Reviewerly/exHarmony huggingface dataset:
Tensor Algebra and its Applications to Data Science and Statistics

This survey provides an overview of common applications, both implicit and explicit, of "tensors" and "tensor products" in the fields of data science and statistics. One goal is to reconcile seemingly distinct usages of the term "tensor" in the literature, and to explain how these usages are manifestations of a common concept. Not all relevant topics are discussed in detail, but the attempt is made to briefly describe and give references for some of the most important topics not included in the main survey. Particular attention is given to tensor decompositions.
Latent Space is Feature Space: Regularization Term for GANs Training on Limited Dataset

Generative Adversarial Networks (GAN) is currently widely used as an unsupervised image generation method. Current state-of-the-art GANs can generate photorealistic images with high resolution. However, a large amount of data is required, or the model would prone to generate images with similar patterns (mode collapse) and bad quality. I proposed an additional structure and loss function for GANs called LFM, trained to maximize the feature diversity between the different dimensions of the latent space to avoid mode collapse without affecting the image quality. Orthogonal latent vector pairs are created, and feature vector pairs extracted by discriminator are examined by dot product, with which discriminator and generator are in a novel adversarial relationship. In experiments, this system has been built upon DCGAN and proved to have improvement on Frechet Inception Distance (FID) training from scratch on CelebA Dataset. This system requires mild extra performance and can work with data augmentation methods. The code is available on github.com/penway/LFM.
Local Model Reconstruction Attacks in Federated Learning and their Uses

In this paper, we initiate the study of local model reconstruction attacks for federated learning, where a honest-but-curious adversary eavesdrops the messages exchanged between a targeted client and the server, and then reconstructs the local/personalized model of the victim. The local model reconstruction attack allows the adversary to trigger other classical attacks in a more effective way, since the local model only depends on the client's data and can leak more private information than the global model learned by the server. Additionally, we propose a novel model-based attribute inference attack in federated learning leveraging the local model reconstruction attack. We provide an analytical lower-bound for this attribute inference attack. Empirical results using real world datasets confirm that our local reconstruction attack works well for both regression and classification tasks. Moreover, we benchmark our novel attribute inference attack against the state-of-the-art attacks in federated learning. Our attack results in higher reconstruction accuracy especially when the clients' datasets are heterogeneous. Our work provides a new angle for designing powerful and explainable attacks to effectively quantify the privacy risk in FL.
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks

To understand the training dynamics of neural networks (NNs), prior studies have considered the infinite-width mean-field (MF) limit of two-layer NN, establishing theoretical guarantees of its convergence under gradient flow training as well as its approximation and generalization capabilities. In this work, we study the infinite-width limit of a type of three-layer NN model whose first layer is random and fixed. To define the limiting model rigorously, we generalize the MF theory of two-layer NNs by treating the neurons as belonging to functional spaces. Then, by writing the MF training dynamics as a kernel gradient flow with a time-varying kernel that remains positive-definite, we prove that its training loss in $L_2$ regression decays to zero at a linear rate. Furthermore, we define function spaces that include the solutions obtainable through the MF training dynamics and prove Rademacher complexity bounds for these spaces. Our theory accommodates different scaling choices of the model, resulting in two regimes of the MF limit that demonstrate distinctive behaviors while both exhibiting feature learning.
IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels

Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different scanners, and the variety of image properties they produce. Moreover, access to large-scale datasets with annotated regions of interest is scarce, and obtaining good results is thus difficult. To overcome these challenges, we introduce IB-U-Nets, a novel architecture with inductive bias, inspired by the visual processing in vertebrates. With the 3D U-Net as the base, we add two 3D residual components to the second encoder blocks. They provide an inductive bias, helping U-Nets to segment anatomical structures from 3D images with increased robustness and accuracy. We compared IB-U-Nets with state-of-the-art 3D U-Nets on multiple modalities and organs, such as the prostate and spleen, using the same training and testing pipeline, including data processing, augmentation and cross-validation. Our results demonstrate the superior robustness and accuracy of IB-U-Nets, especially on small datasets, as is typically the case in medical-image analysis. IB-U-Nets source code and models are publicly available.
Thermal Infrared Image Inpainting via Edge-Aware Guidance

