Reviewerly/exHarmony huggingface dataset:
A Multispectral Image Compression Algorithm for Small Satellites Based on Wavelet Subband Coding

This article proposes a lossy compression algorithm and scalable multispectral image coding—including blue, green, red, and near-infrared wavelengths—aimed at increasing image quality based on the amount of data received. The algorithm is based on wavelet subband coding and quantization, predictive multispectral image coding at different wavelengths, and the Huffman coding. The methodology was selected due to small satellites' low data rate and a brief line of sight to earth stations. The test image database was made from the PeruSat-1 and LANDSAT 8 satellites in order to have different spatial resolutions. The proposed method was compared with the SPIHT, EZW, and STW techniques and subsequently submitted to a peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation; it showed better efficiency and reached compression ratios of 20, with a PSNR of 30 and an SSIM of approximately 0.8, depending on the multispectral image wavelength.
Virtual University Education is an Environment without Losing its Essence in the Teaching and Learning Spaces

The objective of this research is to know the impact of the use of virtual classrooms in university higher education. A descriptive and quantitative research was carried out in the engineering programs of the National University of the Altiplano. A survey was conducted of a random sample of 3,581 students. The results show that the students of the National University of the Altiplano do not use the virtual classrooms in their entirety. It is concluded that virtual classrooms are not used by students, since they have not been implemented according to the reality of the students. However, if use were made of these technologies, students could increase their academic performance taking advantage of the elements and resources offered by this technology to increase your academic performance.
Optimal feature selection-based biometric key management for identity management system: Emotion oriented facial biometric system

Identity management systems with biometric key binding make digital transactions secure and reliable. A novel methodology is proposed to develop an intelligent key management system using facial emotions. Key binding with facial emotions makes use of an intrinsic user specific trait facilitating a more natural computer to human interaction. The proposed system utilizes metaheuristic swarm intelligence based optimization techniques to extract optimal features. The work demonstrates key binding by encrypting an image with a secret key bound to optimal features extracted from facial emotions. Efficiency and correctness of proposed key management is validated by successful decryption at receiving end with any one of the enrolled emotions given as input. Deer Hunting Optimization Algorithm and Chicken Swarm Optimization are merged to select optimal features from facial emotions. The derived algorithm is called Fitness Sorted Deer Hunting Optimization Algorithm with Rooster Update. Seven facial emotions — anger, disgust, fear, happiness, sadness, surprise and neutral are used to extract optimal features from Japanese Female Facial Expressions and Yale Facial datasets to train the neural network. Proposed work achieved better performance results over state-of-art optimization algorithms such as whale optimization algorithm, grey wolf optimization, chicken swarm optimization and deer hunting optimization algorithm. Accuracy of proposed model is 2.2% better than deer hunting optimization algorithm and 12.3% better than chicken swarm optimization for a key length 80.
Interactive Augmented Reality For The Depth Of An Object Using The Model-Based Occlusion Method

The general concept in marker-based Augmented Reality is to add virtual objects in the real world using markers as object tracking. In its development AR devices can detect 3D real objects as object tracking (3D Object Tracking) so as to allow interaction between virtual objects and real objects. However, the application of AR for general devices such as Android smartphones that do not have depth sensors, virtual objects are added without having depth information from the real world so that virtual content is always displayed in front of or on top of real objects and causes Occlusion problems. Occlusion refers to the problem when real objects that are closer to the user are covered by more distant virtual objects. This research formulates the handling of the Occlusion problem using the Model-Based Occlusion method in which the geometry information of the model from real objects must be known and registered in advance to the system. To maintain the suitability of the model's geometry information with its real object, Tracking is needed. In this case using 3D Object Tracking by utilizing real objects that have been registered in the ModelBased Occlusion. The results of this study are the virtual objects that appear can occupy the correct position in the Augmented Reality application. In this case using 3D Object Tracking by utilizing real objects that have been registered in the ModelBased Occlusion. The results of this study are the virtual objects that appear can occupy the correct position in the Augmented Reality application. In this case using 3D Object Tracking by utilizing real objects that have been registered in the ModelBased Occlusion. The results of this study are the virtual objects that appear can occupy the correct position in the Augmented Reality application.
CNN Hyperparameter Optimization Based on CNN Visualization and Perception Hash Algorithm

