Reviewerly/exHarmony huggingface dataset:
Application of VR technology in civil engineering education

Abstract Virtual reality (VR) technology is a kind of computer simulation system that can create and experience the virtual world. It uses computers to generate a simulation environment. It is a system simulation of multisource information fusion, interactive three‐dimensional dynamic view, and physical behavior for users to immerse in the environment. The practical application of virtual instructional models is developed to support civil engineering teaching, disciplines related to civil engineering processes, including classroom education and e‐learning technologies. The virtual model can be interactively operated, allowing the teacher or student to monitor the physical evolution of the work and the progress of architectural activities inherent in it. This paper analyzes the development status of VR and its application in teaching research and describes the application of a VR teaching model to demonstrate the feasibility and advantages of VR teaching. The developed application can display the physical evolution of the task, detect the planned construction sequence, and view the shape details of each construction component. The use of VR technology in developing these instructional applications contributes to education by increasing the efficiency of the models to allow for the interactivity of each simulation task. Therefore, the new concept of VR technology applied in teaching mode brings a new perspective to civil engineering education.
A New Image Encryption Algorithm Based on Single S-Box and Dynamic Encryption Step

Chaotic-based S-box image encryption schemes promise the practicability solution in securing digital images. However, the high-dimensional continuous chaotic has increased the algorithm’s complexity. Recent alternatives that focused on double or multiple S-boxes approaches, on the other hand, have been proven vulnerable to differential attacks. This paper presents an efficient and secure chaotic-based S-box image encryption scheme. Firstly, a single S-box with a size of 10×26 was constructed by using a low-dimensional chaotic system. Without a complex mathematical operation, the constructed single S-box has obvious efficiency advantages and achieved a higher image entropy rate than recent double or multiple S-boxes. Secondly, a new dynamic encryption step method is proposed to solve the high correlation and deterministic problems in multiple S-box encryptions. Under the control of the dynamic encryption step algorithm, it effectively destroys the correlation between the source image’s pixels. The experimental results and security analysis show that the proposed scheme enjoys higher security and is more efficient to secure digital images in real-world applications.
Digital Transformation and Artificial Intelligence Applied to Business: Legal Regulations, Economic Impact and Perspective

Digital transformation can be defined as the integration of new technologies into all areas of a company. This technological integration will ultimately imply a need to transform traditional business models. Similarly, artificial intelligence has been one of the most disruptive technologies of recent decades, with a high potential impact on business and people. Cognitive approaches that simulate both human behavior and thinking are leading to advanced analytical models that help companies to boost sales and customer engagement, improve their operational efficiency, improve their services and, in short, generate new relevant information from data. These decision-making models are based on descriptive, predictive and prescriptive analytics. This necessitates the existence of a legal framework that regulates all digital changes with uniformity between countries and helps a proper digital transformation process under a clear regulation. On the other hand, it is essential that this digital disruption is not slowed down by the regulatory framework. This work will demonstrate that AI and digital transformation will be an intrinsic part of many applications and will therefore be universally deployed. However, this implementation will have to be done under common regulations and in line with the new reality.
TypeNet: Deep Learning Keystroke Biometrics

We study the performance of Long Short-Term Memory networks for keystroke biometric authentication at large scale in free-text scenarios. For this we explore the performance of Long Short-Term Memory (LSTMs) networks trained with a moderate number of keystrokes per identity and evaluated under different scenarios including: i) three learning approaches depending on the loss function (softmax, contrastive, and triplet loss); ii) different number of training samples and lengths of keystroke sequences; iii) four databases based on two device types (physical vs touchscreen keyboard); and iv) comparison with existing approaches based on both traditional statistical methods and deep learning architectures. Our approach called TypeNet achieves state-of-the-art keystroke biometric authentication performance with an Equal Error Rate of 2.2% and 9.2% for physical and touchscreen keyboards, respectively, significantly outperforming previous approaches. Our experiments demonstrate a moderate increase in error with up to 100,000 subjects, demonstrating the potential of TypeNet to operate at an Internet scale. To the best of our knowledge, the databases used in this work are the largest existing free-text keystroke databases available for research with more than 136 million keystrokes from 168,000 subjects in physical keyboards, and 60,000 subjects with more than 63 million keystrokes acquired on mobile touchscreens.
Server-Aided Bilateral Access Control for Secure Data Sharing With Dynamic User Groups