Image inpainting has achieved fundamental advances with deep learning. However, almost all existing inpainting methods aim to process natural images, while few target Thermal Infrared (TIR) images, which have widespread applications. When applied to TIR images, conventional inpainting methods usually generate distorted or blurry content. In this paper, we propose a novel task -- Thermal Infrared Image Inpainting, which aims to reconstruct missing regions of TIR images. Crucially, we propose a novel deep-learning-based model TIR-Fill. We adopt the edge generator to complete the canny edges of broken TIR images. The completed edges are projected to the normalization weights and biases to enhance edge awareness of the model. In addition, a refinement network based on gated convolution is employed to improve TIR image consistency. The experiments demonstrate that our method outperforms state-of-the-art image inpainting approaches on FLIR thermal dataset.
The Display Design and Implementation of Augmented Reality based on play-based learning model in museum contexts

In an era of rapid advances in information technology, augmented reality is becoming increasingly evident as a new technological medium for creating interesting learning experiences for the children's education sector.It uses computer-generated 3D information to embed virtual objects into the physical environment, providing children with an interactive, immersive, virtual-real environment, and it is one of the powerful tools for arousing children's interest in learning and enhancing their cognitive abilities.In this paper, we develop a museum learning service based on a PBL (play-based learning) model that combines AR technology, children's games and museum learning.The practice was eventually implemented in the Chinese Geological Museum.
The Evolution of the Manosphere Across the Web

In this paper, we present a large-scale characterization of the Manosphere, a conglomerate of Web-based misogynist movements roughly focused on "men's issues," which has seen significant growth over the past years. We do so by gathering and analyzing 28.8M posts from 6 forums and 51 subreddits. Overall, we paint a comprehensive picture of the evolution of the Manosphere on the Web, showing the links between its different communities over the years. We find that milder and older communities, such as Pick Up Artists and Men's Rights Activists, are giving way to more extremist ones like Incels and Men Going Their Own Way, with a substantial migration of active users. Moreover, our analysis suggests that these newer communities are more toxic and misogynistic than the former.
Cyber Crimes and Cyber Hygiene Practices Among Young Girls

The world evidences an immense transformation from pen &amp; paper culture to digital culture, particularly, post covid pandemic. We live in the digitally complex generation. The evolution of digitalization and explosive usage of internet have lead to cyber crimes (e-crimes) and cyber violations. With the increase of ‘Work from Home’ option, women are more prone to serious consequences of cyber attacks and lead to traumatic experiences. The Government of India has started attempting to renovate our nation into digitally enabled and empowered, through its “The Digital India” project. But, is the digitalization completely favourable for our people? The primary aim of the study is to answer this question by creating awareness on cyber crimes and security measures among women and young girls, whom the law looks down and does not recognize properly, even in today’s developed society. An attempt is made to keep them away from any sort harm by cyber usage or exploitations. In the direction to caution women about cyber threats by not hindering digitalization and innovation, the authors have analyzed previous research studies conducted with respect to cyber threats and security space, major cyber crime cases in India, Cyber laws, agencies that deal with cyber security and key initiatives implemented by our Indian government to protect innocent people from cyber threats. Hence, at the end of this study, protective measures and cyber hygiene practices are suggested to make women and young girls become more cautious and reorient their behaviour to make safest use of internet and protect themselves against cyber attacks.
Ethereum Application for Product Anti-Counterfeiting

The project's goal is to create an application that assures consumers don't have to rely solely on merchants to determine whether or not products are authentic. It also assures that producer may use this system to supply genuine products without having to run direct operated outlets, lowering product quality assurance costs dramatically. The trade in counterfeit goods is expanding, hurting the sales and earnings of businesses who are affected. This project proposes a fully-functional blockchain system to prevent product counterfeiting to ensure the identification and traceability of real products throughout the supply chain. Establishments only need to pay minimal transaction fees, and they no longer need to be concerned about the probability of obtaining counterfeit products. This project utilizes Ethereum blockchain and Remix to develop the application code in the form of a Smart Contract. For testing and implementation, Ganache and MetaMask are used, which simulates the blockchain environment and user account respectively. The Client-Side application leverages with Reactjs and Web3js to interact with smart contract and provide its functionalities to the users, maintaining the integrity of the system. Key Words: Ethereum, Anti-Counterfeit, Solidity, Web3js, Reactjs, Ganache
Challenges of Reprographic Information Resources within the Library and Some Selected Private Business Centers in Three Universities in Ogun State, Nigeria

