Reviewerly/exHarmony huggingface dataset:
Discovery of Single-Vendor Marketplace Operators in the Tor-Network

In the Tor-network are many single-vendor marketplace web sites with a wide range of offers. Some of these vendor websites could be hosted by the same operators. In this paper, a method is presented to find out similarities between these vendor websites to discover possible operational structures between them. In order to accomplish this, similarity values are determined between the darknet websites by combining various features from the different categories structure, content and metadata. A dataset is determined by a first execution of the method and manual validation. Based on this data set, important features are extracted using decision trees. The features of the category structure HTML-Tag, HTML-Class, HTML-DOM-Tree as well as the metadata features File Content and Links-To have proven to be particularly important and can very effectively highlight similarities between darknet web sites. Supported by the similarity detection method, it was found that only 49% of 258 single-vendor marketplaces were unique, i.e. no similar sites existed. In addition, it was possible to find several duplicates of vendor websites, which made up 20%.
Entropy-Aware Model Initialization for Effective Exploration In Deep Reinforcement Learning

Effective exploration is one of the critical factors affecting performance in deep reinforcement learning. Agents acquire data to learn the optimal policy through exploration, and if it is not guaranteed, the data quality deteriorates, which leads to performance degradation. This study investigates the effect of initial entropy, which significantly influences exploration, especially in the early learning stage. The results of this study on tasks with discrete action space show that 1) low initial entropy increases the probability of learning failure, 2) the distributions of initial entropy for various tasks are biased towards low values that inhibit exploration, and 3) the initial entropy for discrete action space varies with both the initial weight and task, making it hard to control. We then devise a simple yet powerful learning strategy to deal with these limitations, namely, entropy-aware model initialization. The proposed algorithm aims to provide a model with high initial entropy to a deep reinforcement learning algorithm for effective exploration. Our experiments showed that the devised learning strategy significantly reduces learning failures and enhances performance, stability, and learning speed.
Object Detection Algorithm Based on YOLOv3 Model to Detect Occluded Targets

Abstract Target detection has experienced a rapid development stage from the concept to the practical application, the core technology has been broken through, many difficult problems have been solved, and it has been widely used in various fields. However, the existing target detection models still have some technical problems such as incomplete detection and low accuracy. Therefore, this paper proposes a target detection algorithm based on V3 model to detect occluded targets. This paper makes an in-depth investigation and Research on the existing target detection model, analyzes the shortcomings of the current traditional target detection model through the test results, and the advantages of the network detection model based on YOLOv3 in target detection. Aiming at the main problems existing in the current detection technology, this paper puts forward the optimization and improvement scheme. By reconstructing the whole framework of multi-target tracking algorithm, optimizing the feature fusion algorithm, and improving the NMS algorithm, the scheme greatly improves the accuracy of detecting the occluded objects. The simulation results show that the improved scheme can improve the accuracy by 12.3% compared with the traditional scheme. The analysis shows that the target detection model based on YOLOv3 network can effectively improve the detection accuracy of the model.
Succinct Data Structures for Series-Parallel, Block-Cactus and 3-Leaf Power Graphs

We design succinct encodings of series-parallel, block-cactus and 3-leaf power graphs while supporting the basic navigational queries such as degree, adjacency and neighborhood optimally in the RAM model with logarithmic word size. One salient feature of our representation is that it can achieve optimal space even though the exact space lower bound for these graph classes is not known. For these graph classes, we provide succinct data structures with optimal query support for the first time in the literature. For series-parallel multigraphs, our work also extends the works of Uno et al. (Disc. Math. Alg. and Appl., 2013) and Blelloch and Farzan (CPM, 2010) to produce optimal bounds.
Representation Decomposition For Image Manipulation And Beyond

Representation disentanglement aims at learning interpretable features, so that the output can be recovered or manipulated accordingly. While existing works like infoGAN [1] and ACGAN [2] exist, they choose to derive disjoint attribute code for feature disentanglement, which is not applicable for existing/trained generative models. In this paper, we propose a decomposition-GAN (dec-GAN), which is able to achieve the decomposition of an existing latent representation into content and attribute features. Guided by the classifier pre-trained on the attributes of interest, our dec-GAN decomposes the attributes of interest from the latent representation, while data recovery and feature consistency objectives enforce the learning of our proposed method. Our experiments on multiple image datasets confirm the effectiveness and robustness of our dec-GAN over recent representation disentanglement models.
Data Security Protocol with Blind Factor in Cloud Environment