As a versatile technique, cloud-fog computing extends the traditional cloud server to offer various on-demand data services. Maintaining data confidentiality is one of the most crucial requirements for data services, many cryptosystems have been proposed to reserve information privacy against such an untrusted environment. However, in cloud-fog computing, how to confidentially and efficiently share data and fetch desirable data without expensive data decryption for resource-constrained end-devices is challenging. In this paper, we propose a cloud-fog system for the Internet-of-Things (IoT) ecosystem by introducing a cryptographic primitive called server-aided revocable bilateral attribute-based encryption (SRB-ABE). Our solution is a secure and lightweight bilateral access control system with dynamic user groups, including (1) fine-grained data user and data owner access control simultaneously; (2) outsourced data source identification; (3) server-aided user revocation with publicly updatable ciphertexts; and (4) lightweight data decryption mechanism with one exponentiation computation. We present the formal definition and concrete construction of SRB-ABE with security proofs to build cloud-fog systems. The extensive comparison and experimental analysis demonstrate that our construction has superior functionality and comparable performance than the most relevant solutions.
Intelligent real-time Arabic sign language classification using attention-based inception and BiLSTM

Bio-inspired deep learning models have revolutionized sign language classification, achieving extraordinary accuracy and human-like video understanding. Recognition and classification of sign language videos in real-time are challenging because the duration and speed of each sign vary for different subjects, the background of videos is dynamic in most cases, and the classification result should be produced in real-time. This study proposes a model based on a convolution neural network (CNN) Inception model with an attention mechanism for extracting spatial features and Bi-LSTM (long short-term memory) for temporal feature extraction. The proposed model is tested on datasets with highly variable characteristics such as different clothing, variable lighting, and variable distance from the camera. Real-time classification achieves significant early detections while achieving performance comparable to the offline operation. The proposed model has fewer parameters, fewer deep learning layers, and requires significantly less processing time than state-of-the-art models.
Influence of design data availability on the accuracy of physical photovoltaic power forecasts

Accurate photovoltaic (PV) power forecasts are essential for the grid integration of the technology. Most solar forecasting studies deal only with solar irradiance forecasting, which highlights the importance of the irradiance to power conversion methods to enable the practical utilization of the advanced irradiance forecasting techniques. The PV power output is commonly calculated with physical model chains that rely on the main design parameters of the PV plants. This study presents an analysis of how the unavailability or uncertain knowledge of the design parameters affects the physical power forecast accuracy. The analysis is performed by verifying the power forecasts created by the bests of 32,400 model chains for 16 PV plants in Hungary for five data availability scenarios based on numerical weather prediction data. The results show that even though the knowledge of all relevant design parameters ensures the lowers errors, the critical parameters are only the nameplate capacities and the module orientation. The increase in mean absolute error and root mean square error is only 1.3% and 0.5%, respectively, if only these crucial parameters are known compared to the baseline scenario. If necessary, the tilt and azimuth angles can also be inferred, where the resulting inaccuracy depends on the estimation errors. The results reveal that the physical model chains can be used with a decent accuracy for power forecast calculations even if the design parameters are unknown, which highly broadens the applicability of the physical method.
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

In this paper, we propose a novel framework to translate a portrait photo-face into an anime appearance. Different from existing translation methods which do not designate specific styles, we aim to synthesize anime-faces which are style-consistent with a given reference anime-face. However, unlike typical translation tasks, such anime-face translation is particularly challenging due to the large and complex variations of appearances among anime-faces. Existing methods often fail to transfer the styles of reference anime-faces to the generated anime-faces, or introduce noticeable artifacts/distortions in the local shapes of their generated anime-faces. We propose a novel GAN-based anime-face translator, called AniGAN, to synthesize high-quality anime-faces. Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photo-face. New normalization functions are designed for the generator to further improve local shape transformation and color/texture style transfer. Besides, we propose a double-branch discriminator to learn domain-specific distributions through individual branches and learn cross-domain shared distributions via shared layers, helping generate visually pleasing anime-faces and effectively mitigate artifacts/distortions. Extensive experiments on benchmark datasets qualitatively and quantitatively demonstrate the superiority of our method over state-of-the-art methods.
A Survey of Graph Neural Networks for Electronic Design Automation