This study investigates challenges of reprographic information resources within the library and some selected private business centers in three universities in Ogun State, Nigeria. The research design adopted for this study was descriptive survey method, with the used questionnaire as instrument to collect data, purposive sampling was used to select forty five (45) reprographic centers from the three selected institutions namely FUNAAB, OOU, TASUED. Forty two of the instruments were useful for the study. Data collected was analyzed using simple percentage. The result of the findings revealed that the types of reprographic services mostly available in the institutions are photocopy, printing, spiral binding. The extent of use of reprographic services by students both in business centers and libraries, the result revealed photocopy, printing, and scanning. This finding further revealed that the profit margin of daily income is between #1000 - #5000 naira. The major challenges faced in the business which are unstable power supply, inadequate reprographic equipment. The finding recommends that the library management and the business owners should improve in providing more relevant services for better patronage. The study suggests that good customer relationship will help the business to grow with time which will help generate more profit. The library management should provide a good budget that will accommodate the purchase of reprographic equipment’s while the business centers can approach bodies that can assist them for loans as far as they are sure of the environment and have a good business plan.
The Role of Investment on Economic Growth; An Empirical Case of Iraq For the Period of 2004 to 2020

This paper employs time series techniques to analyse the effect of foreign direct investment and national investment on economic growth in Iraq. The study uses secondary annual data over the period 2004-2020. The gross domestic product )GDP( is the dependent variable, and foreign direct investment )FDI( and national Investment )NI( are the explanatory variables. The empirical analysis starts with run ordinary least square )OLS(. The result of Augmented Dickey Fuller )ADF( and Phil- lips Perron )PP( test illustrates that the series are non-stationary in the level form, however station- ary in the first difference. The study further utilises the Johansen cointegration test whereby it finds the variables are cointegrated and there is a long run relationship between dependent and indepen- dent variables. The results demonstrates that foreign direct investment has statistically significant positive impact on gross domestic product by almost 0.1013, meanwhile, national investment has positive impact on Iraqi gross domestic product with 0.0555.
Students’ perceptions of blended and remote learning and its impact upon sense of belonging

Blended learning has been widely recognised for its ‘transformative potential’ (Garrison and Kanuka, 2004) in higher education, especially when it comes to its versatility and increased opportunities for distributed learners. Nonetheless, the technological challenges it poses, alongside issues linked with policy, resources and support structures, have led to considerable resistance to the concept of blended practice. Despite this mixed reputation (Antunes, Armellini and Howe, 2021; Lomer and Palmer, 2021), few studies have attempted to explore students’ perceptions of blended delivery, with most research focussing on staff experience (Torrisi-Steele and Drew, 2013). The session reports on a small-scale evaluative study on student perceptions of blended learning that we conducted in the academic year 2020/21. A critical realist framework that considers both agency and structure has been applied to situate these perceptions while our mixed methods approach offers a multi-layered insight into the captured diversity of experience. The aim of the conference session was to discuss with the participants the implications of the findings for future practice and, more specifically, consider the role of Learning Development in enhancing post-pandemic student experience.
Efficient Human Face Recognition in Real-Life Applications using the Discrete Wavelet Transformation (HFRDWT)

&lt;p&gt; Human Face receives major attention and acquires most of the efforts of the researches and studies of Machine Learning (ML) in detection and recognition. In real-life applications, the problem of quick and rapid recognition of the Human Face is always challenging the researchers to come out with powerful and reliable techniques. In this paper, we proposed a new human face recognition system using the Discrete Wavelet Transformation (DWT) named (HFRDWT). The proposed system showed that the use of Wavelet Transformation along with the Convolutional Neural Network (CNN) to represent the features of an image had significantly reduced the face recognition time, which makes it useful in real-life areas, especially in public and crowded places. The Approximation coefficient of the DWT played the dominant role in our system by reducing the raw image resolution to quarter while maintaining the high level of the accuracy rate that the raw image had. Results on ORL, Japanese Female Facial Expression (JAFFE), extended Cohn-Kanade (CK+), Labeled Faces in the Wild (LFW) datasets, and our new Sudanese Labeled Faces in the Wild (SuLFiW) dataset showed that our system obtained the least recognition timing and acceptable high recognition rate compared to the other systems. &lt;/p&gt;
Creation of Innovative Teaching Spaces with Gamma Tutor: A Techno-Blended Model for Rural Mathematics Teaching

