Reviewerly/exHarmony huggingface dataset:
MDPL-net: Multi-layer Dictionary Learning Network with Added Skip Dense Connections

Dictionary learning (DL) is powerful for representation learning, while it fails to capture the deep hierarchical information hidden in data. In this paper, we propose a new generalized end-to-end mulita-layer representation learning architecture referred to as Multi-layer Dictionary Pair Learning Network (MDPL-net) for the deep sparse and hierarchical representation of images. To enable MDPL-net to conduct accurate classification, MDPL-net clearly integrates the skip connection end-to-end network and multi-layer deep sparse dictionary learning into a unified architecture. The representation learning module has several hidden DL blocks, where each hidden DL block has a dictionary pair learning (DPL) layer, a batch-norm layer and an activation function layer, and the DL blocks are connected in a feed-forward manner. To further improve the information flow and maintain the privileged features between different DL blocks, a novel skip dense connectivity pattern is deployed between hidden DL blocks, which can obtain more stable and discriminative features. The DPL layer jointly formulates the discriminative synthesis dictionary and analysis dictionary by minimizing reconstruction error within each batch over the feature maps from front layers. Extensive results on benchmark databases demonstrate the effectiveness of MDPL-net for discriminative representation and robust image classification.
A text as unique as fingerprint

Authorship Attribution, study to deduce an author for a given text based on then writing characteristics is a problem with a long history and refers to the task of author recognition of a text in a group of candidate authors, based on relevant features extracted from the text (Stylometry). Stylometry occurs by identifying relevant attributes that define a space in which authors can be distinguished from each other. Writers use language in different ways to express their ideas, and this variation in language makes authorship recognition possible. In this paper the problem of authorship recognition is being exposed in order to make it a tool for use in the distance education platform of the Ministry of Health: AVASUS. Therefore, the stylometric characteristics are extracted from the collected data set and different classification algorithms are trained to predict the authors of texts longer than 30 characters. The results were relevant obtaining values of 92% accuracy in some classifiers and showed that extraction techniques of stylometric characteristics could be used to recognize the author of a text.
Vehicle Type Classification in Surveillance Image based on Deep Learning Method

Vehicle type classification is an important part of intelligent traffic. With the development of research in the field of classification, especially in deep learning, many Convolutional Neural Network (CNN) architectures have been created. This becomes very challenging because increasing the accuracy of the CNN architecture in classifying vehicle types will contribute to the field of intelligent traffic systems. The method we propose is to improve the existing CNN architecture, ResNet-50, by replacing the Global average pooling (GAP) function with a flatten layer and adding a hidden layer before softmax activation. We set the number of filter on the residual block so that the parameters used are smaller than ResNet50. Our research focuses on the vehicle front view image dataset from surveillance cameras for the training and testing process. From the experimental results, our proposed method in the vehicle type classification outperforms ResNet-50, VGG16 Network and CNN in previous studies by yielding accuracy of 96.26%.
Evaluating the Modsecurity Web Application Firewall Against SQL Injection Attacks

SQL injection attacks target databases of web servers. The ability to modify, update, retrieve and delete database contents imposes a high risk on any website in different sectors. In this paper, we investigate the efforts done in the literature to detect and prevent the SQL injection attacks. We also assess the efficiency of the Modsecurity web application firewall in preventing SQL injection attacks.
Instantaneous frequency extraction of highly nonstationary optical interferometric signal using Reassigned Smoothed Pseudo Wigner Ville Distribution

Instantaneous frequency (IF) of optical interferometric signal often carries information on various physical parameters such as velocity, strain and displacement. We propose smoothed Pseudo Wigner-Ville distribution method for extracting IF of highly non-stationary polynomial frequency modulated (FM) fringe signal. Cohen class signal representation is followed and time and frequency resolutions are independently optimized using separable kernel. Reassignment technique is further applied for sharpening of the spectrogram with these optimized kernels that improves accuracy in IF significantly. Quadratic FM along with strong nonlinear IF based fringe signal is considered for showing the efficacy of proposed method. Results are compared with that of existing Polynomial Wigner Ville Distribution method and Continuous Wavelet Transform (CWT) based approach and found to be more accurate in proposed technique. Finally fringe signal generated by varying the length of one of the legs of Michelson interferometer has been used to validate the proposed method experimentally.
Inverted Residual Siamese Visual Tracking With Feature Crossing Network

