Reviewerly/exHarmony huggingface dataset:
Design of the Experiment Teaching Model of Multi-mode Integrated Hardware Development with Arduino

In terms of the hardware development training of applied talents in the field of Internet of Things, due to backward teaching methods and diverse motherboards and chips in experiment teaching, the experiment process is chaotic and practical development costs much time and energy.By introducing the experiment teaching architecture of multi-mode integrated hardware development with Arduino, the model of the common hardware development system is established, thusconstructing innovative heterogeneous hardware environment integrated teaching model and mixed embedded hardware development programming teaching model, integrating experimental development teaching motherboards and chips.This paper also puts forward the experiment teaching steps of multi-mode integrated hardware development with Arduino, thus providing useful reference for the hardware experiment teaching of such majors as Internet of things.
Augmented Performative Design: A Workflow Utilizing Augmented Reality for Structurally Stable Masonry Design

This paper presents an experimental performative design workflow for masonry structures, incorporating augmented reality (AR) technology and real-time stability simulation. We aim to resolve the lack of structural stability simulation in currently existing immersive design workflows. Our workflow consists of two phases: a) an AR design phase, in which interactive inputs, such as gestures, screen-based inputs, or marker recognitions, are translated to a design algorithm for the masonry structure generation and preview as holograms; b) an AR-assisted structural stability simulation phase, in which the designed structure is simulated on its structural performance attributes (without mortar or other adhesives) and shown as an AR holographic result animation on-site with the structurally optimised feedback. Our research findings highlight the advantages of the proposed workflow, which allows users modification suggestions and design implementation control for the assembly of structurally stable masonry structures, as well as the challenges arising regarding design diversity.
ATTRIBUTE-BASED PROXY RE-ENCRYPTION WITH KEYWORD SEARCH ON PERSONAL HEALTH RECORD

Personal health records (PHRs) contain sensitive health information that needs to be protected from unauthorized access. At the same time, patients may need to share their PHRs with healthcare providers or researchers to receive appropriate care or participate in medical studies. Attribute-based proxy re-encryption with keyword search (ABPRE-KS) is a what cryptographic technique that enables secure and controlled sharing of encrypted PHR data while preserving privacy and confidentiality of sensitive patient information. ABPRE-KS works by encryption of the PHR data using a key derived from the patient's attributes and allowing the patient to delegate access to the data to third parties using a proxy re-encryption scheme. To enable keyword search on the encrypted PHR data, an additional searchable encryption scheme can be used. ABPRE-KS provides a powerful tool for securely sharing and searching PHR and it can be used to facilitate better healthcare outcomes and medical research while protecting patient privacy.
Coupling Co-presence in Physical and Virtual Environments Toward Hybrid Places

Recent advancements in information and communication technologies (ICT) including Internet-of-Things (IoT), Extended Reality (XR), and Digital Twins (DT) enabled a massive leap toward the coupling of physical and virtual systems for working, living, learning, and playing. Current approaches mostly focus on augmenting physical environments with digital information to improve the design and operations of buildings and cities. Fewer approaches explored a deeper integration of physical and virtual environments to create places for meaningful social interactions across realities. In this paper, we discuss the concept of ‘hybrid places’ that couple elements of physical and virtual places to unlock new opportunities for situated social interactions. Specifically, we conceptualize a framework for hybrid places where a virtually-augmented physical place (the hybrid-physical) and a physically-augmented virtual place (the hybrid-virtual) dynamically interact to enable co-presence across realities. Following, we demonstrate a preliminary workflow where the presence of people in a physical place is detected through occupancy sensors and displayed in the coupled virtual place through VR technology; the presence of people in the virtual place is then displayed in the coupled physical place through AR technology. This paper aims at revisiting the concept of ‘place’ considering recent technology developments, and laying the foundation for future work that will explore the benefits and opportunities for hybrid places to promote social interactions and collaboration across physical and virtual realms.
Design of Intelligent Cloud Computing Platform for English Writing Assisted Teaching System

In order to solve the problems caused by the heavy load of the traditional embedded software development teaching platform server, such as slow response speed and poor experience of users, an intelligent English writing auxiliary teaching system based on the cloud service platform was proposed [1].Based on the cloud platform, the system manages the computer resources scattered in the server and the client in a unified way, so as to improve the efficiency of resource retrieval.In addition, based on the fuzzy logic model, the system can evaluate and evaluate students' knowledge and learning outcomes according to their cognitive and knowledge acquisition preferences [2].The real analysis results show that the proposed system has good performance and can play a positive role in improving students' performance.Simulation results further verify the effectiveness of the proposed method [3].
Analysing supervised learning approaches for detecting shilling attacks in collaborative recommendations