Innovative techno-blended teaching methodologies are needed for 21st-century classrooms. This paper reports on the introduction of a techno-blended device for mathematics teaching in South African senior secondary classrooms. The research sample included 12 third-year mathematics education student teachers from a rural university. A qualitative case study design was employed. Participants were purposively selected. Data collection methods included non-participatory observation and in-depth individual interviews. The TPACK model and the Mathematical Knowledge for Teaching Framework guided the analysis of the data. The study found that the mathematics student teachers successfully implemented GammaTutor in their classrooms, thus demonstrating their proficiency and aptitude in utilising technology in the learning environment. The GammaTutor device also aided in teaching and learning mathematics by simplifying mathematical ideas for learners. Furthermore, it enabled the development of learner discourse as a crucial component for developing learners' problem-solving skills. Since the mathematics student teachers engaged the learners with a variety of mathematical exercises using the GammaTutor technology, the integration of GammaTutor in the mathematics classroom exhibited learner-centred provisioning. The study proposes a teaching model for creating innovative teaching spaces in rural schools in South Africa.
Utilizing Generative Adversarial Networks to develop a robust Defensive System against Adversarial Examples

Using the special ability of Generative Adversarial Networks (GANs) to create fresh adversarial instances for model retraining, we offer a novel defense strategy against adversarial examples in this study.In order to achieve this, we create an automated pipeline that combines a convolutional neural network that has already been trained with an external GAN called the Pix2Pix conditional GAN.This pipeline allows us to identify the transformations between adversarial examples and clean data as well as create new adversarial examples on the fly.In an iterative pipeline, these adversarial samples are used to strengthen the model, attack, and defense.Our simulation findings show that the suggested strategy works well.
EFEKTIVITAS DAN EFISIENSI PENGGUNAAN SISTEM E-OFFICE DALAM KEGIATAN KORESPONDENSI PADA PT PENGEMBANGAN PARIWISATA INDONESIA (PERSERO)

This study aims to determine the effectiveness and efficiency of using the e-office system in correspondence activities at PT Pengembangan Pariwisata Indonesia (Persero) The Nusa Dua Bali based on the characteristics of ISO/IEC 25010 : 2011 Quality in Use Model. The location of this research is at PT Indonesia Tourism Development (Persero)/ITDC The Nusa Dua. The number of samples used was 39 people using the census method. Collecting data using a 5 point likert scale questionnaire in the form of a google form. The data analysis technique used quantitative descriptive. The results showed the level of effectiveness of the use of the e-office system in correspondence activities at PT Pembangunan Wisata Indonesia (Persero) The Nusa Dua Bali with effective criteria. The efficiency level of using the e-office system in correspondence activities at PT Pembangunan Wisata Indonesia (Persero) The Nusa Dua Bali with efficient criteria. needs to be improved so that the implementation of e-office is more effective and efficient, namely the stability of the internet network to be maintained, the system to be updated and developed regularly, such as adding a digital signature feature, increasing file upload size, and being integrated directly with employee e-mails.
Demystifying Public Cloud Auditing for IT Auditors

Abstract The purpose of this chapter is to provide insight into the internal audit challenges that a large Dutch bank was confronted with as the consequence of the IT transformation that was focused on leveraging the opportunities offered in the public cloud. Our work contributes to the body of literature by providing a conceptual framework that can help institutions navigate their assurance requirements in the public cloud. Furthermore, we provide practical attention points from the field for IT auditors. Finally, discussion points are shared with respect to the IT auditing of the traditional on-premise and the public cloud environment.
Throughput Analysis and Data Sharing Technique to Assess the Impact of Blockchain Technology using Cryptographic Algorithm

Blockchain is a data structure made up of chronologically ordered data blocks. Research revealed that traditional blockchains have constrained throughput. It is ideal for blockchain technology to achieve higher throughput by increasing the block size and lessening the block interval. The methodology used in this paper is of two parts. The first approach uses the cryptographic protocol to ensure the authenticity and integrity of the information using an Ethereum blockchain type. This means that transactions are recorded in write-only mode and contributes to transaction trust and transparency. Our methodology automates smart and secure evaluations and provides credentials using an Improved Elliptic Curve Cryptography Algorithm (IECCA). It is implemented using encryption and decryption algorithms and is demonstrated using analytical and content neutrality. We also demonstrate operation from back-end to end-users application including student and faculty members. The Level of security performance of the proposed scheme is compared with another scheme for a 20MB file size. The proposed schemes achieve 83% IECCA, 78 percent DES, 76% RSA, and 70% AES respectively. The second approach identifies the boundary for conventional block sizes and block intervals. Security of blockchain using stale block rate is identified in the network by conducting a realistic experiment. This enables the realization of optimal block parameters like size and interval. Results presents achieve a rate of transmission for a blockchain platform to be deployed in other activities beyond cryptocurrency, like electronic voting.