Siamese networks based visual tracking has recently drawn great attention due to their superior representation and tracking accuracy. However, the backbone networks and prediction networks still cannot fully take advantage of features from modern deep networks. In this paper, we propose an inverted residual Siamese feature-crossing network (IRSiamese-FCN) which is end-to-end trained off-line with a large amount of image pairs. Specifically, the Siamese backbone networks for feature extraction consist of an inverted residual network and a feature-crossing network (FCN). The designed IR architecture is light weighted by combination of depthwise and pointwise convolutions. Moreover, non-linearities and linearities are proceeded separately in deep and narrow layers. Feature-crossing network is to perform feature-level aggregations, which makes deep and shallow layers complement each other more closely and further improves tracking accuracy. We conduct ablation studies and comparison experiments over five large benchmarks. The results demonstrate that the proposed tracker can achieve competitive performance.
Survey paper: Taxonomy of website anti-phishing solutions

Nowadays web users are vulnerable to various cyber-attacks, among these attacks we find phishing. Phishing is a social engineering cyberattack, which targets naive online users tricking into revealing sensitive information such as username, password, social security number or credit card number, etc, based on imitating legitimate websites or sending a malicious URL in an email to deceive users. Several anti-phishing solutions have been developed such as black/whitelist, visual similarity, machine learning methods ... etc, but phishing attacks keep on growing, which has made phishing detection a primary concern of developers. In this paper, we present a taxonomy of an anti-phishing solutions. The classification helps in understanding various anti-phishing approaches and algorithms developed for phishing detection.
Residue-Net

Deep neural networks are widely deployed on embedded devices to solve a wide range of problems from edge-sensing to autonomous driving. The accuracy of these networks is usually proportional to their complexity. Quantization of model parameters (i.e., weights) and/or activations to alleviate the complexity of these networks while preserving accuracy is a popular powerful technique. Nonetheless, previous studies have shown that quantization level is limited as the accuracy of the network decreases afterward. We propose Residue-Net, a multiplication-free accelerator for neural networks that uses Residue Number System (RNS) to achieve substantial energy reduction. RNS breaks down the operations to several smaller operations that are simpler to implement. Moreover, Residue-Net replaces the copious of costly multiplications with non-complex, energy-efficient shift and add operations to further simplify the computational complexity of neural networks. To evaluate the efficiency of our proposed accelerator, we compared the performance of Residue-Net with a baseline FPGA implementation of four widely-used networks, viz., LeNet, AlexNet, VGG16, and ResNet-50. When delivering the same performance as the baseline, Residue-Net reduces the area and power (hence energy) respectively by 36% and 23%, on average with no accuracy loss. Leveraging the saved area to accelerate the quantized RNS network through parallelism, Residue-Net improves its throughput by 2.8x and energy by 2.7x.
Why cybersecurity insurance should be regulated and compulsory

This paper argues that promoting and regulating cybersecurity insurance could solve a key problem: despite the well-publicized hacks of businesses across the world and numerous government awareness campaigns, many small- and medium-sized companies (SMEs) in Europe do not practise proper cybersecurity. Introducing compulsory cybersecurity insurance for SMEs would be the single most effective way to achieve cyber resilience in a modern digital economy and protect businesses from both cybercriminals and state-sponsored hackers. Besides setting minimum standards for company cybersecurity and ensuring that post-breach support services are included in every insurance policy, governments must also address significant issues in the emerging cyber insurance market such as removing false incentives regarding ransoms and fines and creating a backstop mechanism to address aggregate risk. Moreover, they should ensure that all claims are collected in one database since this data would transform our understanding of malware threats and the costs they are causing. Combining these measures could unleash the potential of cyber insurance for the protection of all businesses and their customers, especially if the EU adopts a coherent policy for all member states.
EEDNet: Enhanced Encoder-Decoder Network for AutoISP

Image Signal Processor (ISP) plays a core rule in camera systems. However, ISP tuning is highly complicated and requires professional skills and advanced imaging experiences. To skip the painful ISP tuning process, we introduce EEDNet in this paper, which directly transforms an image in the raw space to an image in the sRGB space (RAW-to-RGB). Data-driven RAW-to-RGB mapping is a grand new low-level vision task. In this work, we propose a hypothesis of the receptive field that large receptive field (LRF) is essential in high-level computer vision tasks, but not crucial in low-level pixel-to-pixel tasks. Besides, we present a ClipL1 loss, which simultaneously considers easy examples and outliers during the optimization process. Benefiting from the LRF hypothesis and ClipL1 loss, EEDNet can generate high-quality pictures with more details. Our method achieves promising results on Zurich RAW2RGB (ZRR) dataset and won the first place in AIM2020 ISP challenging.
Clustering Research Papers Using Genetic Algorithm Optimized Self-Organizing Maps

