Reviewerly/exHarmony huggingface dataset:
FPGA Design of Transposed Convolutions for Deep Learning Using High-Level Synthesis

Abstract Deep Learning (DL) is pervasive across a wide variety of domains. Convolutional Neural Networks (CNNs) are often used for image processing DL applications. Modern CNN models are growing to meet the needs of more sophisticated tasks, e.g. using Transposed Convolutions (TCONVs) for image decompression and image generation. Such state-of-the-art DL models often target GPU-based high-performance architectures, due to the high computational and hardware resource needs of TCONV layers. To avoid prohibitive GPU energy costs, CNNs are increasingly deployed to decentralized embedded autonomous devices, such as Field Programmable Gate Arrays (FPGAs). However, this poses challenges for designing efficient hardware implementations of TCONV layers. This paper presents a parameterized design and implementation of a new TCONV module, which is synthesizable onto FPGAs. It is implemented using the High-Level Synthesis (HLS), through a C++ template to parameterize its functional and non-functional properties. These parameters allow kernel sizes, image sizes, quantization and parallelism to be varied by users. With a systematic exploration in this design space, we find an optimal instance of this TCONV module that achieves 6.25 Giga Outputs per Second ( Gout/s ) using just 1.53 W of power. We then use our TCONV layer in two neural networks for image decompression and image generation. Image decompression achieves a speed throughput of more than 30K frames-per-second ( fps ) using only the 16% of resources on average, image generation achieves an energy efficiency of 324 fps /W and outperforms comparable state-of-the-art models by at least 7.3×.
Anti-inference ability of CNN mainstream models based on face mask detection

Face Mask detection is important under epidemic condition, it influences prevention and controlling of epidemic. Picture recognition is mature technology with the development of convolution neural network (CNN). Camera with this function can be easily established for face mask detection, providing unmanned detection for safety concern. Picture noise occurs for long-term using of detection equipment. This research paper aims to explore behaviors of CNN mainstream models under face mask detection, observing their capability of resisting noise interference. AlexNet, visual geometry group (VGG), residual network (ResNet), EfficienNet and Vision Transformer (Vit) are selected as mainstream model. Gaussian (White) noise and Impulse (Salt and Pepper) noise are select to implement noise interference in pictures. Training methods and comparison of testing results are presented in this paper. Result of comparison shows Vit gives best performance for anti-interference ability.
LASSO and Elastic Net Tend to Over-Select Features

Machine learning methods have been a standard approach to select features that are associated with an outcome and build a prediction model when the number of candidate features is large. LASSO has been one of the most popular approaches to this end. LASSO approach selects features with large regression estimates, rather than based on statistical significance, associating the outcome, by imposing L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net (EN) tends to select more features than LASSO. The overly selected features that are not associated with the outcome act like white noise, so that the fitted prediction model loses the prediction accuracy. In this paper, we propose to use the standard regression methods (without any penalizing approach) with stepwise variable selection procedure to overcome these issues. Unlike LASSO and EN, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation based method selects very small number of features while maintaining a high prediction power, while LASSO and EN make a large number of false selections to result in loss of prediction accuracy. Contrary to LASSO and EN, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high dimensional data even without advanced bioinformatics knowledge.
ENCOURAGING STUDENTS’ MOTIVATION TO WRITE WITH GOOGLE SITE.

This study discussed about students’ motivation in using google site as tool in teaching writing. This study used both quantitative and qualitative method. The instrument consist of observation, questionnaire and interview. After analyzing the findings and discussion in the preceding chapter, it can be concluded that utilizing Google Site as an online learning platform stimulates students' motivation in learning writing. The students demonstrated a significant degree of motivation when learning Descriptive Text through Google Site. They were enthusiastic about learning English and were supportive of using Google Site as an effective online learning medium. To motivate students, it is essential for teachers to employ diverse media, as evidenced by the results of this study, which show that using the Google Site application motivates students to learn English, as demonstrated by the dominant extrinsic motivation among the students.
GAN Architecture Leveraging a Retinex Model With Colored Illumination for Low-Light Image Restoration

In this work, we study the restoration of low-light images with outdoor scenes without ground truth. Until now, approaches in the literature have avoided using the Retinex decomposition model in an unsupervised way or have added constraining priors on the searched components. We propose here to relax the constraint of a grayscale illumination of the Retinex model. Indeed, according to the physics of light, it should include a colored illumination. Resulting from this new decomposition model, we formulate a new deep learning-based architecture inspired by the style transfer methods. Our method enables us to visualize the illumination (i.e. a complex style with the same dimensions as an image) and the reflectance (i.e. the content). It achieves more visually pleasing components compared to the state-of-the-art i.e. without artifact, without noise amplification and without hallucination with a simple restoration for each of the components.
Compact and Divisible E-Cash with Threshold Issuance

Decentralized, offline, and privacy-preserving e-cash could fulfil the need for both scalable and byzantine fault-resistant payment systems. Existing offline anonymous e-cash schemes are unsuitable for distributed environments due to a central bank. We construct a distributed offline anonymous e-cash scheme, in which the role of the bank is performed by a quorum of authorities, and present its two instantiations. Our first scheme is compact, i.e. the cost of the issuance protocol and the size of a wallet are independent of the number of coins issued, but the cost of payment grows linearly with the number of coins spent. Our second scheme is divisible and thus the cost of payments is also independent of the number of coins spent, but the verification of deposits is more costly. We provide formal security proof of both schemes and compare the efficiency of their implementations.
DP-SIPS: A simpler, more scalable mechanism for differentially private partition selection

