Reviewerly/exHarmony huggingface dataset:
State of the Art and Current Perception of Augmented Reality in Honduras

The research was conducted on Augmented Reality (AR), its concept, its precedents, the fields of application in which it is presented, and the advantages it has at the time this technology is implemented. It also analyzes the implementation of AR in the world in various sectors such as education, medicine, and industry. The applications of augmented reality in Honduras were identified. And the factors why Honduras has not implemented AR are analyzed. AR is a human-computer interface design strategy aimed at enhancing a user's visual perception by superimposing computer graphics on the user's view. AR has a series of devices, and software, among other elements that operate as development tools making the experience with this reality more attractive. In the fields of application four areas stand out: Education, Textile, Marketing, and Manufacturing. By evaluating the sectors and fields where AR is applied worldwide, a comparison and analysis are made to define which would be the best option to develop this technology in the best way in Honduras.
Volumetric Segmentation of Brain Tumors Using Neural Networks

Although medical images can provide exceptional anatomical views, distinguishing the pixels of lesions from those of healthy tissue remains one of the most challenging tasks in medical image analysis. This paper focuses on the task of automatic brain tumors segmentation from Magnetic Resonance Imaging (MRI) images. The aim of the study is to identify an optimal solution based on volumetric convolutional neural architectures, trained on the Brain Tumor Segmentation (BraTS) 2020 dataset. This work presents some of the most popular Convolutional Neural Networks-like volumetric neural networks (Seg-Net 3D, U-Net 3D, ResUNet) used for automatic segmentation of brain tumors in a comparative manner and how they can be optimized to achieve maximum performance. The best Dice score results are obtained after training the ResUNet type network, which leads to a percentage of 91.4% for the whole tumor, while the U-Net network reaches a maximum value of Dice score of 90.8%. The worst results are obtained by using the SegNet architecture, that leads to a dice score of 90.4%.
Frame Rate Up-Conversion under Stationary Constraints of Spatio-Temporal Local Motion

Aiming at the unreliable and over-smoothing problems of the initial motion vector field in existing smoothing methods, a motion vector field smoothing algorithm is proposed. Motion vector outliers can be found in the initial motion vector field by measuring the angle between each motion vector and the average of its neighboring MVs. Based on the location of the outliers, an outlier map is generated. Through evolutionary rules, some hidden outliers are found, and after many iterations, the distribution of motion vector outliers tends to be stable, thus achieving a good balance between over-smoothing and computational redundancy. Spatial-temporal coherence is exploited to correct motion vector outliers. The algorithm significantly improves the visual quality of generated frames and enhances the ability to detect and correct unreliable motion vectors.
Compression of Activation Signals from Split Deep Neural Network

The use of artificial neural networks for the purpose of image classification, together with the advancement in computational capabilities of edge devices, plays an important role in the new emerging 5G use case scenarios. However, one of the main challenges of applications involving the use of these networks in edge devices is still the limitation of computational resources. An alternative for saving resources and promoting privacy are the split learning (or split inference) techniques, in which a deep neural network is cut into two parts and executed in distinct devices. Most of these techniques rely on sending the activation signals (output of the cut layer) through the communication channel. This work proposes a new compression algorithm for decreasing the bit rate required for the transmission of the activation signals (or simply “scores”). The presented results demonstrate that the transmission rate can be decreased without hurting the neural network accuracy.
Securing Digital Payment by using Blockchain Technology

The aim of the research study for securing digital payment by using blockchain technology, is to understand the rapid growth of technology which affects digital payment’s security. Without proper digital security, there will be a lot of issues when users make digital transactions. Therefore, it will also affect user’s trust while using digital transactions. To overcome these issues that users face in digital transactions, a new method that has been proposed in this research is to establish a two-way trust by storing user information under blockchain technology. Face recognition authentication has been developed to store inside blockchain technology. This research includes the framework, data collection, and testing of systems. Data collection was done by using different types of method to collect sampling data.
Multibiometric Identification of Computer Network Users in the Distance Learning Process

The article considers the multibiometric identification of a student to improve the quality and minimize errors in the process of his identification using voice and visual biometric features. Sound processing methods, models of neural networks and Gaussian mixtures for identifying a student by voice signs, and methods of visual identification of a person by video stream and photographs were studied. A software system has been developed for recognizing and indexing people on video while identifying a person by voice signs to use in the educational process to account for students attending remote classes. The system provides for the extraction of acoustic characteristics from a recording of a human language and the subsequent assignment of the obtained data to one of the specified classes (speakers). A multilayer neural network (MNN) was used as a classifier. The classifier was trained on a data set of 43832 audio files from 108 speakers. MHC on the test sample showed an accuracy of 91%. At the stage of processing the video stream frames, a face was detected in the frame, and the detected face was recognized. Face recognition in the system was based on the search for the most appropriate template of basic images stored in the database.
A Modified Non-separable Haar Wavelet Transform with Rabin-p Cryptosystem for Colour Image Watermarking

