Reviewerly/exHarmony huggingface dataset:
Generative feedback residual network for high-capacity image hiding

At present, there is always a potential threat in the process of information transmission. As a way to protect data security, image hiding has attracted extensive attention. Current image hiding algorithms have insufficient resistance to deep leaning based steganalysis algorithms and relatively low hiding capacity. This paper presents an image hiding algorithm based on a generative feedback residual network (GFR-Net), which hides multiple color secret images in a single color carrier image. First, several secret images and a carrier image were fed into the image hiding network, in which the secret images were embedded into the carrier image, resulting in an output of container image. A recovery network also based on GFR-Net was designed to reconstruct the secret images from the container. The extensive experiments for hiding normal and encrypted images show that the proposed image hiding model has a good performance in terms of payload and security.
Igneous rock classification using Convolutional neural networks (CNN)

Abstract This paper describes how convolutional neural networks are used to identify and classify igneous rocks (CNN). Igneous rocks are formed while still hot, hot magma crystallises and solidifies. Melt originates deep beneath the Earth’s surface, amid active plate borders or hot zones, and then rises to the surface. There are also various kinds of igneous rocks, which are addressed throughout this work, distinguishing each one is a difficult feat in and of itself. Machine learning, which is fundamentally a three-layer neural network, is a subset of deep learning. These neural networks attempt to mimic conscious brain function of humans by letting it to “learn” from massive volumes of data, but they continue to fail. A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning approach for assigning priority (learnable weights and biases) to various aspects/objects in a picture while also identifying them. Classification of images, audio and video segmentation, decision support systems, speech recognition, and image analysis are just a few of the applications for CNNs.
Blockchain and Artificial Intelligence: Connecting Two Distinct Technologies to Comply with GDPR's Data Protection By Design Principle

The aim of the paper is to present some of the general principles of data protection law that can be applied to automated decision-making applications embedded into blockchain technology in order to comply with the provision of the European Union’s General Data Protection Regulation (GDPR). The analysis focuses on the applicability of the ‘data protection by design’ principle during the development of such systems. Because blockchain-based networks are built on distributed data processing operations, therefore data controlling or processing of participating nodes should comply some abstract data protection patterns predetermined and collectively built-in during the system’s development phase. On the other hand, the imprint of AI’s automated data processing could be also observed and tracked back in the blockchain due to its historically retroactive nature. In the end, the study presents the human mind and its ‘uploading’ with conscious and unconscious contents as an analogy to blockchain-based AI systems. My goal is to highlight that the synergy of blockchain and machine learning-based AI can be hypothetically suitable to develop robust yet transparent automated decision-making systems. The compliance of these distributed AI systems with data protection law’s principles is a key issue regarding the high risks posed by them to data subjects rights and freedoms.
ISAIR: Deep inpainted semantic aware image representation for background subtraction

Nowadays, deep learning is impacting computer vision applications significantly by learning to extract and describe the essential features that can assist in solving a specific problem. One of the main drawbacks of deep learning-based approaches is their requirement of annotated training data. For instance, in the area of background subtraction, a sequence of annotated training data that demonstrates the foreground and background of the scene is required; such a sequence is required from different scenarios the module performs. Hence, such methods cannot perform directly in new environments, and they expect training data from the new scene. In order to benefit from the high generalization ability and easy-to-use capability of traditional background subtraction techniques, and rich feature representation of deep learning approaches, this paper utilized deep learning based techniques and investigated the best image representation that can assist conventional background subtraction approaches. The proposed image representation is based on the detection of objects which are semantically moving and inpainting the parts of the images related to such objects. Experiments and results show that the proposed image representation can have the online and portable capability of traditional techniques and improve their accuracy by approximately 20% when a conventional type of monocular camera is used. • New image representation based on detecting and in painting inherently moving objects. • Benefiting from advantages of traditional and deep learning approaches simultaneously. • Robust background modeling in different environments without tuning parameters. • End-to-end framework outperforms state-of-the-art background subtraction techniques.
Online Computation of Time-Optimization-Based, Smooth and Path-Consistent Stop Trajectories for Robots