Collaborative recommendation systems offer users personalized recommendations based on their past interactions and the actions of other users. However, these systems can be compromised by shilling attacks, in which fake feedback and ratings are introduced in order to manipulate the recommendations made by the system. It is important to identify and mitigate these attacks to maintain the reliability and accuracy of the recommendations. There are different ways to deal with shilling attacks, which involve using technology to detect fake ratings and assess the trustworthiness of users. Some solutions include using machine learning to spot patterns, applying filters to weed out fake ratings, using a combination of different filtering techniques to make recommendations, and establishing reputation systems to evaluate the reliability of users and their ratings. This work provides a comprehensive overview of the current methods for detecting shilling attacks in collaborative recommendation systems, including different types of attacks and various detection approaches. It also discusses the limitations and challenges of these approaches and compares their performance.
Prioritized Energy Efficient Resource Scheduling in Cloud Computing

Resource scheduling in cloud computing is one of the impactful area of research. Cloud service providers maintain its efficacy through proper resource management schemes. Users experience seamless cloud services when cloud service provider manages its resources efficiently. Another aspect in resource management is energy efficient schemes. Energy efficiency largely depends on employment of minimum number of resource servers. This article discusses energy efficient resource scheduling mechanism on multi-layer prioritized resource requests. Resource requests are segregated into three categories and those are scheduled on resource servers based on their fast availability. The work focuses on scheduling resource requests on previously active servers as much as possible before activating any idle servers. Proposed scheduling scheme has been tested on a standard benchmark datasets and performance of the proposed method establishes its efficacy.*
Artwork restoration using paired image translation-based generative adversarial networks

Preservation of the artworks has historical and cultural importance. However, with time, environmental factors severely affect artworks, and these damages are often complicated to repair manually and through traditional methods. We propose a method to restore artwork that has been damaged over time. This work proposes a systematic approach using paired image-to-image translation based on a generative adversarial network. The experimental results have been quantitatively evaluated. The experimental results obtained from the presented work visually prove that the presented approach of artwork restoration completely restores the damaged artwork.
Ethereum Blockchain using AES-CMAC

Managing and storing data are crucial tasks for any industry, since they require accurate and secure record-keeping. Using blockchain technology has significantly increased recently due to its capability to address some of the challenges that come with traditional data storage and management systems. In this paper, we will explore the use of Ethereum smart contracts powered by the Solidity programming language using AES-CMAC.
FPGA Implementation of Image Watermarking Process using Xilinx System Generator

Watermarking is a tool of confidentiality that protects valuable information hidden in the set of digital media by modifying the particular graphic contents. Special purpose hardware designed systems can be mapped in watermarking through FPGA. In this paper, the process of watermarking is performed on image using Xilinx system generator. Watermarking is performed in DWT domain using MATLAB after scaling of watermark data in Simulink environment. The co-hardware simulation has been performed for developed model using VIRTEX 6, ML605 BOARD to see the capability of watermarking system. The Simulink and system Generator through FPGA model extracts the Watermark appropriately and thereafter device utility for embedding system is determined.
Overview of GAN Structure

This chapter will introduce generative adversarial networks (GANs), a type of neural network that can generate new data samples that resemble a given dataset. The chapter will discuss the difference between generative and discriminative models, an overview of the basic architecture of GANs, which consists of a generator and a discriminator, and how they are trained using an adversarial process. We also discuss the weaknesses of GANs and highlight some of their current challenges and limitations.
CsdRec: Accuracy and Diversity-Awared Team Recommendation for Collaborative Software Development

Collaborative Software Development (CSD) as an effective software development paradigm has achieved rapid development. In CSD, one critical and challenging problem is to find a group of compatible and diverse developers who can complete the assigned task as quickly as possible. To solve this problem, most previous approaches focus on measuring the relevance of tasks and developers based on their historical interactions. However, such approaches usually suffer from data sparsity and cold start problem which may reduce the recommendation accuracy. Moreover, previous approaches often ignored the diversity of team members which may reduce the overall collaboration efficiency. In this paper, we propose an attentive text-enhanced graph embedding learning framework, termed as CsdRec. We first incorporate text embedding into one-hop interaction learning and two-hop graph learning to learn the relevance between tasks and team members. Then, we explicitly model the team members’ diversity and incorporate it with relevance. Finally, to evaluate the performance of CsdRec, we conduct extensive experiments on two real-world datasets. The experimental results show that CsdRec outperforms other state-of-the-art approaches significantly.
TrustScapes: A Visualisation Tool to Capture Stakeholders’ Concerns and Recommendations About Data Protection, Algorithmic Bias, and Online Safety

