Reviewerly/exHarmony huggingface dataset:
Frequency Shift Method: A Technique for 3-D Shape Acquisition in the Presence of Strong Interreflections

This paper presents a structured-light codification technique called Frequency Shift Method which is especially designed for scenes containing strong interreflections. The method works by projecting multiple sine wave patterns having different frequencies. While the phase shift method associates a phase to each column of the projector, in the proposed codification, each column of the projector is uniquely encoded by the frequencies used, and explicitly decoded using the Discrete Fourier Transform. This encoding allows the method to recover all the projector columns that contribute to a camera pixel&#x2019;s observed intensity. An energy minimization is then used to separate direct and indirect illuminations. Objects presenting diffuse and specular interreflections are considered and our experimental results illustrate the efficiency of our method compared to previously proposed methods.
Face recognition method combining SVM machine learning and scale invariant feature transform

Facial recognition is a method to identify an individual from his image. It has attracted the intention of a large number of researchers in the field of computer vision in recent years due to its wide scope of application in several areas (health, security, robotics, biometrics...). The operation of this technology, so much in demand in today's market, is based on the extraction of features from an input image using techniques such as SIFT, SURF, LBP... and comparing them with others from another image to confirm or assert the identity of an individual. In this paper, we have performed a comparative study of a machine learning-based approach using several classification methods, applied on two face databases, which will be divided into two groups. The first one is the Train database used for the training stage of our model and the second one is the Test database, which will be used in the test phase of the model. The results of this comparison showed that the SIFT technique merged with the SVM classifier outperforms the other classifiers in terms of identification accuracy rate.
Detecting Credit Card Fraud Using Majority Voting-Based Machine Learning Approach

Credit cards have become the common payment mode for both offline and online purchases as a result of new developments in communication systems and electronic-commerce systems, resulting in a substantial rise in transactions related frauds. Every year, fraudulent credit card purchases budget companies and customers a considerable amount of money and fraudsters are actively attempting to use new technology and methods to commit fraud. The identification of fraud prone transactions has become a major factor influencing the adoption of e-payment systems. As a consequence, there is an inevitable need for accurate algorithms for detecting fraudulent transactions in credit cards. An intelligent method for detecting fraud in credit card transactions is introduced in this paper. Popular machine learning algorithms like logistic regression, gradient boosting, random forest, k-nearest neighbor, and voting classifier are treated against the credit card dataset in this paper. On comparing different approaches, the proposed paper showcases that the voting classifier provides optimal accuracy of 89.25% and F1-score of 0.633.
Verifiable Privacy-Preserving Queries on Multi-Source Dynamic DNA Datasets

DNA sharing and querying of personal genomic sequences have becoming more critical than ever with the accumulation of large-scale biomedical data. Quantitative genomic studies heavily rely on multi-source DNA datasets from different institutions, who are reluctant to share the data via cloud centers. The high sensitivity of DNA has compelled the government to restrict its acquisition and usage. One potential solution to tackle the issue is designing a secure query strategy on the encrypted DNA datasets. However, the popular secure DNA query schemes remain defective in verifiability, reliability, and NDA privacy. To relieve the issues, we propose a DNA searchable encryption method named EncGD to achieve verifiable privacy-preserving queries and reliable updates on multi-source dynamic DNA datasets. Specifically, the proposed scheme EncGD is designed by the plaintext-related permutation and substitution primitives, which can enhance the DNA privacy due to the chosen-plaintext attack (CPA)-resist ability. Furthermore, the method realizes the verifiability and reliability by adding known information to block the malicious behaviors of clouds. Experimental results demonstrate the superior efficiency of the proposed method comparing with the two state-of-the-art schemes in terms of time and space costs.
A Survey on Adaptive Authentication Using Machine Learning Techniques

Adaptive authentication is a reliable technique to dynamically select the best mechanisms among multiple modalities to authenticate a user based on the user's risk profile generated using behavior and context-based information. Websites or enterprise applications enabled with adaptive authentication will have a more robust security system as analyzing the large volume of the user, device, and browser data in real time generates a risk score that decides the appropriate level of security. Though a significant amount of research is being carried out on adaptive authentication, no single model is suitable for a global attack. This paper provides a structured (extensive) survey of current adaptive authentication techniques available in the literature to identify the challenges which demand future research.
Metaheuristic Enhancement with Identified Elite Genes by Machine Learning