Enforcing the cessation of motion is a common action in robotic systems to avoid the damage that the robot can exert on itself, its environment or, in shared environments, people. This procedure raises two main concerns, which are addressed in this paper. On the one hand, the stopping procedure should respect the collision free path computed by the motion planner. On the other hand, a sudden stop may produce large current peaks and challenge the limits of the motor’s control capabilities, as well as degrading the mechanical performance of the system, i.e., increased wear. To address these concerns, we propose a novel method to enforce a mechanically feasible, smooth and path-consistent stop of the robot based on a time-minimization algorithm. We present a numerical implementation of the method, as well as a numerical study of its complexity and convergence. Finally, an experimental comparison with an off-the-shelf stopping scheme is presented, showing the effectiveness of the proposed method.
A novel image processing technique based on deep learning for water consumption detection

In recent years, traditional image processing techniques have seen the introduction of novel tools, able to face issues that are not always handy with classical vision algorithms. For example, classical image processing algorithms (measurement, detection of features, and many others) require a controlled environment, like illumination, target positioning, and vibration that can influence the scene for the correct operation. On the other hand, the machine learning approaches enabled image processing techniques also in non-controlled environments. One of these applications can be represented by developing a leak detector at the household level, based on processing pictures of the mechanical water meter dial. The proposed research investigates using a deep learning approach to detect the minimal movement of the water meter needles related to water leakage. In particular, a CNN was trained to correlate successive differences on the water meter dial images taken with an applied calibrated water flow. From this analysis, it is possible to detect the absence of periods with null consumption and thus detect small water losses.
Hash-based signature revisited

Abstract The current development toward quantum attack has shocked our confidence on classical digital signature schemes. As one of the mainstreams of post quantum cryptography primitives, hash-based signature has attracted more and more concern in both cryptographic research and application in recent years. The goal of this paper is to present, classify and discuss different solutions for hash-based signature. Firstly, this paper discusses the research progress in the component of hash-based signature, i.e., one-time signature and few-time signature; then classifies the tree-based public key authentication schemes of hash-based signature into limited number and stateful schemes, unlimited number and stateful schemes and unlimited number and stateless schemes. The above discussion aims to analyze the overall design idea of different categories of hash-based signatures, as well as the construction, security reduction and performance efficiency of specific schemes. Finally, the perspectives and possible development directions of hash-based signature are briefly discussed.
Estimation of Solar Radiation on a PV Panel Surface with an Optimal Tilt Angle Using Electric Charged Particles Optimization

Solar energy is a promising renewable energy source that can fulfill the world’s current and future energy needs. The angle at which a photovoltaic (PV) panel faces the horizon determines the incidence of solar radiation. The incident solar radiation on PV panels could be optimized by adjusting their tilt angles and increasing the power output of the PV array. In this study, solar energy model-based research was conducted in the Saudi Arabian cities of Dhahran and Makkah. This study investigated the performance of a 1 kW monocrystalline silicon PV array in these cities. Analyzing the optimal tilt angle for efficiency and performance improvement of the PV panel is challenging. The optimal tilt angle is determined by combining the data of the Sun’s diffuse, direct radiation and the global horizontal Sun radiation. This research examined the four empirical models by applying the electric charged particle optimization (ECPO) algorithm to estimate the solar radiation on sloped surfaces. The model’s results were compared to the global horizontal solar radiation based on the daily mean solar radiation value in these cities. The Hay–Davies–Klucher–Reindel model presented the maximum amount of tilted surface solar radiation in the year and at different periods. In contrast, the Badescu model exhibited the weakest results of all the isotropic and anisotropic models. Finally, using the ECPO algorithm, all models indicated that tilted surfaces (IT) received more solar radiation than horizontal surfaces (Ig).
Analyzing the Use of Heuristics in a Virtual Reality Learning Context: A Literature Review

With concepts such as immersion and presence, known as hedonistic qualities of importance in the virtual reality (VR) experience, the question arises whether the more pragmatic heuristics are effective in the evaluation of an artifact. However, despite the importance of the heuristics for artifact evaluations, the available studies do not provide a rigorous review of these heuristics and their efficiency. Thus, this review aims to look at how heuristics have been applied in various virtual learning environments (VLEs) that involve virtual reality learning activities, either for use in heuristic evaluations or as design principles. In addition, it examines how these heuristics support the evaluation of a more hedonistic quality, such as presence, and lastly, the aim is to gauge the estimated efficiency of heuristics as an evaluation method. This article is a systematic review of research investigating using heuristics in a virtual reality learning context. The review includes articles published from January 2017 to February 2022, and from the screened records, twelve articles were analyzed in full-text form. This review shows the versatility of heuristics and their applications as well as the key concepts that are vital to the user’s experience in a virtual reality learning context. This review indicates that heuristic evaluation is a valuable tool, as it provides a clear summary of what needs to be handled in the next iteration of the application.
ML-based reconfigurable symbol decoder: An alternative for next-generation communication systems