Digital image watermarking is a tried-and-true method of securing image media, but there is a need to constantly perfect the technique to keep up with the technological progress. In this paper, a non-blind image watermarking technique is used to embed a robust and imperceptible watermark, designed with a focus on security and efficiency. Images are transformed from spatial domain to transform domain using modified non-separable Haar wavelet transform and singular value decomposition, and the watermark is encrypted by Arnold’s cat map scrambling and Rabin-p cryptosystem. The modified non-separable Haar wavelet transform is an alternative to the popular and widely used discrete wavelet transform, which produces a similar transform but in a much more efficient way. On the other hand, the watermark is secured by image scrambling and encryption of the scrambling parameters. A highly robust and imperceptible image watermarking technique is made while prioritizing efficiency and security.
MULABS: Multi-Task Learning with Attention-Based Scoring for Click-Through Rate Prediction on Sparse Data in Healthcare Real-World Scenarios

Personalized recommender systems have found wide applications in healthcare since it has the potential to inform individual users about the most needed medical resources. However, most recommender systems levering deep learning techniques demand a rich amount of data to learn patterns. In this paper, we aim to improve the performance of a recommender system when data are sparse and in low volume. In particular, we introduce a multi-task learning approach to alleviate the learning pressure and propose an attention-based scheme to boost the model performance by fully utilizing users’ historical data. In the experiments, we develop a novel dataset from a public dataset to mimic the real-world scenarios in healthcare. The results demonstrate that this multi-task learning with attention-based scoring (MULABS) aided recommender system is more accurate in predicting user preference with an AUC of 0.7541, requiring limited computation and memory resources.
The Quantitative Parameters in Computer-Assisted Approach: Author’s Lexical Choices in the Novels by Martin Amis

The article aims to determine and analyze the quantitative parameters in terms of absolute word frequency, relative word frequency, and rank in novels by M. Amis: “London fields” “Night train” “Other people: a mystery story” and “Success”, which expose lexical choices of the author. Quantitative text analysis as part of computational data processing is supported and accessed through the web browser tool – Voyant, which generates quantitative parameters grounded on the most frequent textual elements (words) in corpora. The most frequent words in general corpora are further analyzed due to their frequencies and rank in each corpus and textual segments. The researched quantitative parameters form the ground for perspective textual semantic analysis.
Path Planning of Dual Ammunition Loading Robot Based on Velocity Repulsion Field

Aiming at the problems of slow charging speed of single ammunition loading robot and complicated path planning of dual robots, a path planning method of dual ammunition loading robots based on velocity repulsion field is proposed. Firstly, a robot collision detection system is established, and the robot is simplified by the form of sphere enclosing, so that the collision detection between robots can be converted into geometric distance detection; secondly, the collision-free motion path of the double-charged robot is planned by using the velocity repulsion field algorithm; finally, the experimental verification is carried out in matlab. The results show that the algorithm can effectively plan the collision-free path of the robot, which satisfies the work requirements and verifies the reliability and effectiveness of the algorithm.
SiamRPN with Multi-Scale Feature Fusion for Visual Object Tracking

Visual object tracking aims to predict the bounding box in the whole sequence by giving an initial position, which has been paid more and more attention in recent years. The newly proposed Siamese trackers show great potential on high accuracy and robustness. In this paper we introduce a visual object tracker based on the Siamese region proposal network (SiamRPN) with multi-scale feature fusion. Specifically, it employs an additional feature fusion operation in the Siamese sub-network for feature extraction. It has achieved higher accuracy than the baseline SiamRPN without fine-tuning the original network and without considerable efficiency loss.
Secure KNN Set Similarity Search in Outsourced Cloud Environments

