Reviewerly/exHarmony huggingface dataset:
Adversarial Attack on Deepfake Detection Using RL Based Texture Patches

The advancements in GANs have made creating deepfake videos a relatively easy task. Considering the threat that deepfake videos pose for manipulating political opinion, recent research has focused on ways to better detect deepfake videos. Even though researchers have had some success in detecting deepfake videos, it has been found that these detection systems can be attacked. The key contributions of this paper are (a) a deepfake dataset created using a commercial website, (b) validation of the efficacy of DeepExplainer and heart rate detection from the face for differentiating real faces from adversarial attacks, and (c) the proposal of an attack on the FaceForensics++ deepfake detection system using a state-of-the-art reinforcement learning-based texture patch attack. To the best of our knowledge, we are the first to successfully attack FaceForensics++ on our commercial deepfake dataset and DeepfakeTIMIT dataset.
Network embedding from the line graph: Random walkers and boosted classification

In this paper, we propose to embed edges instead of nodes using state-of-the-art neural/factorization methods (DeepWalk, node2vec, NetMF). These methods produce latent representations based on co-ocurrence statistics by simulating fixed-length random walks and then taking bags-of-vectors as the input to the Skip Gram Learning with Negative Sampling (SGNS). We commence by expressing commute times embedding as matrix factorization, and thus relating this embedding to those of DeepWalk and node2vec. Recent results showing formal links between all these methods via the spectrum of graph Laplacian, are then extended to understand the results obtained by SGNS when we embed edges instead of nodes. Since embedding edges is equivalent to embedding nodes in the line graph, we proceed to combine both existing formal characterizations of the line graphs and empirical evidence in order to explain why this embedding dramatically outperforms its nodal counterpart in multi-label classification tasks.
Co-Registration of Eye Movements and Fixation—Related Potentials in Natural Reading: Practical Issues of Experimental Design and Data Analysis

A growing number of studies are using co-registration of eye movement (EM) and fixation-related potential (FRP) measures to investigate reading. However, the number of co-registration experiments remains small when compared to the number of studies in the literature conducted with EMs and event-related potentials (ERPs) alone. One reason for this is the complexity of the experimental design and data analyses. The present paper is designed to support researchers who might have expertise in conducting reading experiments with EM or ERP techniques and are wishing to take their first steps towards co-registration research. The objective of this paper is threefold. First, to provide an overview of the issues that such researchers would face. Second, to provide a critical overview of the methodological approaches available to date to deal with these issues. Third, to offer an example pipeline and a full set of scripts for data preprocessing that may be adopted and adapted for one’s own needs. The data preprocessing steps are based on EM data parsing via Data Viewer (SR Research), and the provided scripts are written in Matlab and R. Ultimately, with this paper we hope to encourage other researchers to run co-registration experiments to study reading and human cognition more generally.
Verification of Neural Network Compression of ACAS Xu Lookup Tables with Star Set Reachability

Neural network approximations have become attractive to compress data for automation and autonomy algorithms for use on storage-limited and processing-limited aerospace hard-ware. However, unless these neural network approximations can be exhaustively verified to be safe, they cannot be certified for use on aircraft. This manuscript evaluates the safety of a neural network approximation of the unmanned Airborne Collision Avoidance System (ACAS Xu). First, a set of ACAS Xu closed-loop benchmarks is introduced, based on a well-known open-loop benchmark, that are challenging to analyze for current verification tools due to the complexity and high-dimensional plant dynamics. Additionally, the system of switching and classification-based nature of the ACAS Xu neural network system adds another challenge to existing analysis methods. Experimental evaluation shows selected scenarios where the safety of the ownship aircraft’s neural network action selection is assessed with respect to an intruder aircraft over time in a closed loop control evaluation. Set-based analysis of the closed-loop benchmarks is performed using the Star Set representation using both the NNV tool and the nnenum tool, demonstrating that set-based analysis is becoming increasingly feasible for the verification of this class of systems.
Exploiting statistical and structural features for the detection of Domain Generation Algorithms