In this paper, the network structure and the optimal hyperparameter selection which affect the performance of the model are obtained through the analysis of the convolutional neural network model with mathematical interpretation and visualization. In the study, we used visual methods such as deconvolution and Guided Grad-CAM to display the network structure, parameter changes, and the learning process of the model convolutional layer. Simultaneously, we developed CNN hyperparameters optimization strategy based on the perceptual hash algorithm according to its training characteristics. This method significantly improves the accuracy of image classification of the model and the generalization ability of the model and also provides certain theoretical support for the optimization and understanding of deep learning models in practical application. In addition, the hyperparameter optimization method based on the deep learning model feature map reconstruction visualization proposed in this paper also provides a good idea for the formulation of model training strategies.
A Hybrid Compressed Data Structure Supporting Rank and Select on Bit Sequences

We introduce a practical data structure for supporting the fundamental operations rank, select, and member on integer sets (and their corresponding characteristic bit vector). Our data structure uses a hybrid compression approach to achieve compressed space, while still supporting the operations efficiently. Our aim is to take advantage of the long runs of successive elements that arise in several practical scenarios. Our experimental results indicate that our approach is highly competitive in these cases with the most efficient state-of-the-art data structures.
Display Name-Based Anchor User Identification across Chinese Social Networks

Anchor user identification across social networks is a classification task which determines whether a pair of accounts from different social networks belong to the same user. It is a fundamental research of information dissemination across social networks. Based on the observation that users prefer to use similar or identical display names in different social network, some researchers utilized the similarity between display names to build models. However, due to Chinese social network setting and pronunciation and font characteristics of Chinese display names, these methods do not perform well in Chinese social network datasets. To address this problem, we analyze the display name pairs of Chinese anchor users which are obtained by a crawler build in this paper. Then we define 4 special features to extract the pronunciation and font similarities. Finally, we use Gradient Boosting to establish the identification model. The experiments based on the ground-truth datasets we obtained show that these features can improve the performance of display name-based anchor user identification between Chinese social networks.
SARL: OO Framework Specification for Static Analysis

Semantic static analysis allows sound verification of program properties, that is, to prove that a given property holds for all possible executions. However, modern object-oriented applications make heavy use of third-party frameworks. These provide various functionalities (like libraries), as well as an extension of the execution model of the program. Applying standard models to statically analyze software relying on such frameworks could be potentially unsound and imprecise. In this paper we introduce SARL, a domain-specific language which allows one to specify the runtime behaviors of frameworks of object-oriented programs. Such specifications can be then applied to automatically generate annotations on program components of the application to model the framework runtime environment. In addition, SARL specifications document which aspects of a framework are supported by the static analyzer and how. We adopted SARL to model WindowsForms and ASP.NET, two of the most popular .NET frameworks in an existing industrial static analyzer (Julia). We then analyzed the three most popular GitHub repositories using these frameworks, comparing the results with and without SARL. Our experimental results show that the application of SARL sensibly improved the precision and soundness of the analysis without affecting its runtime performances.
A Brief History of Piano Action Mechanisms

The action mechanism of keyboard musical instruments with strings, such as pianos, transforms the motion of a depressed key into hammer swing or jack lift, which generates sound by striking the string of the instrument.The mechanical design of the key action influences many characteristics of the musical instrument, such as keyboard responsiveness, heaviness, or lightness, which are critical playability parameters that can "make or break" an instrument for a pianist.Furthermore, the color of the sound, as well as its volume, given by the shape and amplitude of the sound wave respectively, are both influenced by the key action.The importance of these mechanisms is highlighted by centuries of studies and efforts to improve them, from the simple rigid lever mechanism of 14 th -century clavichords to the modern key action that can be found in concert grand pianos, with dozens of bodies and compliant elements.This paper presents a brief history of this evolution with a focus of mechanism design and development, with an analysis of the most important designs from a technical perspective.Four milestones can be identified in the history of the key action mechanism: the clavichord, the harpsichord, the fortepiano, and the modern piano (including both the grand and upright pianos).In addition, modern electro-mechanical, analogue, and digital instruments are briefly introduced to identify the current trends and discuss possible future developments of key actions.
Color Visual Cryptography Schemes Using Linear Algebraic Techniques over Rings