Modern Machine Learning (ML) techniques offer numerous opportunities to enable intelligent communication designs while addressing a wide range of problems in communication systems. A wide majority of communication systems ubiquitously employ the Maximum Likelihood (MLH) decoder in the symbol decoding process with QPSK modulation, thereby providing a non-reconfigurable solution. This work addresses the application of an ML-based reconfigurable solution for such systems. The proposed decoder can be considered a strong candidate for future communication systems, owing to its upgradable functionality, lower complexity, faster response, and reconfigurability. First, a novel low-complexity dataset for model training/testing is generated, that uses only the received symbols. Subsequently, three predictors are extracted from each of the received noisy symbols for model training/testing. The model is then trained/tested using nineteen standard ML-based classifiers, and the computations of various performance metrics indicate the suitability of Naïve Bayes (NB), and Ensemble Bagged Decision Tree (EBDT) classifiers for the model. The simulation results show that the model respectively delivers significant decoding accuracies and error rates of about 93% and 7% during testing, even for a low SNR of 5 dB. Moreover, the statistical analysis of simulation results shows the marginal superiority of the Gaussian Naïve Bayes (GNB) classifier. Further, the model reconfiguration is validated using a BPSK modulated dataset. Finally, a user-separation scheme that eliminates Successive Interference Cancellation (SIC) in the next-generation Power-Domain (PD) Non-Orthogonal Multiple Access (NOMA) networks is suggested by employing the proposed decoder.
A Visual Human-Computer Interaction System Based on Hybrid Visual Model

The traditional human-computer interaction is mainly through the mouse, keyboard, remote control, and other peripheral equipment electromagnetic signal transmission. This paper aims to build a visual human-computer interaction system through a series of deep learning and machine vision models, so that people can achieve complete human-computer interaction only through the camera and screen. The established visual human-computer interaction system mainly includes the function modes of three basic peripherals in human-computer interaction: keyboard, mouse (X-Y position indicator), and remote control. The convex hull method was used to switch between these three modes. After issuing the mode command, Gaussian mixture was used to quickly identify the moving human body to narrow the scope of our image processing. Subsequently, finger detection in human body was realized based on the Faster-RCNN-ResNET50-FPN model structure, and realized the function of moving mouse and keyboard through the relationship between different fingers. At the same time, the recognition of human body posture was done by using MediaPipe BlazePose, and the action classification models were established through the Angle between body movements so as to realize the control function of remote control. In order to ensure the real-time performance of the interactive system, according to the characteristics of different data processing processes, CPU and GPU computing power resources are used to cross-process images to ensure the real-time performance. The recognition accuracy of the human-computer interaction system is above 0.9 for the key feature points of human body, and above 0.87 for the recognition accuracy of four kinds of command actions. It is hoped that vision-based human-computer interaction will become a widely used interaction mode in the future.
Unintentional modulation microstructure enlargement

Radio frequency fingerprinting (RFF) is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level, device-specific imperfections. The RFF-related information is mainly in the form of unintentional modulation (UIM), which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation (IM). It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF. This paper proposes a UIM microstructure enlargement (UMME) method based on feature-level adaptive signal decomposition (ASD), accompanied by autocorrelation and cross-correlation analysis. The common IM part is evaluated by analyzing a newly-defined benchmark feature. Three different indexes are used to quantify the similarity, distance, and dependency of the RFF features from different devices. Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode. The visual image qualitatively shows the magnification of feature differences; different indicators quantitatively describe the changes in features. Compared with the original RFF feature, recognition results based on the Gaussian mixture model (GMM) classifier further validate the effectiveness of the proposed algorithm.
A Humanoid Robot for Hand-Sign Recognition in Human-Robot Interaction (HRI)

Using a set of features, a 3-D representation of the angular relationship between a dozen biological components is created. Hidden Markov models are then applied to a feature vector in order to encode it. An in-depth explanation of the process of developing a transition gesture model is presented in order to accurately recognize significant movements. Model reduction, which combines similar states using data-dependent statistics and relative entropy to lower the number of states in the transition gesture model, is used to reduce the number of states in the model. A posture or stance is defined by the static hand combinations and hand postures that are used. In this paper, we investigate hand gesture detection using data from time-of-flight sensors and existing machine learning methods. The findings demonstrate that hand postures may be recognized with a minimum of inaccuracy when using a computer. According to the experimental results, the system successfully recognizes hand motions, and the system's performance is suitable for real-time implementations.
Sentiment Analysis on E-Commerce Apparels using Convolutional Neural Network