This paper presents a new methodological approach, TrustScapes, an open access tool designed to identify and visualise stakeholders’ concerns and policy recommendations on data protection, algorithmic bias, and online safety for a fairer and more trustworthy online world. We first describe how the tool was co-created with young people and other stakeholders through a series of workshops. We then present two sets of TrustScapes focus groups to illustrate how the tool can be used, and the data analysed. The paper then provides the methodological insights, including the strengths of the TrustScapes and the lessons for future research using TrustScapes. A key strength of this method is that it allows people to visualise their ideas and thoughts on the worksheet, using the keywords and sketches provided. The flexibility in the mode of delivery is another strength of the TrustScapes method. The TrustScapes focus groups can be conducted in a relatively short time (1.5–2 hours), either in person or online depending on the participants’ needs, geological locations, and practicality. Our experience with the TrustScapes offers some lessons (related to the data collection and analysis) for researchers who wish to use this method in the future. Finally, we describe how the outcomes from the TrustScapes focus groups should help to inform future policy decisions.
Seafloor-Invariant Caustics Removal From Underwater Imagery

Mapping the seafloor with underwater imaging cameras is of significant importance for various applications including marine engineering, geology, geomorphology, archaeology, and biology. For shallow waters, among the underwater imaging challenges, caustics, i.e., the complex physical phenomena resulting from the projection of light rays being refracted by the wavy surface, is likely the most crucial one. Caustics is the main factor during underwater imaging campaigns that massively degrades image quality and affects severely any 2-D mosaicking or 3-D reconstruction of the seabed. In this article, we propose a novel method for correcting the radiometric effects of caustics on shallow underwater imagery. Contrary to the state-of-the-art, the developed method can handle the seabed and riverbed of any anaglyph, correcting the images using real pixel information, thus, improving image matching and 3-D reconstruction processes. In particular, the developed method employs deep learning architectures to classify image pixels to “noncaustics” and “caustics.” Then, it exploits the 3-D geometry of the scene to achieve a pixelwise correction, by transferring appropriate color values between the overlapping underwater images. Moreover, to fill the current gap, we have collected, annotated, and structured a real-world caustic data set, namely, R-CAUSTIC, which is openly available. Overall, based on the experimental results and validation, the developed methodology is quite promising in both detecting caustics and reconstructing their intensity.
Poseidon-NDP: Practical Fully Homomorphic Encryption Accelerator Based on Near Data Processing Architecture

With the development of the important solution for privacy computing -Fully Homomorphic Encryption (FHE), the explosion of data size and computing intensity in FHE applications brings enormous challenges to the hardware design. In this paper, we propose a novel co-design scheme for FHE acceleration named “Poseidon-NDP”, which focuses on improving the efficiency of the hardware resource and the bandwidth. Specifically, we investigate the special implications of the hardware imposed by the FHE applications. It empirically shows that the FHE performance is suffered from both the intractable data movement and the computation bottleneck. Besides, we also introduce the opportunity and the challenges of accelerating FHE on Near Data Processing (NDP) architecture. Based on such analysis, we propose an optimized technique called “NTT-fusion” to simplify the FHE operator and reduce its hardware overhead. Then, we design the accelerator based on the simplified operator to achieve maximized data and computation parallelism with limited hardware resources. Additionally, we evaluate Poseidon-NDP with 4 domain-specific FHE applications on the SmartSSD, which is a practical NDP device. The empirical studies show that the efficient co-design enables Poseidon-NDP vastly superior to the state-of-the-art FHE acceleration techniques: (1) up to 217×/84× speedup over CPU and high-performance GPUs for the NTT; (2) up to 3.7×/29× higher speedup/Energy Delay Product (EDP) over the SOTA FPGA accelerator for the FHE applications; (3) up to 4.9× higher bandwidth utilization over CPU due to the NDP-based architecture.
Slimmer CNNs Through Feature Approximation and Kernel Size Reduction