With the boom in cloud computing, it is a promising choice for data owners to outsource their data to the cloud server, which can large scale data storage, management, and query processing. Nevertheless, the cloud server is untrusted and it may capture or infer the sensitive information, such as medical or financial records, which necessitate them to be encrypted before being outsourced for privacy concerns. In this paper, we propose a secure KNN set similarity search in outsourced cloud environments by Yao's garbled circuits which can preserve the data privacy for both data owner and the user. To support this framework, we design a novel unified structure, called secure R-tree circuit index, and propose a scheme to achieve completely secret grouping in garbled circuits. Based on the above, we design a series of secure arithmetic sub-protocols to facilitate KNN set similarity query process efficiently. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approaches are empirically evaluated and demonstrated.
Pedestrian Detection based on YOLOv3 Algorithm

In order to improve the accuracy of pedestrian detection, this paper proposes a target detection algorithm based on yolov3 to detect the missing detection and small-size pedestrian in the original yolov3. This paper introduces ECA attention mechanism to focus on key features and suppress unnecessary features to increase expressiveness; The SPP network structure is added in the algorithm for backbone feature extraction network, which effectively reduces the model calculation amount. The experiment of the improved YOLOv3 algorithm in the INRIA dataset shows that the accuracy of the proposed algorithm in this paper is improved compared with the original model, and the map value is as high as 92.84%. The experimental results present that the detection preciosn is effectively improved and this model can be applied to the detection of daily pedestrians.
Comparative analysis of embedded databases performance on single board computer systems using Python

The article deals with comparative analysis of SQL and NoSQL embedded databases performance on single-board computer systems, such as SQLite, LMDB and UnQLite. The aim of the article is to perform comparative testing of CRUD operations performance through database connection software implemented using Python, on single-board computer systems that need to store data in local storage without continuous running of database server process in system tray. The analysis of the related publications was done and the aim of work was set. The hardware and software was chosen to hold the experiment. The experiment details were described. The results were obtained, highlighting strong and weak features of SQL and NoSQL databases as well as key-value stores. Received results can be used when designing Python software for single-board computers with ability to store large amount of data in device local memory.
Classification With Unimodular Matrices In Hybrid Models

Guessing the architecture of a variational quantum circuit can be fraught with error, since determining the correct locations and types of parameterized quantum gates is often an empirical task. This work demonstrates that using a general parameterized unimodular matrix achieves a higher classification accuracy faster than comparable classical models. Variations of this ansatz and the performance results are explored and discussed to analyze this approach.
Demo: Human-Computable One-Time Passwords

This demo shows an enhanced alternative to the Multi-Factor Authentication (MFA) methods. The improvement lies in the elimination of any supplementary gadgets/devices or theft-sensitive biometric data, by substituting it with direct human-computer authentication. This approach remains secure also in untrusted systems and environments. Despite the use of different identification factors by MFA methods, the basic condition for reliable authentication is the use of the intelligence of the human brain, in the form of a static password. For security reasons, it is recommended to use different passwords for each online account. As a result, users often adopt insecure password practices (e.g., reuse or weak password) or they have to frequently reset their passwords. We solved this problem in such a way that the user reconstructs each of his passwords, calculating the response to the public challenge according to his secret by performing simple mathematical operations, i.e. adding modulo 10. For each internet account, such a challenge must be stored on the server with the correct response as a hashed password, but only the user needs to know the secret, only one secret as a universal private key for all these accounts. This secret key is used by our innovative challenge-response protocol for human-generated One-Time Passwords (OTP) based on a hard lattice problem with noise introduced by our new method which we call Learning with Options (LWO). This secret has the form of an outline like a kind of handwritten autograph (Fig. 1), designed in invisible ink. The password generation process requires following such an invisible contour, similar to a manual autograph, and it can also be done offline on paper documents with an acceptable level of security and usability meeting the requirements for post-quantum symmetric cyphers and commercial implementation also in the field of IoT. Many attempts to achieve this goal have been made for over 30 years since Matsumoto's first publication in 1991, but only two protocols have been commercially implemented: strong but very slow HB, presented by Hopper and Blum in 2000 [2], and easy and fast but very weak grIDsure (GS) presented by Brostoff et al. in 2010 [3]. Our iChip scheme has security properties better than HB and usability close to GS, while eliminating their drawbacks.
Private Federated Framework for Health Data

Federated Learning (FL) is an efficient way to train Machine Learning (ML) algorithms on distributed datasets where data owners are restricted by policies to share their raw data. Through local training and model aggregation to a central server, this method reduces the need to communicate raw data with parties outside of the premises. However, FL raises serious privacy concerns. Therefore, additional privacy measures are required. The differential privacy (DP) approach is a cutting-edge privacy method used to perturb the local models prior to transmission and add an additional layer of privacy. However, this technique can affect the utility of the framework. To balance the privacy-utility trade-off, we implement a hybrid private technique to sanitize raw data using a combination of a top-down taxonomy tree and DP noise. The generalized data using DP noise is used to train local models to be shared in the FL architecture. The proposed framework achieves improved utility with a moderate privacy budget.
Recognition of Voice Commands using CNN for Romanian Language

