has been widely used for urban anomalies forecasting problem, as urban anomalies such as traffic accidents or illegal assembly are usually caused by many complex factors and they would affect many fields. Although many efforts have been devoted to fusing multiple datasets for anomalies detection, most of the work is to extract the spatio-temporal features one by one from multiple datasets and then fuse to get the result or anomaly score. However, the correlation between data from multiple domains at each moment is ignored, which is especially important when detecting anomalies by analyzing the impacts from multiple datasets. In this paper, we propose a novel end-to-end deep learning based framework, namely deep spatio-temporal multiple domain fusion network to collect the impacts of urban anomalies on multiple datasets and detect anomalies in each region of the city at next time interval in turn. We formulate the problem on a weighted graph and obtain spatiotemporal features with adaptive graph convolution and temporal convolution. In addition, a cross-domain convolution network is applied to fully obtain connection between multiple domains. We evaluate our method with real-world dataset collected in New York City and experiments on our model show the advantages nearly 10% beyond the state-of-the-art urban anomalies detection methods.
Anomaly Subgraph Detection with Feature Transfer

Anomaly detection in multilayer graphs becomes more critical in many application scenarios, i.e., identifying crime hotspots in urban areas by discovering suspicious and illicit behaviors in social networks. However, it is a big challenge to identify anomalies in a layer graph due to the insufficient anomaly features. Most existing methods of anomaly detection determine whether a node is abnormal by looking at the observable anomalous feature values. However, these methods are not suitable for scenarios in which the abnormal features are scarce, e.g., geometric graphs or non-public data in social network services. In this paper, to detect anomaly in a graph with insufficient anomalous features, we propose a pioneering approach ASD-FT (Anomaly Subgraph Detection with Feature Transfer) based on a strategy of anomalous feature transfers between different layers of a multilayer graph. The proposed ASD-FT detects anomaly subgraphs from the graph of the target layer by analyzing the anomalous features in the graph of another layer. We demonstrate the effectiveness and robustness of our approach ASD-FT with extensive experiments on five real-world datasets.
Bootstrapped model learning and error correction for planning with uncertainty in model-based RL

Having access to a forward model enables the use of planning algorithms such as Monte Carlo Tree Search and Rolling Horizon Evolution. Where a model is unavailable, a natural aim is to learn a model that reflects accurately the dynamics of the environment. In many situations it might not be possible and minimal glitches in the model may lead to poor performance and failure. This paper explores the problem of model misspecification through uncertainty-aware reinforcement learning agents. We propose a bootstrapped multi-headed neural network that learns the distribution of future states and rewards. We experiment with a number of schemes to extract the most likely predictions. Moreover, we also introduce a global error correction filter that applies high-level constraints guided by the context provided through the predictive distribution. We illustrate our approach on Minipacman. The evaluation demonstrates that when dealing with imperfect models, our methods exhibit increased performance and stability, both in terms of model accuracy and in its use within a planning algorithm.
A Study on Dark Channel Prior based Image Enhancement Techniques

The existence of haze and cloud degrades the quality of the image taken by camera sensors and decreases their clarity. The degradation can majorly be seen using the transmission map, which is one of the crucial parameters of Dehazing using a single image. The estimation of the transmission map is an underlying issue, and lots of different prior are proposed for that. Among them, one of the widely recognized prior is a dark channel prior (DCP). DCP is based on the statistics that a local patch in the sky-free region often has some pixel which has less-intensity value in at least one of the R.G, and B color channel. In this paper, we first discuss the limitations of the DCP and then give a detailed review on how to overcome the limitations of DCP by improving the calculation of global ambient light and transmission map compared to the standard DCP.
Anomaly Detection with Deep Graph Autoencoders on Attributed Networks

Anomaly detection on attributed networks aims to differentiate rare nodes that are significantly different from the majority. It plays an important role in various practical scenarios, such as intrusion detection and fraud detection. However, existing graph-based methods mainly adopt shallow models that cannot capture the highly non-linear interactions between nodes in an attribute network consisting of different information modalities. To tackle the above issues, in this paper, we propose a novel deep model named DeepAE for anomaly detection which (a) can capture the high non-linearity in both topological structure and nodal attributes through graph convolutional autoencoder, (b) fully exploits the intrinsic information of the network with the description of various proximities, (c) and preserve the differences between anomalies and the majority by applying Laplacian sharpening. We perform anomaly detection by measuring the reconstruction errors of nodes. Experimental results on realworld datasets demonstrate that DeepAE outperforms the stateof-art baselines.