The traveling salesman problem (TSP) is a classic NP-hard problem in combinatorial optimization. Due to its difficulty, heuristic approaches such as hill climbing (HC), variable neighborhood search (VNS), and genetic algorithm (GA) have been applied to solve it as they intelligently explore complex objective space. However, few studies have focused on analyzing the objective space using machine learning to identify elite genes that help designing better optimization approaches. For that, this study aims at extracting knowledge from the objective space using a decision tree model. According to its decision-making basis, a simulated boundary is reproduced to retain elite genes from which any heuristic algorithms can benefit. Those elite genes are then integrated into a traditional VNS, unleashing a remarkable enhanced VNS named genetically modified VNS (GM-VNS). Results show that the performance of GM-VNS surpasses conventional VNS in terms of solutions' quality on various real-world TSP instances.
Historical Map Toponym Extraction for Efficient Information Retrieval

The paper deals with detection, classification and recognition of toponyms in hand-drawn historical cadastral maps. Toponyms are local names of towns, villages and landscape features such as rivers, forests etc. The detected and recognized toponyms are utilized as keywords in an information retrieval system that allows intelligent and efficient searching in historical map collections. We create a novel annotated dataset that is freely available for research and educational purposes. Then, we propose a novel approach for toponym classification based on KAZE descriptor. Next we compare and evaluate several state-of-the-art methods for text and object detection on our toponym detection task. We further show the results of toponym text recognition using popular Tesseract engine.
FACEIFY: Intelligent System for Text to Image Translation

Image to Image generation has been a widely explored research topic in recent times. A relatively less explored topic involves multi-modal translation, i.e. Text-to-Image translation. In daily life, people use descriptions for identifying persons. The perception of faces varies from person to person. Different people may focus on different attributes of the face, which may be a non-physical feature too. With the modern computer vision techniques, smart devices could also be empowered to perceive humans and different objects in its surroundings, which makes them intelligent systems. Generating images from the text can have vast applications in different fields. Deep learning was incapable of producing high-quality images until recently. In recent times, the introduction of GANs [4] has successfully been used as generative models for synthesising image, text, speech, etc and has also found application in Text-to-Image translation. The current approaches for image synthesis from text other than GANs are not able to generate images with the required satisfactory details corresponding to the descriptions. This paper aims at enabling intelligent systems with the ability of Text-to-Image translation, focused on generating human facial images from textual descriptions with satisfactory details and consistency with the content of the descriptions. The proposed Text-to-Image translation model is capable of understanding different classes of attributes. Physical attributes such as skin tone, hair, eyes as well as the non-physical attributes like expressions, feelings, mood, etc.
Static Calibration for Line-Scan Cameras Based on a Novel Calibration Target

Automated quality control in industrial environments requires sensors to obtain information about the manufactured products. Visual sensors are crucial for this task. In industrial environments, line-scan cameras are commonly used for their ability to acquire images from fast moving objects at high resolution. However, the information in the images must be translated from image coordinates to scene coordinates. This work proposes a calibration procedure for line-scan cameras particularly designed for industrial environments. The proposed approach is based on a novel calibration target that can be used in multi-camera architectures, a common scenario found in many product inspection pipelines. Industrial environments are very challenging for visual sensors, as they are affected by harsh conditions. The calibration procedure integrates an outlier rejection method to deal with these issues. In addition, the calibration target includes a large number of feature points. Consequently, the removal of some of these features does not affect the performance of the calibration. Additional signal processing methods are proposed to extract features from the images. The calibration procedure uses data acquired from multiple observations by optimizing the reprojection error, resulting in an accurate and robust method, suitable for in industrial environments. The design of the calibration target, the increased number of features, the outlier rejection method and other contributions of this work result in an accurate estimation of the camera parameters. The proposed method is validated using synthetic and real data acquired with a commercial line-scan camera.
Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation

Studies on domain adaptation (DA) for remote sensing (RS) imagery analysis lack consistency in selection and description of evaluation scenarios. Without properly characterizing datasets, model assumptions, and evaluation scenarios, it is difficult to objectively compare DA methods and reach conclusions about their suitability across different applications. With this motivation, this work seeks to empirically assess to which extent the interaction between data characteristics and model assumptions influence the effectiveness of DA methods. Using the widely explored task of building footprint segmentation as case study, we perform a large-scale study across over 200 domain adaptation scenarios that include variations across view angles, areas observed, and sensors used for data acquisition. Rather than adopting different model architectures or optimization criteria, we contrast the performances of two DA methods based on adversarial learning that differ only in their assumptions about source and target domains. Informed by metadata and data characteristics unveiled using traditional computer vision techniques as well as pre-trained deep models, we provide a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks. As a “cherry-picking” exercise demonstrates, different claims regarding which model is best could be made by selecting different subsets of evaluation scenarios. While well-calibrated assumptions can be beneficial, mismatching assumptions can lead to negative biases in DA applications. This study intends to motivate the community towards more consistent evaluation protocols, while providing recommendations and insights toward creating novel benchmark datasets, documenting data characteristics, application-specific knowledge, and model assumptions.
A Hybrid Algorithm for Multilayer Perceptron Design with Intuitionistic Fuzzy Logic Using Malignant Melanoma Disease Data

In the article a method for reducing the input data coming to the input of a neural network of the Multilayer Perceptron type is proposed. The data that was used for the analysis are related to one of the most malignant tumors - malignant melanoma. They refer to patients with malignant melanoma that were registered in Oncology Complex Center in Burgas town. The data and contain information about age, sex, marital status of the patient, date of diagnosis, name of the disease according to the International statistical classification of diseases and health problems. The InterCriteria Analysis (ICA) approach was used to analyze the relationships between these parameters. A new improved structure and algorithm for increasing the learning speed of the Multilayer Perceptron (MLP) neural network are proposed. The algorithm is hybrid and includes determining the degrees of consonance and dissonance between the observed parameters. From the pairs of parameters (from ICA) with the highest consonance and with the lowest dissonance, one parameter is removed. For the purposes of MLP training, one of the values of the fuzzy pair of parameters is redundant because it does not carry additional information, and its removal reduces the number of neurons in the input layer of the neural network. In turn, this reduces the total error generated by each neuron. Another positive part is the faster learning of the neural network due to the lightweight architecture.
DPCrypto: Acceleration of Post-Quantum Cryptography Using Dot-Product Instructions on GPUs

Modern NVIDIA GPU architectures offer dot-product instructions (DP2A and DP4A), with the aim of accelerating machine learning and scientific computing applications. These dot-product instructions allow the computation of multiply-and-add instructions in a single clock cycle, effectively achieving higher throughput compared to conventional 32-bit integer units. In this paper, we show that the dot-product instruction can also be used to accelerate matrix-multiplication and polynomial convolution operations, which are widely used in post-quantum lattice-based cryptographic schemes. In particular, we propose a highly optimized implementation of FrodoKEM wherein the matrix-multiplication is accelerated by the dot-product instruction. We also present specially designed data structures that allow an efficient implementation of Saber key-encapsulation mechanism, utilizing the dot-product instruction to speed-up the polynomial convolution. The proposed FrodoKEM implementation achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.37\times $ </tex-math></inline-formula> higher throughput than the state-of-the-art implementation on a V100 GPU. This paper also presents the first implementation of Saber on GPU platforms, achieving 124,418, 120,463, and 31,658 key exchanges per second on RTX3080, V100, and T4 GPUs, respectively. Since matrix-multiplication and polynomial convolution operations are the most time-consuming operations in lattice-based cryptographic schemes, we strongly believe that the proposed methods can be beneficial to other KEM and signatures schemes based on lattices.
Multi-Source Domain Adaptation and Fusion for Speaker Verification

Recently, domain adaptation model was studied for Speaker Verification (SV) task to solve the performance reduction resulted from various mismatch between the training dataset and the testing dataset. However, since most schemes apply adaptation from single source domain to the target domain, they may not be robust enough against various mismatch between the samples in the source domain and those in the target domain. In this paper, multiple source domain adaptation and fusion model was studied to solve this problem. First, the x-vector based SV scheme is pretrained on each source domain. Second, UnShared Network (USN) combining weight regularization loss based adaptation model is adopted to adapt each pretrained model to the target domain and maintain the speaker-related feature as much as possible. Third, the Registered-Utterance-To-Test-Utterance (RUTTU) similarity matrix is constructed based on the x-vector feature extracted by the adapted model from each source domain. Fourth, the Similarity Network Fusion (SNF) technique is introduced and modified to obtain Modified Similarity Network Fusion (MSNF) scheme, which is used to fuse the RUTTU similarity matrices obtained from multiple adapted models. Finally, speaker matching is achieved by averaging the obtained fused similarities between the test utterance and each of the registered utterances of a specific speaker. Extensive experimental results demonstrate that i) the proposed fusion model outperforms the Multi-source Distilling Domain Adaptation (MDDA) model and Logistic Regression Fusion (LRF) model in SV task; ii) the USN combining weight regularization loss based adaptation strategy, the MSNF fusion scheme, and the modified speaker matching mechanism all contribute to the performance enhancement; iii) the fusion effectiveness is little influenced by the hyper-parameters setting of MSNF.
Blockchain Application Development Using Model-Driven Engineering and Low-Code Platforms: A Survey