In this paper, we studied the capability of the DLCNN (Deep Learning Convolutional Neural Network) classifier to be included into an intelligent human-machine interface. This interface will interact with a small number of people (generally one person, the owner of the device: autonomous car, smart home, intelligent wheelchairs, etc.). The main goal was to use a classifier capable of being trained quickly (maximum 10 training epochs), with a minimal set of training data. We need an algorithm of reasonable complexity, that can be integrated on small single-board computers (SBCs) like Raspberry Pi for example. We used 2 sets of voice commands, one in English and one in Romanian, which will train two different DL-CNNs. MFSC (mel-frequency spectral coefficients) coefficients did not provide the expected results, but with MFCC (mel-frequency cepstral coefficients) coefficients we obtained a 92% accuracy rate in the voice commands recognition.
Operations that Allow the Manipulation of Basic Belief Assignment(BBAs)Defined on Product Space

The theory of belief functions is a modeling language for representing and combining pieces of evidence to construct representations of our beliefs about certain aspects of the world. This study proposed to present several concepts and examples to combine a pair of belief functions into a single one. To get enough conditions to change ignorance when independent pieces of evidence are combined with the help of diverse rules. We can evaluate combining rules as depressing or encouraging depending on the sign of the change in ignorance after they are applied. This work has efficient to implement and used successfully in a diversity of applications, including classification, pattern recognition, and sensor fusion.
Memory–Efficient FM-Index Construction for Reference Genomes

FM-index is traditionally constructed over the forward strand complemented with the reverse strand to support searching both strands by executing a single procedure. Although it expedite the process of indexing, it consumes large amount of memory. In this paper we propose a novel algorithm that is capable of compute the FM-index of a given reference sequence without appending its reverse complement to it. In fact, we deduce the rank of the suffixes on the reverse complemented DNA sequences from the suffix ranks on the forward strand. It reduces the memory consumption significantly. Given a reference genome F of length n, FR is the concatenated forward and reverse strand, where R is the reverse complement string of F such that each base is replaced with its corresponding complement in the reverse order. The algorithm makes it possible to compute the FM-index over 2n–symbols long FR by using the FM-index over the n–symbols long FM-index, which nearly halves the memory consumption. The embarrassingly parallel process can speed up significantly with the availability of more cores/threads.
On Quantum-Enhanced LDPC Decoding for Rayleigh Fading Channels

Quantum and Classical computers continue to work together in tight cooperation to solve difficult problems. The combination is thus suggested in recent times for decoding the Low Density Parity Check (LDPC) codes, for the next generation Wireless Communication systems. In this paper we have worked out the Quadratic Unconstrained Binary Optimization (QUBO) formulation for Rayleigh Fading channels for two different scenarios- channel state fully known and not known. The resultant QUBO are solved using D-Wave 2000Q Quantum Annealer and the outputs from the Annealer are classically postprocessed, invoking the notion of diversity. Simple minimum distance decoding of the available copies of the outputs led to improved performance, compared to picking the minimum-energy solution in terms of Bit Error Rate (BER). Apart from providing these results and the comparisons to fully classical Simulated Annealing (SA) and the traditional Belief Propagation (BP) based strategies, some remarks about diversity due to quantum processing are also spelt out.
Blockchain‐based secure deduplication of encrypted data supporting client‐side semantically secure encryption without trusted third party

Abstract To accommodate the new demand for the deduplication of encrypted data, secure encrypted data deduplication technologies have been widely adopted by cloud service providers. At present, of particular concern is how deduplication can be applied to the ciphertexts encrypted by semantically secure symmetric encryption scheme. Avoiding disadvantages of the existing methods, in this article, we propose a blockchain‐based secure encrypted data deduplication protocol supporting client‐side semantically secure encryption. In the proposed protocol, the smart contracts are deployed by the first file uploader, and then the subsequent uploaders implement an interactive proof of ownership for the same file with the help of the smart contracts executing a cloud data integrity auditing protocol. The smart contracts play the role of the trusted third party and therefore make up for the poor feasibility for the existence of a trusted third party in real scenario. In addition, in the proposed protocol, there is no need for other clients who have uploaded the same file to be online to help the current uploader obtain the encryption key. We also prove its security and evaluate its performance.