Nowadays, malware campaigns have reached a high level of sophistication, thanks to the use of cryptography and covert communication channels over traditional protocols and services. In this regard, a typical approach to evade botnet identification and takedown mechanisms is the use of domain fluxing through the use of Domain Generation Algorithms (DGAs). These algorithms produce an overwhelming amount of domain names that the infected device tries to communicate with to find the Command and Control server, yet only a small fragment of them is actually registered. Due to the high number of domain names, the blacklisting approach is rendered useless. Therefore, the botmaster may pivot the control dynamically and hinder botnet detection mechanisms. To counter this problem, many security mechanisms result in solutions that try to identify domains from a DGA based on the randomness of their name. In this work, we explore hard to detect families of DGAs, as they are constructed to bypass these mechanisms. More precisely, they are based on the use of dictionaries so the domains seem to be user-generated. Therefore, the corresponding generated domains pass many filters that look for, e.g. high entropy strings. To address this challenge, we propose an accurate and efficient probabilistic approach to detect them. We test and validate the proposed solution through extensive experiments with a sound dataset containing all the wordlist-based DGA families that exhibit this behaviour and compare it with other state-of-the-art methods, practically showing the efficacy and prevalence of our proposal.
Multi-level cross-view consistent feature learning for person re-identification

Person re-identification plays an important role in searching for a specific person in a camera network with non-overlapping cameras. The most critical problem for re-identification is feature representation. In this paper, a multi-level cross-view consistent feature learning framework is proposed for person re-identification. First, local deep, LOMO and SIFT features are extracted to form multi-level features. Specifically, local features from the lower and higher layers of a convolutional neural network (CNN) are extracted, these features complement each other as they extract apparent and semantic properties. Second, an ID-based cross-view multi-level dictionary learning (IDB-CMDL) is carried out to obtain sparse and discriminant feature representation. Third, a cross-view consistent word learning is performed to get the cross-view consistent BoVW histograms from sparse feature representation. Finally, a multi-level metric learning fuses multiple BoVW histograms, and learns the sample distance in the subspace for ranking. Experiments on the public CUHK03, Market1501, and DukeMTMC-ReID datasets show results that are superior to many state-of-the-art methods for person re-identification.
Reinforcement Learning for Logic Recipe Generation: Bridging Gaps From Images to Plans

It is a challenging task to produce recipes from images, due to the difficulty in bridging the gap from intuitive, static images to sequential, dynamic recipes. In this paper, we propose a novel recipe generation system for producing effective recipes from images. As medium steps, ingredient generation is introduced to guide recipe generation in our system. With potential information in ingredient lists, ingredient selection and ingredient sequence, the system is taught to generate effective recipes. For information representation, a hierarchical attention mechanism is designed to extract effective features for ingredient production and recipe generation. In order to guarantee the comprehensiveness and logic in recipes, a specific and explicit criterion around ingredients is designed under the framework of reinforcement learning. In ingredient generation, the system is required to generate ingredients with correct sequence in cooking procedures. And in recipe generation, ingredients in recipes are required to be consistent with produced ingredients. In experiments, the proposed method is compared with state-of-the-art methods to evaluate the feasibility. The results indicate that the proposed system achieves a better performance than other methods on both aspects of producing proper ingredients and effective recipes.
Arabic L2 readability assessment: Dimensionality reduction study

Readability is a measure that associates a written text to a reader’s skill or grade level. Readability assessment is very important in the field of second or foreign language (L2) learning. The complexity of reading in some languages, such as Arabic, presents a great challenge to overcome and increases the need for such a measure before giving texts to a learner. In order to develop powerful tools that automate this choice, we must first choose the optimal feature set to be used in the development of such a tool. In this paper, we present an approach to automatically measure the readability of Arabic as a foreign language through a series of experiments. We begin by using a wide range of features plausibly relevant to readability, as found in the literature, and reduce them in subsequent experiments by eliminating features that appear to have little significance in readability prediction. Our objective is to keep the smallest set of features that gives good readability prediction accuracy based on three different corpora for learners of Arabic as a foreign language, annotated with five difficulty levels. Our best L2 readability accuracy result is 86.15%.
Cross-Model Hashing Retrieval Based on Deep Residual Network

In the era of big data rich in We Media, the single mode retrieval system has been unable to meet people's demand for information retrieval.This paper proposes a new solution to the problem of feature extraction and unified mapping of different modes: A Cross-Modal Hashing retrieval algorithm based on Deep Residual Network (CMHR-DRN).The model construction is divided into two stages: The first stage is the feature extraction of different modal data, including the use of Deep Residual Network (DRN) to extract the image features, using the method of combining TF-IDF with the full connection network to extract the text features, and the obtained image and text features used as the input of the second stage.In the second stage, the image and text features are mapped into Hash functions by supervised learning, and the image and text features are mapped to the common binary Hamming space.In the process of mapping, the distance measurement of the original distance measurement and the common feature space are kept unchanged as far as possible to improve the accuracy of Cross-Modal Retrieval.In training the model, adaptive moment estimation (Adam) is used to calculate the adaptive learning rate of each parameter, and the stochastic gradient descent (SGD) is calculated to obtain the minimum loss function.The whole training process is completed on Caffe deep learning framework.Experiments show that the proposed algorithm CMHR-DRN based on Deep Residual Network has better retrieval performance and stronger advantages than other Cross-Modal algorithms CMFH, CMDN and CMSSH.
Land Registry Management using Blockachain