The research on color Visual Cryptographic Scheme (VCS) is much more difficult than that of the black and white VCS. This is essentially because of the fact that in color VCS, the rule for superimposition of two colors is not that simple as in black and white VCS. It was a long standing open issue whether linear algebraic technique in constructing Black and White visual cryptographic schemes could also be extended for color images. It was thought that such an extension was impossible. However, we resolve this issue by providing color VCS in same color model for the threshold access structures by extending linear algebraic techniques from the binary field $$\mathbb {Z}_2$$ to finite ring $$\mathbb {Z}_c$$ of integers modulo c. We first give a construction method based on linear algebra to share a color image for an (n, n)-threshold access structure. Then we give constructions for (2, n)-threshold access structures and in general (k, n)-threshold access structures. Existing methodology for constructing color VCS in same color model assumes the existence of black and white VCS, whereas our construction is a direct one. Moreover, we give closed form formulas for pixel expansion which is combinatorially a difficult task. Lastly, we give experimental results and propose a method to reduce pixel expansion.
Integration of an online voting solution with the SMESEC security framework

SMESEC is an H2020 project that designed and implemented a lightweight cybersecurity framework for protecting small and medium-sized enterprises against cyber threats. The framework integrates a large variety of contemporary cybersecurity tools but also provides additional features aiming to facilitate novice users to get an overview of existing threats through simple tutorials, extensive explanatory material and a complete lessons-learned section. One of the possible areas in which the project can demonstrate its potential is in the protection of online voting solutions for small companies or governmental institutions. Scytl has developed an online voting solution that has been used in many private and governmental elections. The solution follows the principles of security by design, however infrastructure security must also be considered and ensured. This is not always easy for small companies or institutions, which are primarily interested on self-administrate their systems, but in the same time cannot afford the full spectrum of necessary security tools for tackling major and severe cyber threats. This paper aims to provide a detailed overview of the components and the architecture of a fully-functional and widely used online voting system, some of the imminent threats such a deployment faces and most importantly a description of the integration with the SMESEC framework and how exactly this was beneficial for the specific online voting solution.
Multivariate statistical matching using graphical modeling

The goal of statistical matching, at a macro level, is the estimation of the joint distribution of variables separately observed in independent samples. The lack of joint information on the variables of interest leads to uncertainty about the data generating model. In this paper we propose the use of graphical models to deal with the statistical matching uncertainty for multivariate categorical variables. The use of Bayesian networks in the statistical matching context allows both to introduce extra sample information on the dependence structure between the variables of interest and to use such an information to factorize the joint probability distribution according to the graph decomposition of a multivariate dependence in lower dimension components. This representation of the joint probability distribution, taking advantage of local relationships, allows to simplify both parameters estimation and statistical matching quality evaluation in a multivariate context. A simulation experiment is performed in order to evaluate the performance of the proposed methodology with and without auxiliary information, as well as to compare it with the saturated multinomial model, in terms of uncertainty reduction. Finally, an application to a real case is provided. Results show a considerable improvement in the quality of statistical matching when the dependence structure is taken into account.
POSTER: Blockchain-based Key Management Protocol for Resource-Constrained IoT Devices

To avoid the authentication-related security issues in the smart IoT networks, blockchain technology is deployed to allow smooth and secure key management features and blockchain parameter updates. The proposed system has been designed for light-weight IoT devices with additional security measures provided by deploying difficulty parameters. This paper examines the hash mining procedure along with the application of proposed solution implemented in java programming language. Also the paper examines deploying different hashing algorithms in the proposed solution and compares their processing time. The higher difficulty parameters combined with faster hashing algorithms provides the most suitable solution for IoT devices with less computational power and memory requirements.
MMD Based Discriminative Learning for Face Forgery Detection

Face forensic detection is to distinguish manipulated from pristine face images. The main drawback of existing face forensics detection methods is their limited generalization ability due to differences in domains. Furthermore, artifacts such as imaging variations or face attributes do not persistently exist among all generated results for a single generation method. Therefore, in this paper, we propose a novel framework to address the domain gap induced by multiple deep fake datasets. To this end, the maximum mean discrepancy (MMD) loss is incorporated to align the different feature distributions. The center and triplet losses are added to enhance generalization. This addition ensures that the learned features are shared by multiple domains and provides better generalization abilities to unseen deep fake samples. Evaluations on various deep fake benchmarks (DF-TIMIT, UADFV, Celeb-DF and FaceForensics++) show that the proposed method achieves the best overall performance. An ablation study is performed to investigate the effect of the different components and style transfer losses.
Cost-Efficient Super-Resolution Hardware Using Local Binary Pattern Classification and Linear Mapping for Real-Time 4K Conversion