With the huge amount of published research papers, retrieving relevant information is a difficult task for any researcher. Effective clustering algorithms can help improve and simplify the retrieval process. Here, we propose an approach for automatic clustering for text document using a Self-Organizing Map (SOM). It is one of unsupervised artificial neural network that widely used for data analysis, data compression, clustering, and data mining. The quality and accuracy of a SOM algorithm depends on the selection of values for some of its parameters which are its initial learning rate, SOM matrix dimensions, and the number of iterations. Best values are typically selected using trial and error; however, in the current paper we suggest a more systematic approach to parameters optimization using the genetic algorithm. The proposed method is applied to cluster 3 scientific papers datasets using their keywords. Similar research papers were mapped closer to each other. Clustering results were validated using the Dunn index.
A survey on Augmented Virtual Reality: Applications and Future Directions

Augmented Reality (AR) is an evolving technology that reforms different fields like education and learning, manufacturing, health, etc. Companies are experiencing new changes in their process due to the adoption of new technology for their performance improvement and cost reduction. Therefore, research made on AR must consider an interdisciplinary review to understand its role as a hybrid model and generate an organized framework. The integration of emerging technologies has helped in the overall transformation of the business for a better chance. The research investigates the existing integration of innovative augmented reality (AR) technology, which has different application areas. Based on the literature survey, future research directions can be derived for the researchers and practitioners.
On Preserving Variability Consistency in Multiple Models

Model-driven software product line engineering (MDPLE) is a holistic approach to realize variability-intensive systems by using models. In MDPLE the usage of models aims to increase the level of automation by reducing the product derivation to a pure code derivation step. Since models are present at different development phases, they have to be kept consistent all over these phases, for example by storing information about corresponding elements in model transformations. Reasons why to use model transformations or similar automated mechanisms are manifold. For instance, if the product line is built in a forward-engineering process, model transformations will be beneficial to propagate the coarse-grained information of an early phase to the subsequent phase automatically. In contrast to single-variant engineering, in MDPLE there is not only the challenge to keep multiple models consistent but also their presence conditions. Since variability mechanisms and the ways how presence conditions across different models are maintained vary, this contribution categorizes the consistency maintenance of presence conditions in MDPLE approaches to give an overview of already existing techniques. As a result, we find that while several automated solutions to keep presence conditions across models consistent exist, they are not employed in the MDPLE tool landscape.
OPLS-SR: A novel face super-resolution learning method using orthonormalized coherent features

Face super-resolution (FSR) is an effective way to deal with low-resolution (LR) face images, which can infer the latent high-resolution (HR) face images from the LR inputs. In contrast with traditional FSR methods such as interpolation, learning-based methods generate more realistic HR images of LR faces by exploiting the relationship between HR and LR images. In this paper, we propose a novel FSR learning approach based on orthonormalized partial least squares referred to as OPLS-SR. It first learns a latent coherent feature space of low-dimensional HR and LR face embeddings via a recursive optimization, and then super-resolves the LR face images through global face reconstruction and facial detail compensation. Experimental results on the CAS-PEAL-R1 and FERET face databases have demonstrated the effectiveness of the proposed OPLS-SR method in terms of quantitative and qualitative evaluations.
NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols

Web search engines usually keep users’ profiles for multiple purposes, such as result ranking and relevancy, market research, and targeted advertisements. However, user web search history may contain sensitive and private information about the user, such as health condition, personal interests, and affiliations that may infringe users’ privacy since a user’s identity may be exposed and misused by third parties. Numerous techniques are available to address privacy infringement, including Private Information Retrieval (PIR) protocols that use peer nodes to preserve privacy. Previously, we have proved that PIR protocols are vulnerable to the QuPiD Attack. In this research, we proposed NN-QuPiD Attack, an improved version of QuPiD Attack that uses an Artificial Neural Network (RNN) based model to associate queries with their original users. The results show that the NN-QuPiD Attack gave 0.512 Recall with the Precision of 0.923, whereas simple QuPiD Attack gave 0.49 Recall with the Precision of 0.934 with the same data.
Technology and mathematics as a cognitive component

Abstract The objective of the study was to analyze the technological impact and cognitive attributes of technology for the construction of meaningful learning of mathematics students at Universities in Ecuador and Colombia. The sample was non-probability, with the participation of 1124 students. A mixed study of sequential explanatory design was applied. In the quantitative phase, correlational method, were applying a digital questionnaire, the data were analyzed through multiple regression and statistical analysis of nested variance to two variables demonstrating the need to implement mathematical software. In the qualitative phase, the mathematical software Wiris and Geogrebra were implemented and results merged by the contrasting method. The conclusions demonstrate the impact of digital connectivity, distinguishing technology as a cognitive component through dynamic knowledge. The method was effective to the development of logical analytic thinking; boosted the activity of the right limbic lobe.
Facilitating Exploration with Interaction Snapshots under High Latency