Driven by Moore’s law, the chip design complexity is steadily increasing. Electronic Design Automation (EDA) has been able to cope with the challenging very large-scale integration process, assuring scalability, reliability, and proper time-to-market. However, EDA approaches are time and resource-demanding, and they often do not guarantee optimal solutions. To alleviate these, Machine Learning (ML) has been incorporated into many stages of the design flow, such as in placement and routing. Many solutions employ Euclidean data and ML techniques without considering that many EDA objects are represented naturally as graphs. The trending Graph Neural Networks are an opportunity to solve EDA problems directly using graph structures for circuits, intermediate RTLs, and netlists. In this paper, we present a comprehensive review of the existing works linking the EDA flow for chip design and Graph Neural Networks.
Design Ontology Supporting Model-Based Systems Engineering Formalisms

Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development.MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, development, testing, fielding, etc.However, the modeling languages and techniques are heterogeneous, even within the same enterprise system, which leads to difficulties for data interoperability.The discrepancies among data structures and language syntaxes make information exchange among MBSE models more difficult, resulting in considerable information deviations when connecting data flows across the enterprise.Therefore, this article presents an ontology based upon graphs, objects, points, properties, roles, and relationships with extensions (GOPPRRE), providing metamodels that support the various MBSE formalisms across lifecycle stages.In particular, knowledge graph models are developed to support unified model representations to further implement ontological data integration based on GOPPRRE throughout the entire lifecycle.The applicability of the MBSE formalism is verified using quantitative and qualitative approaches.Moreover, the GOPPRRE ontologies are used to create the MBSE formalisms in a domain-specific modeling tool, MetaGraph, for evaluating its availability.The results demonstrate that the proposed ontology supports the formal structures and descriptive logic of the systems engineering lifecycle.
A new secure arrangement for privacy-preserving data collection

A big number of users' healthy data are necessary for the Internet of Things (IoT) healthcare. Therefore, the institutions, which have access to more data can provide better medical services such as more accurate diagnosis. However, privacy is often a bottleneck for IoT healthcare. Users often refuse to provide their health data based on privacy considerations. To balance the requirement of data collection and personal privacy, a lot of privacy-preserving data collection schemes are provided. A very important work of these schemes is to produce a secret position for every user to store her/his data, which is named secure arrangement. A novel secure arrangement method is proposed in this paper, which is based on matrix eigenvalue calculation. Compared with the current secure arrangement methods, the proposed method is more robust and efficient, which drives the proposed scheme to be more suitable for repeated aggregation. Then we use an example to illustrate how to use the proposed arrangement method to construct a privacy data collection protocol. We prove the proposed scheme is secure and efficient in security analysis and efficiency analysis.
Herd behavior in information security decision-making

Current information security behavior research assumes that lone individuals make a rational, informed decision about security technologies based on careful consideration of personally available information. We challenge this assumption by examining how the herd behavior influences users' security decisions when coping with security threats. The results show that uncertainty about a security technology leads users to discount their own information and imitate others. We found that imitation tendency has a more substantial effect on security decisions than the personal perceived efficacy of the security technology. It is essential for researchers and managers to consider how the herd behavior effect influences users' security decisions.
Visual question answering: Which investigated applications?

Visual Question Answering (VQA) is an extremely stimulating and challenging research area where Computer Vision (CV) and Natural Language Processig (NLP) have recently met. In image captioning and video summarization, the semantic information is completely contained in still images or video dynamics, and it has only to be mined and expressed in a human-consistent way. Differently from this, in VQA semantic information in the same media must be compared with the semantics implied by a question expressed in natural language, doubling the artificial intelligence-related effort. Some recent surveys about VQA approaches have focused on methods underlying either the image-related processing or the verbal-related one, or on the way to consistently fuse the conveyed information. Possible applications are only suggested, and, in fact, most cited works rely on general-purpose datasets that are used to assess the building blocks of a VQA system. This paper rather considers the proposals that focus on real-world applications, possibly using as benchmarks suitable data bound to the application domain. The paper also reports about some recent challenges in VQA research.
Adversarial point cloud perturbations against 3D object detection in autonomous driving systems