Convolutional Neural Networks (CNNs) have been shown to achieve state of the art results on several image processing tasks such as classification, localization, and segmentation. Convolutional and fully connected layers form the building blocks of these networks. The convolution layers are responsible for the majority of the computations even though they have fewer parameters. As inference is used much more than training (which happens only once), it is important to reduce the computations of the network for this phase. This work presents a systematic procedure to trim CNNs by identifying the least important features in the convolution layers and replacing them either with approximations or kernels of reduced size. We also propose an algorithm to integrate the lower kernel approximation technique for a given accuracy budget. We show that using the linear approximation method can achieve a 15 – 80% savings with a median of 52% reduction while the lower kernel method can achieve 33 – 95% reduction with a median of 65% in the required number of computations with only a marginal 1% loss in accuracy across several benchmark datasets. We have also demonstrated the proposed methods on VGG-16 architecture for various datasets. On VGG-16 we have achieved 4.2 -45% savings in MAC computations (with a median of 18.5%) with only a marginal 0.5% loss in accuracy. We also show how an existing hardware accelerator for DNNs (DianNao) can be modified with low added complexity to take advantage of the kernel approximations, and estimate the speedups that can be obtained in such a way on custom embedded hardware.
Combining Deep Image Prior and Second-Order Generalized Total Variance for Image Denoising

Deep image prior is a classical unsupervised deep learning method that does not require plenty of training samples, because in some practical applications, like medical imaging, collecting tons of training samples is not always viable. At the same time, it is hard and time-consuming to construct a favorable training set, since it requires that the selected data must be sufficient and typical. In addition, due to the overfitting problem of the deep image prior method, it can lead to the loss of image texture details and the destruction of edge information. And the second-order total generalized variation can effectively protect the image edge information. Therefore, in this work, an image denoising method that combines a deep image prior with the second-order total generalized variation is proposed to remove noise, preserve the image edge and texture structuration a better way. To solve the new model efficiently, first, we transform the constrained problem into an unconstrained problem using the augmented Lagrangian method, and then solve the augmented Lagrangian function using the alternating direction method of multiplication. Experimental results show that the proposed method can removes noise more effectively, retain more image details, and obtain higher performance in terms of peak signal-to-noise ratio and structural similarity, and outperform other existing methods.
Federated Active Semi-Supervised Learning With Communication Efficiency

Federated learning (FL) unites multiple participants to collaboratively learn a global consensus model on the centralized server by aggregating their individual models trained locally on clients. To meet the goal of obtaining an optimal model, sufficient labeled data and myriad communications are required during training. However, the major problems are the limited budget for manually annotating unlabeled instances and the restricted bandwidth of server and clients. This article presents a communication-efficient federated active semi-supervised learning (CEFedASSL) framework that unites active learning (AL) clients and a semi-supervised learning (SSL) client to train models on unlabeled data while achieving communication efficiency. In each AL client, different query strategies are, respectively, applied for the local model to obtain a more robust model and query only the optimal samples which significantly reduces the cost of annotation. Subsequently, these optimal samples are encrypted as input to fine-tune the pretrained model of the SSL client by performing self-training, thereby enhancing the model performance while preserving the privacy of data. Furthermore, we propose an efficient selective aggregation strategy to reduce the communication cost between clients and the server. Empirical experiments on four different learning tasks demonstrate that the proposed CEFedASSL distinctively outperforms the common FL algorithms in terms of both model performance and communication costs.
Impediments during the compilation of a search and seizure warrant for digital information by forensic investigators in South Africa

Purpose The purpose of this paper is to identify and discuss impediments in the compilation of an application for a search and seizure warrant for digital evidence and the structure of such a warrant in South African criminal cases. Design/methodology/approach This paper provides a brief overview of international and local impediments, followed by a detailed discussion of the implications of these impediments and how it is approached in various jurisdictions. The methodology of this paper consists of a literature review. Findings Addressing the impediments in the compilation of the application and the warrant will be beneficial for forensic investigators, the South African Police Service (SAPS) and the administration of justice in South Africa. Research limitations/implications Search and seizures for digital evidence form part of civil, regulatory and criminal search and seizures. This study focuses on the search and seizure of digital evidence in criminal matters pursuant to mainly the provisions of the Criminal Procedure Act 51 of 1977 and the Cybercrimes Act 19 of 2020. Originality/value The originality of this paper lies in the approach to the drafting of applications for search and seizure warrants for digital information in South Africa. The contribution of the study is that, by using this approach, the SAPS can address the impediments during the application and compilation of the warrants, which would enhance the quality of investigations and contribute to the successful investigation and prosecution of crime in South Africa.