The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, no-code and low-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we review academic approaches from the discipline of model-driven engineering as well as industrial no-code and low-code development platforms for blockchains. We further present a case study for an integrated no-code blockchain environment for demonstrating the state-of-the-art in this area. Based on the gained insights we derive requirements for the future development of no-code and low-code approaches that are dedicated to the field of blockchains.
2D Target/Anomaly Detection in Time Series Drone Images Using Deep Few-Shot Learning in Small Training Dataset

Optimization problem is an important challenge in two-dimensional (2D) target/anomaly detection as a real-world application. As manual time series drone image interpretation is time-consuming and expensive, deep learning methods are of high interest for 2D target/anomaly detection. Despite 2D target and anomaly detection from time series drone images based on deep learning models is an active field in remote sensing engineering, but annotating remote sensing time series data is costly for training step. To build robust machine learning methods in remote sensing, deep few-shot learning approaches have been developed from real-world and real-time datasets based on drone images in small training data as an optimized solution. In this chapter, we focus on two real-world applications of 2D target/anomaly detection based on a new deep few-shot learning method, which can be widely used in urban management and precision farming. The experiments are based on two time series multispectral datasets, including traffic monitoring (as a target) and weed detection (as an anomaly). Compared with the few-shot learning with different backbones, the proposed method, called SA-Net, demonstrates better performance and good generalization ability for 2D target/anomaly detection.
A Framework for Mobile Learning Acceptance Amongst Formal Part-Time Learners: From the Andragogy Perspective

Mobile learning is posited to be an effective learning tool for various learners. However, limited studies have been conducted to explore formal part-time learners&#x2019; behavior towards mobile learning. Therefore, this study investigated the level of acceptance of mobile learning by formal part-time learners in Malaysia. A theoretical model was developed based on the unified theory of acceptance and use of technology (UTAUT) and formal part-time learners&#x2019; attributes such as self-directed learning, prior mobile learning experience, learning readiness, and orientation to learning. The developed model and hypotheses were examined using a questionnaire, and measurement and structural models were analysed using SmartPLS (v 3.2.7). The participants of the study comprised 394 formal part-time learners enrolled in five public universities in Malaysia. Finding confirmed the significant influence of self-directed learning, learning readiness, effort expectancy, performance expectancy, social influence, and facilitating conditions in predicting the mobile learning behavioral intention and usage behavior. These findings can be considered by practitioners to enhance behavioral intention towards m-learning usage. The attributes of formal part-time learners toward the usage of m-learning in public universities were properly underlined in this study. This study&#x2019;s main contribution would be to aid in the exploration of formal part-time learners&#x2019; characteristics, which would aid m-learning practitioners in developing the necessary applications and strategies.
Innate Motivation for Robot Swarms by Minimizing Surprise: From Simple Simulations to Real-World Experiments

Applications of large-scale mobile multirobot systems can be beneficial over monolithic robots because of higher potential for robustness and scalability. Developing controllers for multirobot systems is challenging because the multitude of interactions is hard to anticipate and difficult to model. Automatic design using machine learning or evolutionary robotics seem to be options to avoid that challenge, but bring the challenge of designing reward or fitness functions. Generic reward and fitness functions seem unlikely to exist and task-specific rewards often have undesired side effects. Approaches of so-called innate motivation try to avoid the specific formulation of rewards and work instead with different drivers, such as curiosity. Our approach to innate motivation is to minimize surprise, which we implement by maximizing the accuracy of the swarm robot’s sensor predictions using neuroevolution. A unique advantage of the swarm robot case is that swarm members populate the robot’s environment and can trigger more active behaviors in a self-referential loop. In this article, we summarize our previous simulation-based results concerning behavioral diversity, robustness, scalability, and engineered self-organization, and put them into context. In several new studies, we analyze the influence of the optimizer’s hyperparameters, the scalability of evolved behaviors, and the impact of realistic robot simulations. Finally, we present results using real robots that show how the reality gap can be bridged.