Compared with the traditional system, cloud storage users have no direct control over their data, so users are most concerned about security for their data stored in the cloud. One security requirement is to resolve any threats from semi-trusted key third party managers. The proposed data security for cloud environment with semi-trusted third party (DaSCE) protocol has solved the security threat of key managers to some extent but has not achieved positive results. Based on this, this paper proposes a semi-trusted third-party data security protocol (ADSS), which can effectively remove this security threat by adding time stamp and blind factor to prevent key managers and intermediaries from intercepting and decrypting user data. Moreover, the ADSS protocol is proved to provide indistinguishable security under a chosen ciphertext attack. Finally, the performance evaluation and simulation of the protocol show that the ADSS security is greater than DaSCE, and the amount of time needed is lower than DaSCE.
Quality Assurance Guidelines for Successful Startups

The importance of software quality assurance is often overlooked in startups, leading to consequences that deem the product and subsequently the startup to fail in the long run. This paper looks into the current software quality related scenarios present in startups and tries to pinpoint issues which eventually cause their downfall. By acquiring their insight, we hypothesized an adoptable guideline to mitigate the issues faced in the startups and implement a quality framework for any software-based startup organization.
Coercive Behaviour Catching : A Real Time Analysis Approach

Social media platforms such as Instagram, Face-book, Twitter etc are appearing as powerful channels of communication presently. They have become a chunk of our daily living. Individuals can share their feelings and opinions over these social media platforms. Although, this freedom of expression comes with a costly price. Oftentimes individuals express their aggression on social network which successively hurts the feelings or belief of the intended victims. Victims suffer from societal isolation, depression, degrading self-confidence, and suicidal thoughts. Thus, the prevention of Coercive behavior is very essential and requires periodic detection.Cyberbullying or Coercive behavior is a crime in which a predator targets a person with online intimidation, hate, and trolls. Numerous social media bullying classification techniques have been implemented, but many of them did not perform real-time analysis. The goal of this proposed system is to classify tweets on a real-time basis. So consequences of Coercive behavior can be minimized or avoided in the early stages.
Deep Learning for Video Face Recognition

This chapter is concerned with face recognition based on videos or, more generally, sets of images, using deep learning techniques. We first briefly review some naive yet commonly used strategies pertaining to using frame-level features extracted by deep convolutional neural networks (CNNs) for video-level face recognition. Representative strategies include naive feature pooling and pairwise feature distance computation. Then, we present a method named neural aggregation networkNeural aggregation network (NAN), which is a deep learning framework tailored for video-based representation and recognition. NAN can automatically learn the quality of faces in a video/image set and aggregate the frame-level deep features accordingly, yielding more discriminative video-level features. We conduct experimental evaluation on three video face recognitionVideo face recognition datasets. The results indicate that while previous deep learning-based methods with naive pooling or pairwise distances have obtained substantial improvements over traditional methods, the NAN method further outperforms them by an appreciable margin.
A Physical Model of the Trombone Using Dynamic Grids for Finite-Difference Schemes

In this paper, a complete simulation of a trombone using finite-difference time-domain (FDTD) methods is proposed. In particular, we propose the use of a novel method to dynamically vary the number of grid points associated to the FDTD method, to simulate the fact that the physical dimension of the trombone's resonator dynamically varies over time. We describe the different elements of the model and present the results of a real-time simulation.
ARAnimals: Mobile Application with Augmented Reality for the Learning of Vertebrate Animals

This work shows the results of an application of augmented reality for learning about vertebrate animals in preschool children. A mobile application with augmented reality, called ARAnimals, was implemented following the agile Scrum methodology. The research design was quasi-experimental, where the sample consisted of two classrooms of 5-year-old students from a private school in the city of Lima. The experimental group improved by 33.04% compared to the control group, which only improved by 14.56%. Furthermore, the difference in the average time it took to evaluate the subject in the experimental group and in the control group was significant. The time for the experimental group was 13.3 min., and the time for the control group was 16.7 min. It is concluded that the use of a mobile application with augmented reality satisfactorily influences the learning of vertebrate animals, specifically in four aspects that are included in the dimensions to which they correspond.
Automated parasitaemia quantification in mice infected with Trypanosoma cruzi