We propose a new hardware-friendly super-resolution (SR) algorithm using computationally simple feature extraction and regression methods, i.e., local binary pattern (LBP) and linear mapping, respectively. The proposed method pre-trains dedicated linear mapping kernels for different texture types of low-resolution (LR) image patches where the texture type is classified based on LBP features. On inference operation, a high-resolution (HR) image patch is reconstructed by multiplying an LR image patch with a linear mapping kernel, which is inferred by the LBP feature class of the corresponding LR patch. Since, the LBP is a highly efficient feature extraction operator for local texture classification, our method is extremely fast and power-efficient while showing competitive reconstruction quality to the latest machine learning-based SR techniques. We also present a fully pipe-lined hardware architecture and its implementation for real-time operations of the proposed SR method. The proposed SR algorithm has been implemented on a field-programmable-gate-array (FPGA) platform including Xilinx KCU105 that can process 63 frames-per-second (fps) while converting full-high-definition (FHD) images to 4K ultra-high-definition (UHD) images. Extensive experimental results show that the proposed proposed algorithm and its hardware implementation can achieve high reconstruction performance compared to the latest machine-learning-based SR methods while utilizing minimum hardware resources, thereby having remarkably less computational complexity. Sometimes, the latest deep-learning-based SR approaches offer slightly higher reconstruction quality, but they require significantly larger amount of hardware resources than the proposed method.
The Loop of Nonverbal Communication Between Human and Virtual Actor: Mapping Between Spaces

There is a question about the appropriate emotional and expressive language of a virtual actor. In this paper we study facial expressions. We investigate the transformations between the space of Action Units and the standard affective space in the loop of nonverbal communication between a person and a virtual actor using facial expressions []. We are mapping both dimensions into each other using various machine learning algorithms. Action Units space was mapped into emotional space directly using artificial neural networks. Emotional space was mapped into Action Units space with help of dimensionality reduction followed by clusterization of the latter. After the final synthesis, the facial expression of virtual actor can be determined.
Application of Text Proofreading System Based on Artificial Intelligence

This paper expounds the development process and future prospect of intelligent proofreading technology. Based on the model training and the research on the composition of knowledge map, the deep learning model is established to reduce the error rate of intelligent proofreading, and the basic proofreading model of intelligent proofreading system is constructed. By perfecting the proofreading model of specific vocabulary and data docking, Chinese fixed terms, sensitive words, etc., a relatively complete proofreading model is formed. Meanwhile, the intelligent proofreading model is encapsulated to form interface service and independent proofreading service.
SEED: Confidential Big Data Workflow Scheduling with Intel SGX Under Deadline Constraints

Recently, cloud platforms play an essential role in large-scale big data analytics and especially running scientific workflows. In contrast to traditional on-premise computing environments, where the number of resources is bounded, cloud computing can provide practically unlimited resources to a workflow application based on a pay-as-you-go pricing model. One challenge of using cloud computing is the protection of the privacy of the confidential workflow's tasks, whose proprietary algorithm implementations are intellectual properties of the respective stakeholders. Another one is the monetary cost optimization of executing workflows in the cloud while satisfying a user-defined deadline. In this paper, we use the Intel Software Guard eXtensions (SGX) as a Trusted Execution Environment (TEE) to support the confidentiality of individual workflow tasks. Based on this, we propose a deadline-constrained and SGX-aware workflow scheduling algorithm, called SEED (SGX, Efficient, Effective, Deadline Constrained), to address these two challenges. SEED features several heuristics, including exploiting the longest critical paths and reuse of extra times in existing virtual machine instances. Our experiments show that SEED outperforms the representative algorithm, IC-PCP, in most cases in monetary cost while satisfying the given user-defined deadline. To our best knowledge, this is the first workflow scheduling algorithm that considers protecting the confidentiality of workflow tasks in a public cloud computing environment.
A kernel-based weight decorrelation for regularizing CNNs

Recent years has witnessed the success of convolutional neural networks (CNNs) in many machine learning and pattern recognition applications, especially in image recognition. However, due to the increasing model complexity, the parameter redundancy problem arises, and greatly degrades the performance of CNNs. To alleviate this problem, various regularization techniques, such as Dropout, have been proposed and proved their effectiveness. In this paper, we propose a novel adaptive kernel-based weight decorrelation (AKWD) framework, in order to regularize CNNs for better generalization. Different from existing works, the correlation between paring weights is measured by the cosine distance defined in RKHS associated with a specific kernel. The case with the well-known Gaussian kernel is investigated in detail, where the bandwidth parameter is adaptively estimated. By regularizing CNN models of different capacities using AKWD, better performance is achieved on several benchmark databases for both object classification and face verification tasks. In particular, when Dropout or BatchNorm is present, even higher improvements are obtained using the proposed AKWD, that demonstrates a good compatibility of the proposed regularizer with other regularization techniques.