Deep learning models have been demonstrated vulnerable to adversarial attacks even with imperceptible perturbations. As such, the reliability of existing deep neural networks-based autonomous driving systems can suffer. However, deep 3D models have applications in various Cyber-Physical Systems (CPSs) with safety-critical requirements, particularly autonomous driving systems. In this paper, the robustness of deep 3D object detection models under adversarial point cloud perturbations has been investigated. A novel method is developed to generate 3D adversarial examples from point cloud perturbations, which are common due to the intrinsic characteristics of the data captured by 3D sensors, e.g., LiDAR. The generation of adversarial samples is supervised by a dual loss, which constitutes an adversarial loss and a perturbation loss. The adversarial loss produces a point cloud with the property of aggressiveness, while the perturbation loss enforces the produced point cloud subject to visual imperception. We demonstrate that the method can successfully attack 3D object detection models in most cases, and expose their vulnerability to physical-world attacks in the form of point cloud perturbations. We perform a thorough evaluation of popular deep 3D object detectors in an adversarial setting on the KITTI vision benchmark. Experimental results show that current deep 3D object detection models are susceptible to adversarial attacks in the context of autonomous driving, and their performances are degraded by a large margin in the presence of adversarial point clouds generated by the proposed method.
Privacy-enhancing distributed protocol for data aggregation based on blockchain and homomorphic encryption

The recent increase in reported incidents of security breaches compromising users' privacy call into question the current centralized model in which third-parties collect and control massive amounts of personal data.Blockchain has demonstrated that trusted and auditable computing is possible using a decentralized network of peers accompanied by a public ledger.Furthermore, Homomorphic Encryption (HE) guarantees confidentiality not only on the computation but also on the transmission, and storage processes.The synergy between Blockchain and HE is rapidly increasing in the computing environment.This research proposes a privacy-enhancing distributed and secure protocol for data aggregation backboned by Blockchain and HE technologies.Blockchain acts as a distributed ledger which facilitates efficient data aggregation through a Smart Contract.On the top, HE will be used for data encryption allowing private aggregation operations.The theoretical description, potential applications, a suggested implementation and a performance analysis are presented to validate the proposed solution.
Construction of Efficient Smart Voting Machine with Liveness Detection Module

Voting is now governed by regulations that specify how a person's choices may be communicated and their desires can be realized. This study proposes an electronic voting machine (EVM) as an alternative for traditional voting methods, which may include the manual utilization of only microcontroller-based circuits. With the identified fingerprint liveness, the proposed technique will make voting considerably easier, more effective, and less likely to result in fraud. The suggested model will support and advance the trustworthiness of all votes and it will also assist in streamlining the counting and verification process. It is difficult to demonstrate that an advanced voting system has been properly designed since several critical criteria must be satisfied. Poll results should be kept private in the database in order to preserve the data. The voting process must also show the votes obtained by the respective candidates. The proposed authenticated voting machine can be applied to the local area elections in order to speed up the process and make the election process more transparent. To maintain its theoretical strength, the proposed research idea needs further study. The model employs radio frequency and fingerprint recognition to maintain the protection.
Generating visually secure encrypted images by partial block pairing-substitution and semi-tensor product compressed sensing

Recently visually secure image encryption scheme attracts more and more researchers' attention on the grounds that it will not stimulate the attackers' curiosity. Generally, four aspects should be paid great attention in visually meaningful encryption schemes, including the similarity between the cipher image and carrier image, the large embedding capacity which can embed the secret image, the satisfactory recovered quality and the robustness to resist various attacks. To improve the visual quality of the meaningful cipher image and the embedding capacity, this paper proposes a novel visually secure image encryption scheme by combining semi-tensor product compressed sensing and partial block pairing-substitution technique. The blocks of two images which have similar Standard Deviation values are selected to execute the block pairing-substitution. This makes the cipher image more similar to the carrier image, and the secret is more difficult to detect. The metric MSSIM values are all exceeding 0.91, and a close value for two-dimensional detrended fluctuation (2D-DFA), which verifies the high similarity between the cipher image and the carrier image to a greater extent. The embedding capacity can reach four times of the plain image in theory, when the compression ratio is 0.25, which demonstrates the large embedding capacity of the proposed scheme. Furthermore, less computational and spatial resources are required since the incorporation of semi-tensor product compressed sensing and cellular automata. Importantly, the embedding capacity increases as the embedding is performed on the spatial domain. Both the simulation results and theoretical analysis demonstrate the high quality of the cipher images and the high security of the proposed scheme.
Sentiment Analysis for Product Reviews Based on Weakly-Supervised Deep Embedding

Buyers to whom a product would be introduced should check it to make better choices about the item. To arrive at a specific finding, various viewpoint mining methods have been suggested. Several recent developments in machine learning, especially deep learning, have led to considerable progress in solving sentiment classification problems. To achieve valuable scores as poor supervision indicators, this research work suggests an innovative deep learning system for performing product review based emotion classification. To achieve a high-level representation, one needs to learn the embedding before applying a classification layer on top of the embedding.