Latency is, unfortunately, a reality when working with large data sets. Guaranteeing imperceptible latency for interactivity is often prohibitively expensive: the application developer may be forced to migrate data processing engines or deal with complex error bounds on samples, and to limit the application to users with high network bandwidth. Instead of relying on the backend, we propose a simple UX design-interaction snapshots. Responses of requests from the interactions are asynchronously loaded in "snapshots". With interaction snapshots, users can interact concurrently while the snapshots load. Our user study participants found it useful not to have to wait for each result and easily navigate to prior snapshots. For latency up to 5 seconds, participants were able to complete extrema, threshold, and trend identification tasks with little negative impact.
Entropy Reduction Model for Pinpointing Differential Fault Analysis on SIMON and SIMECK Ciphers

In this article, we present a formal model of entropy reduction across the rounds when a fault is injected in SIMON and SIMECK family of lightweight ciphers. The model helps to pinpoint a range of intermediate rounds in a cipher of the same family, which when subjected to a fault injection requires minimal number of such attempts to reveal the secret key. The range of such rounds depict increased vulnerability to fault analysis attacks and, thus, require a stronger countermeasure for such rounds. We demonstrate the proposed entropy-reduction model for all versions of SIMON and SIMECK. The comparisons with existing fault analysis attacks depict that our proposed model requires least number of faults and smaller attack time in almost all versions of SIMON and SIMECK. The proposed entropy-reduction model can be used as a tool for the designers for any generic lightweight Feistel cipher to identify the vulnerable rounds in the encryption/decryption algorithms.
Mutual-information-inspired heuristics for constraint-based causal structure learning

In constraint-based approaches to Bayesian network structure learning, when the assumption of orientation-faithfulness is violated, not only the correctness of edge orientation can be greatly degraded, the soaring cost of conditional independence testing also limits their applicability in learning very large causal networks. Inspired by the strong connection between the degree of mutual information shared by two variables and their conditional independence, we extend the PC-MI algorithm in two ways: (a) the Weakest Edge-First (WEF) strategy implemented in PC-MI is further integrated with Markov-chain consistency to reduce the number of independence testing and sustain the number of false positive edges in skeletal learning; (b) the Smaller Adjacency-Set (SAS) strategy is proposed and we prove that the Smaller Adjacency-Set captures sufficient information for determining whether an unshielded triple forms a v-structure. We have conducted experiments with both low-dimensional and high-dimensional data sets, and the results indicate that our MIIPC approach outperforms the state-of-the-art approaches in both the quality of learning and the execution time.
Curve448 on 32-Bit ARM Cortex-M4

Public key cryptography is widely used in key exchange and digital signature protocols. Public key cryptography requires expensive primitive operations, such as finite-field and group operations. These finite-field and group operations require a number of clock cycles to execute. By carefully optimizing these primitive operations, public key cryptography can be performed with reasonably fast execution timing. In this paper, we present the new implementation result of Curve448 on 32-bit ARM Cortex-M4 microcontrollers. We adopted state-of-art implementation methods, and some previous methods were re-designed to fully utilize the features of the target microcontrollers. The implementation was also performed with constant timing by utilizing the features of microcontrollers and algorithms. Finally, the scalar multiplication of Curve448 on 32-bit ARM Cortex-M4@168 MHz microcontrollers requires 6,285,904 clock cycles. To the best of our knowledge, this is the first optimized implementation of Curve448 on 32-bit ARM Cortex-M4 microcontrollers. The result is also compared with other ECC and post-quantum cryptography (PQC) implementations. The proposed ECC and the-state-of-art PQC results show the practical usage of hybrid post-quantum TLS on the target processor.
Earthquake Early Warning Systems as an Asset Risk Management Tool

Losses due to strong seismic events can amount to millions or billions of US dollars and can affect regions for large periods of time, even severely undermining the economy. Earthquake early warning systems have proven to be helpful tools to mitigate the social and economic impact on communities and businesses. Recent case studies are briefly described, followed by examples of proactive measures for assets, infrastructure, citizens education and empowerment, complementary to earthquake early warning systems.
Chatbot to Simplify Customer Interaction in e-Commerce Channels of Retail Companies

Today, e-commerce has become the main channel of the retail sector, however, the abandonment of online shopping has also increased. This is because it is difficult for customers to navigate the website and they feel the unavailability of a sales consultant to guide them and resolve their doubts. The objective of this study is to propose a chatbot that allows to finish the purchases, improving the user experience in the process of buying online. In addition, it will be validated through acceptance tests to verify in what percentage it simplifies customers' interaction during the whole online purchase process and customer service.