This paper presents a custom solution implementation to automate the calculation of the Trypanosoma cruzi parasites present in a blood smear. The process to determine parasitaemia, or parasites presence in the fresh drop sample, is performed by a software application developed with Python and OpenCV libraries having a Raspberry Pi board for execution environment with a digital camera connected for video capture from an optical microscope. The software implements image processing techniques and the algorithm was approved; allowing a considerable saving of time in the evaluated blood samples processing; increasing the accuracy of the parasite count. Additionally, a cloud multimedia database is integrated with the solution to store images, videos and data associated with parasitaemia results per each sample.
Impact of virtual education in times of pandemic at the University of North Lima

This research work is based on the study of the analysis of the impact of the use of technological tools in university higher education, which we will show in detail based on our university environment that will be investigated in Lima, Peru. In it we will explain the methodology that we will use, in addition to the current problems that we have because of the COVID-19 (global pandemic) that influences in great amount the education that the universities offer at national and global level, that went from being in person to virtual in our country. This implied taking measures in which they are adaptable for both students and teachers, who in both cases do not have, for the most part, knowledge about the use of virtual tools towards learning and teaching necessary to have an optimal achievement. The result of the research work has been obtained based on the analysis of the Malteza cross, a tool that allowed us to analyze the information obtained from CATDWE, with its 4 quadrants North-East, North-West, South-East, South-West, from which we obtained that students and teachers adapted to virtual teaching with the technological resources they had at the time.
Detección de Paquetes en Movimiento sobre una Cinta Transportadora Usando Visión por Computadora [Not available in English]

No English translation of this document was provided by the author or conference organizers.
Support region of μ-law logarithmic quantizers for Laplacian source applied in neural networks

The main aim of the paper is to provide effective and accurate solutions for the calculation of the support region of the μ-law logarithmic companding quantizers. A new solution for the starting point of iterative methods will be proposed, that provides very accurate value of the support region (being the main parameter needed for the design of the quantizer) only after one iteration of the iterative method. Based on this new starting point, an accurate closed-form approximate expression for the calculation of the support region will be derived, as one of the main contributions of the paper. To significantly simplify implementation of the μ-law companding quantizer, piecewise linearization is performed. A new linearization method is presented, based on the optimization of the last segments. Derivation of an accurate closed-form formula for the support region of the linearized quantizer is done, as an important contribution. The obtained linearized μ-law companding quantizer is very simple to design (due to closed-form formulas) and to implement (due to linearization), providing at the same time very high performance (due to optimization of the last segments). Due to these and other advantages (robustness, adjustability to the statistical distribution of the input signal), the proposed quantizer can be used in many topical applications, such as in receivers of 5G wireless systems or in neural networks for quantization of weights and activations. The paper provides an application of the designed quantizers for quantization of weights of a neural network, showing significant decreasing of the bit-rate compared to the standard full-precision representation (from 32 bits to just 5 bits), with the same prediction accuracy of the network. • A very accurate closed-form expression for the support region of the µ-law logarithmic quantizer is provided. • Implementation complexity of the µ-law logarithmic quantizer is drastically reduced by optimized linearization. • Weights of neural networks can be drastically compressed if they are quantized with the proposed quantizer.
Enhancing PRESENT-80 and Substitution-Permutation Network Cipher Security with Dynamic "Keyed" Permutation Networks

This work presents a countermeasure for thwarting side-channel assisted differential plaintext attacks on hard-ware based cryptographic algorithms employing a substitution-permutation network structure. The countermeasure we present, dynamic keyed permutation networks, is integrated directly into a PRESENT-80 OpenCore and using the Cadence GSCLIB 45 nanometer standard cell library we perform attacks on both a modified and unmodified PRESENT-80. Ultimately, we will show that not only does this method successfully thwart differential plaintext attacks but it is also lightweight and increases resilience of the algorithm. Additionally, we demonstrate how PRESENT-80 can be attacked without the need of side-channel techniques and how the presented countermeasure further increases resilience and prevents such an attack.
Study on Various Collabrative Filtering Techniques to Recommend Movies

Suggestion framework is assuming an inexorably significant part for web access; data over-burden is one of the most vital functions that clients experience on the Internet. Proposal framework can be characterized into the content-based methodology, cooperative sifting approach, half and half methodology. Content-based suggestion frameworks will suggest things dependent on the depiction of things and profiles of the client. A community-oriented separating suggestion framework will suggest things dependent on comparability between clients who have appraised similar things previously. Hybrid is a mix of substance based and communicant filtering approaches. Synergistic filtering is one of the topmost favored methods while executing proposal frameworks. In the movie proposal framework typical situation which we can see comprises a set of clients and a set of motion pictures. Rule mining strategies are broadly utilized in finding concealed connections, the connection between a set of things in exchange. In this audit, we have learned about Suggestion frameworks, Rule mining, Synergistic filtering likewise theories types, preferences, disservices, challenges, issues, and so on.
LMI Methods for Extended ℋ<sub>∞</sub> Filters for Landmark-based Mobile Robot Localization