Land Registration is a use case which involves lot of middlemen and central authorities in the process which then puts trust in the system. Keeping traces of who owns which part of land is challenging when there are hundreds or thousands of land records to maintain. Using Blockchain will remove the middlemen in the system and also will reduce corruption and increase speed of the process. Land Registration is a simple decentralized application which is build using the Ethereum Blockchain principals. We can use this registration procedure as an substitute to bypass the existing system flaws. Here the user who owns the land registers his land details and also enters market value of the land by providing all the necessary proofs. A government authority who traditionally looks into land registry is assigned as a super admin can do the registration process. Lands coming under a particular village can be registered to the system only through the super admin who is assigned to that village. The smart contract used here is written in such a way that the owner has to transfer his property completely to the buyer and no transaction of the land can be partial. Even we allow a government authority is involved in registration process, the entire process is transparent and the transaction happens only between the two clients.
A Binary Linear Programming-Based K-Means Algorithm For Clustering with Must-Link and Cannot-Link Constraints

Clustering is probably the most extensively studied problem in unsupervised learning. Traditional clustering algorithms assign objects to clusters exclusively based on features of the objects. Constrained clustering is a generalization of traditional clustering where additional information about a dataset is given in the form of constraints. It has been shown that the clustering accuracy can be improved substantially by accounting for these constraints. We consider the constrained clustering problem where additional information is given in the form of must-link and cannot-link constraints for some pairs of objects. Various algorithms have been developed for this specific clustering problem. We propose a binary linear programming-based k-means approach that can consider must-link and cannot-link constraints. In a computational experiment, we compare the proposed algorithm to the DILS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CC</sub> algorithm, which represents the state-of-the-art. Our results on 75 problem instances indicate that the proposed algorithm delivers better clusterings than the DILS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CC</sub> algorithm in much shorter running time.
RETRACTED: Mobile sensor network and wearable devices application in athlete muscle and physical fitness image monitoring

Mobile sensors have become a connection with more of the new devices and the fast pace. A new group of these devices is a wearable device, wireless sensor networks, and integrating them into sensors and wearable devices is the concept of bringing a new experience to daily living activities. Potential physiological characteristics and training that may affect the strength will contain considerations for improving the expression of maximal expression of Power and limited time. Various factors and strengths related to profit to achieve exercise capacity and great strength. Strong muscle strength contributes to the overall performance improvement of the relevant players in the time characteristics. In the proposed method using K-Nearest Neighbor (KNN)algorithm are using Sports training is physical strength, skill, tactics, psychology, includes the development of intelligence and ability. Physical training is an important part of the training of competitive sports. Physical conditions may also perform directly affects the players. Depletion might not be necessary for the maximum strength benefit of it is not necessary to increase the maximum muscle strength. In practice, programming can combine heavy chains and light loads with improving board strength and power consumption characteristics. Compared to multiple sets, a single set produces excellent training allowances, but the athlete's training status and response relationships must be considered.
PeR-ViS: Person Retrieval in Video Surveillance using Semantic Description

A person is usually characterized by descriptors like age, gender, height, cloth type, pattern, color, etc. Such descriptors are known as attributes and/or soft-biometrics. They link the semantic gap between a person's description and retrieval in video surveillance. Retrieving a specific person with the query of semantic description has an important application in video surveillance. Using computer vision to fully automate the person retrieval task has been gathering interest within the research community. However, the Current, trend mainly focuses on retrieving persons with image-based queries, which have major limitations for practical usage. Instead of using an image query, in this paper, we study the problem of person retrieval in video surveillance with a semantic description. To solve this problem, we develop a deep learning-based cascade filtering approach (PeR-ViS), which uses Mask R-CNN [14] (person detection and instance segmentation) and DenseNet-161 [16] (soft-biometric classification). On the standard person retrieval dataset of SoftBioSearch [6], we achieve 0.566 Average IoU and 0.792 %w IoU > 0.4, surpassing the current state-of-the-art by a large margin. We hope our simple, reproducible, and effective approach will help ease future research in the domain of person retrieval in video surveillance. The source code will be released after the paper is accepted for publication with base-line and pretrained weights. The source code and pre-trained weights available at https://parshwa1999.github.io/PeR-ViS/.
End-to-End Anti-Forensics Network of Single and Double JPEG Detection