The Fourth Industrial Revolution (4.0) is a fusion of advances in Artificial Intelligence (AI), Robotics, the Internet of Things (IoT), Genetic Engineering, Quantum Computing, and other technologies. A large number of people are using internet-based services as a result of enhanced internet infrastructure and decreased costs. As a result, such businesses' attempts to penetrate internet media are disrupted. The e-commerce company, like Amazon, offers both customer-to-customer and business-to-business services in the apparel sector. Companies must understand the needs of buyers to maximize their profits. As a result, consumer sentiment analysis is carried out. However, because this procedure is time-consuming, it is made automatically utilizing artificial intelligence approaches. According to the findings of a study on sentiment analysis on an E-Commerce-based web store for women, the apparels review dataset using the CNN method with the word vector generator and TF-IDF can produce a higher accuracy of 94%.
Improving the Accuracy of Object Detection in Low Light Conditions using Multiple Retinex Theory-based Image Enhancement Algorithms

Object Detection is a vast field that has many applications in present and upcoming technologies. However, improving the accuracy of object detection algorithms remains a persistent challenge. There are some limitations to its accuracy and many factors like image quality, noise, and the illumination of the image play a crucial role in it. It is more likely that an image would have noise if it was captured in low illumination conditions as the camera captures less light. To solve this problem and improve object detection accuracy, this study proposes to pass the low exposure image through existing Retinex theory-based low light image enhancement models and then its output to be passed into an object detection algorithm. Retinex based image enhancement models estimate the areas with low exposures and noise is reduced from the image as well with the help of neural networks. This demonstrates a positive impact on the confidence values of the object detection and more tendency for an object to be detected. Lastly, a comparison has also been performed on three existing low light image enhancement models. MIRNet, MBLLEN, and TCN models have been used for comparison based on confidence values of the objects detected in various images.
Permissioned Blockchain Voting System using Hyperledger Fabric

Elections are an integral part of democracy. With democracy being the most popular government type in today’s world, the need for the use of free and fair elections to elect a presidential candidate or a party has increased. Elections allow everyone in the country above a certain age group (Age limit as per a country or state’s norms) irrespective of their gender, caste, color, religion, etc., to take part in election and vote for the candidate or party of their choice. The most popular methods of conducting elections are using ballot paper and electronic voting machines (EVM’s). These methods come with their own set of challenges such as lack of security, inefficiency, bogus voting, increased expenditure and ease of manipulation of the props used for voting. In order to tackle these challenges, we are proposing an implementation of elections by making use of permissioned blockchain technology. We aim to make a decentralized voting system using Hyperledger Fabric framework to establish a permissioned blockchain network which would negate the disadvantages of previously used voting mechanisms out there and restrengthen the true meaning of free, fair and sovereign election. In this paper we have described several use cases explaining the various security measures that could be added in order to improve security of the voting systems. Also, we have made use of Hyperledger Caliper to benchmark our blockchain based voting system and test out the system performance with respect to different chaincodes.
SAME: The Design Space for Seamless Automotive Multimodal Experience

Multimodal user interfaces for automotive applications, such as voice, gesture, and head-up displays, have significantly enriched the user experience. From a brief literature review, we discuss the concept of seamlessness in autonomous vehicles, and propose a new design space of multimodal user interfaces within the upgrade of driving automated systems and human multitasking across devices. We present a case study illustrating the use of our model in the automotive industry.
A slimmer and deeper approach to deep network structures for low‐level vision tasks

Abstract Deep network design is a fundamental challenge. A right trade‐off between depth and complexity of convolutional neural networks is of significant importance to applications in low‐level vision tasks. Wider feature maps could be beneficial to performance and generality but would increase computational complexity. In this paper, we rethink the balance between width of the feature maps and depth of the network especially for image restoration tasks including deblurring, dehazing, super‐resolution, and denoising. We explore a new approach to network structure by encouraging more depth to deal with restoration requirements while decreasing the width of some feature maps. Such a slimmer and deeper approach can enhance the performance while maintaining the same level of computational costs. We have experimentally evaluated the performances of the proposed approach on four image restoration tasks and obtained state‐of‐the‐art results on quantitative measures and qualitative assessments, demonstrating the effectiveness of the approach.