Localization is still one of the most fundamental tasks for autonomous navigation of mobile robots. However, the existing methods lack robustness when dealing with uncertainties without assuming some characteristics about noise inputs and the nonlinearity of the measurement models. In this work, a theoretical basis for designing two separate extended robust filters based on linear matrix inequalities is proposed to solve the localization problem. The first approach is based on the design of an ℋ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> observer-based filter through a two-step prediction correction structure. In this way, a convex optimization problem needs to be solved at each time step to determine the observer-gain that corrects the predicted pose of a differential wheeled robot. The second approach considers the advantages of a full-order filter which guarantees a better performance under the ℋ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> robust requirements. Besides, satisfactory results that validate theoretical remarks were performed in real and virtual scenarios through simulation frameworks.
Privacy-preserving Multimedia Data Analysis

Journal Article Privacy-preserving Multimedia Data Analysis Get access Xiaofeng Zhu, Xiaofeng Zhu Guangxi Normal University, China Corresponding author: seanzhuxf@gmail.com Search for other works by this author on: Oxford Academic Google Scholar Kim Han Thung, Kim Han Thung University of North Carolina at Chapel Hill, NC, USA Search for other works by this author on: Oxford Academic Google Scholar Minjeong Kim Minjeong Kim University of North Carolina at Greensboro, USA Search for other works by this author on: Oxford Academic Google Scholar The Computer Journal, Volume 64, Issue 7, July 2021, Pages 991–992, https://doi.org/10.1093/comjnl/bxab095 Published: 24 August 2021 Article history Received: 24 April 2021 Revision received: 02 June 2021 Revision requested: 21 June 2021 Published: 24 August 2021
Adversarial Attack on Fake-Faces Detectors Under White and Black Box Scenarios

Generative Adversarial Network (GAN) models have been widely used in various fields. More recently, styleGAN and styleGAN2 have been developed to synthesize faces that are indistinguishable to the human eyes, which could pose a threat to public security. But latest work has shown that it is possible to identify fakes using powerful CNN networks as classifiers. However, the reliability of these techniques is unknown. Therefore, in this paper we focus on the generation of content-preserving images from fake faces to spoof classifiers. Two GAN-based frameworks are proposed to achieve the goal in the white-box and black-box. For the white-box, a network without up/down sampling is proposed to generate face images to confuse the classifier. In the black-box scenario (where the classifier is unknown), real data is introduced as a guidance for GAN structure to make it adversarial, and a Real Extractor as an auxiliary network to constrain the feature distance between the generated images and the real data to enhance the adversarial capability. Experimental results show that the proposed method effectively reduces the detection accuracy of forensic models with good transferability.
Parallelization of Local Diagonal Extrema Pattern Using a Graphical Processing Unit and Its Optimization

The incorporation of medical imaging devices in diagnosis has resulted in huge collection of medical images in hospitals and health centres. A search for a similar image from this image collection corresponding to a new medical image is a much needed help for junior doctors or students. This task involves describing each image in the image collection and also new image. After this, a similarity measure is applied between the image in the image collection and new image. Texture features have been found to be efficient in describing medical images owing to their high discerning capability. Various texture features have been introduced by researchers. Local Diagonal Extrema Pattern (LDEP) is a texture feature that uses only local diagonal neighbours, and hence the dimensionality of resulting feature vector is reduced. In this chapter we discuss parallel LDEP extractor on a GPU using CUDA. A constant kernel execution time for medical images of various sizes has been obtained on a GeForce GTX 1050 GPU.
U-Net for Multi-Organ Segmentation of SPECT Projection Data

In this work we investigate the usage of deep learning techniques on SPECT data solving a multi-organ segmentation problem. We extract projections from 21 Lu-177 MELP SPECT scans and obtain the corresponding ground truth labels from the accompanied CT scans by forward-projection of 3D CT organ segmentations. We train a U-Net to predict the area of the kidney, spleen, liver, and background seen in the projection data, using a weighted dice loss between prediction and target labels to account for class imbalance. With our method we achieved a mean dice coefficient of 72 % on the test set, encouraging us to perform further experiments using the U-Net.