JPEG compression is one of the major image compression methods and is widely used on the Internet. In addition, identifying traces of JPEG compression and double JPEG compression (DJPEG) is crucial in the image forensics field. Therefore, JPEG compression detection and DJPEG compression detection are two of the popular image authentication methods. Many feature-based JPEG detection methods have been proposed for that purpose, and there have been outstanding improvements in DJPEG detection with the development of deep learning. A number of anti-forensics of JPEG detection that counter feature-based detectors have been proposed but only a few techniques that counter DJPEG have been researched. This paper explores whether JPEG reconstruction methods, including restoration and anti-forensics of JPEG detection, can deceive JPEG and DJPEG detectors. We demonstrate that existing anti-forensics of JPEG detection can deceive both JPEG and DJPEG detectors well but perform poorly in non-aligned cases and degrade the image quality. We propose a convolutional neural network (CNN) based anti-forensics method to improve the performance of anti-forensics so that they can proficiently deceive JPEG and DJPEG detectors with higher image quality. Moreover, we explore the generalization algorithm to handle the real scenario.
Mechanistic understanding of entanglement and heralding in cascade emitters

Semiconductor quantum light sources are favorable for a wide range of quantum photonic tasks, particularly quantum computing and quantum information processing. Here, we theoretically investigate the properties of quantum emitters as a source of entangled photons with practical quantum properties including heralding of on-demand single photons. Through the theoretical analysis, we characterize the properties of a cascade (biexciton) emitter, including (1) studies of single-photon purity, (2) investigating the first- and second-order correlation functions, and (3) determining the Schmidt number of the entangled photons. The analytical expression derived for the Schmidt number of the cascade emitters reveals a strong dependence on the ratio of decay rates of the first and second photons. Looking into the joint spectral density of the generated biphotons, we show how the purity and degree of entanglement are connected to the production of heralded single photons. Our model is further developed to include polarization effects, fine structure splitting, and the emission delay between the exciton and biexciton emission. The extended model offers more details about the underlying mechanism of entangled photon production, and it provides additional degrees of freedom for manipulating the system and characterizing purity of the output photon. The theoretical investigations and the analysis provide a cornerstone for the experimental design and engineering of on-demand single photons.
TemporalRI: A Subgraph Isomorphism Algorithm for Temporal Networks

Temporal networks are graphs where each edge is associated with a timestamp or an interval indicating when an interaction between two nodes happens. The Temporal Subgraph Isomorphism (TSI) problem aims at identifying all the subgraphs of a temporal network (called target) matching a smaller temporal network (called query), such that matched target edges satisfy the same chronological order of query edges. Few existing algorithms deal with the TSI problem and most of them can be applied only to small or specific queries. In this paper we propose TemporalRI, a subgraph isomorphism algorithm for temporal networks, inspired by RI algorithm. We show that our algorithm can handle queries of any size and any topology. Experiments on real networks show that TemporalRI is very efficient, especially for large queries and targets.
A Survey on Encrypted Traffic Identification

In recent years, due to the need of privacy protection, the proportion of encrypted traffic in the network continues to growth. Traditional network traffic identification methods for non-encrypted traffic are difficult to use to encrypted traffic, therefore, analysis methods suitable for encrypted traffic have become a new research hotspot in network security and management. This article divides the research on encrypted traffic identification in recent years according to the methods they used, and makes a brief summary.
Influence of Outliers on Estimation Accuracy of Software Development Effort

When applying estimation methods, the issue of outliers is inevitable. The extent of their influence has not been clarified, though several studies have evaluated outlier elimination methods. It is unclear whether we should always be sensitive to outliers, whether outliers should always be removed before estimation, and what amount of precaution is required for collecting project data. Therefore, the goal of this study is to illustrate a guideline that suggests how sensitively we should handle outliers. In the analysis, we experimentally add outliers to three datasets, to analyze their influence. We modified the percentage of outliers, their extent (e.g., we varied the actual effort from 100 to 200 person-hours when the extent was 100%), the variables including outliers (e.g., adding outliers to function points or effort), and the locations of outliers in a dataset. Next, the effort was estimated using these datasets. We used multiple linear regression analysis and analogy based estimation to estimate the development effort. The experimental results indicate that the influence of outliers on the estimation accuracy is non-trivial when the extent or percentage of outliers is considerable (i.e., 100% and 20%, respectively). In contrast, their influence is negligible when the extent and percentage are small (i.e., 50% and 10%, respectively). Moreover, in some cases, the linear regression analysis was less affected by outliers than analogy based estimation.