Partition selection, or set union, is an important primitive in differentially private mechanism design: in a database where each user contributes a list of items, the goal is to publish as many of these items as possible under differential privacy. In this work, we present a novel mechanism for differentially private partition selection. This mechanism, which we call {DP-SIPS}, is very simple: it consists of iterating the naive algorithm over the data set multiple times, removing the released partitions from the data set while increasing the privacy budget at each step. This approach preserves the scalability benefits of the naive mechanism, yet its utility compares favorably to more complex approaches developed in prior work.
Save The Implicit Flow? Enabling Privacy-Preserving RP Authentication in OpenID Connect

OpenID Connect (OIDC) is a Single Sign-On (SSO) protocol that allows users to authenticate to various Relying Parties (RPs) via an Identity Provider (IdP). The main drawback of SSO is its lack of privacy, as the IdP learns the RP’s identity at each user’s login. OIDC supports several protocol flows, of which only one, the Implicit Flow, gives hope for any privacy, as it does not require direct communication between the IdP and RP. This design was initially intended for RPs with technical limitations that prevent them from storing credentials and thus authenticating to the IdP. However, RP authentication is crucial to ensure that users only access properly registered RPs. As a result, the Implicit Flow is being discussed to be excluded from the OAuth specification on which OIDC is based. This paper demonstrates a privacy-preserving approach incorporating RP authentication into the Implicit Flow. The IdP can restrict its service to authenticated RPs and tie each authentication token to a specific user and RP without acquiring knowledge of which user is accessing which RP. We formally define the desired security and privacy properties of such an authenticated Implicit Flow, propose a provably secure construction from generic building blocks, and report on an implementation of our scheme
Examining StyleGAN as a Utility-Preserving Face De-identification Method

Several face de-identification methods have been proposed to preserve users’ privacy by obscuring their faces. These methods, however, can degrade the quality of photos, and they usually do not preserve the utility of faces, i.e., their age, gender, pose, and facial expression. Recently, advanced generative adversarial network models, such as StyleGAN [ 33], have been proposed, which generate realistic, high-quality imaginary faces. In this paper, we investigate the use of StyleGAN in generating de-identified faces through style mixing, where the styles or features of the target face and an auxiliary face get mixed to generate a de-identified face that carries the utilities of the target face. We examined this de-identification method for preserving utility and privacy by implementing several face detection, verification, and identification attacks and conducting a user study. The results from our extensive experiments, human evaluation, and comparison with two state-of-the-art face de-identification methods, i.e., CIAGAN and DeepPrivacy, show that StyleGAN performs on par or better than these methods, preserving users’ privacy and images’ utility. In particular, the results of the machine learning-based experiments show that StyleGAN0-4 preserves utility better than CIAGAN and DeepPrivacy while preserving privacy at the same level. StyleGAN 0-3 preserves utility at the same level while providing more privacy. In this paper, for the first time, we also performed a carefully designed user study to examine both privacy and utility-preserving properties of StyleGAN 0-3, 0-4, and 0-5, as well as CIAGAN and DeepPrivacy from the human observers’ perspectives. Our statistical tests showed that participants tend to verify and identify StyleGAN 0-5 images easier than DeepPrivacy images. All the methods but StyleGAN 0-5 had significantly lower identification rates than CIAGAN. Regarding utility, as expected, StyleGAN 0-5 performed significantly better in preserving some attributes. Among all methods, on average, participants believe gender has been preserved the most while naturalness has been preserved the least.
Disparate Vulnerability in Link Inference Attacks against Graph Neural Networks

Graph Neural Networks (GNNs) have been widely used in various graph-based applications. Recent studies have shown that GNNs are vulnerable to link-level membership inference attacks (LMIA) which can infer whether a given link was included in the training graph of a GNN model. While most of the studies focus on the privacy vulnerability of the links in the entire graph, none have inspected the privacy risk of specific subgroups of links (e.g., links between LGBT users). In this paper, we present the first study of disparity in subgroup vulnerability (DSV) of GNNs against LMIA. First, with extensive empirical evaluation, we demonstrate the existence of non-negligible DSV under various settings of GNN models and input graphs. Second, by both statistical and causal analysis, we identify the difference between three specific graph structural properties of subgroups as one of the underlying reasons for DSV. Among the three properties, the difference between subgroup density has the largest causal effect on DSV. Third, inspired by the causal analysis, we design a new defense mechanism named FairDefense to mitigate DSV while providing protection against LMIA. At a high level, at each iteration of target model training, FairDefense randomizes the membership of edges in the training graph with a given probability, aiming to reduce the gap between the density of different subgroups for DSV mitigation. Our empirical results demonstrate that FairDefense outperforms the existing defense methods in the trade-off between defense and target model accuracy. More importantly, it offers better DSV mitigation.
User-information Communication in Libraries as a Model of Behavior: Traditions and New Manifestations

Abstract: The purpose of the article is to trace the development of user-information communication in line with the development of information and communication technologies. Our environment is a factor that influences the behavior patterns of library users. The rapid introduction of artificial intelligence poses new challenges to both users and libraries, because the model of communication is changing and moving to a new type of services and resources. The adaptation of models in accordance with new trends shows the evolution of the environment and the user. By following these models and applying them in practice, a better communication of the user with the information mediated by the services and resources offered by the libraries is achieved. The new conditions of the information environment in which libraries must work pose a number of challenges, the solution of which they must find and offer to their users. The paper analyzes changes in behavior in traditional situations as well as in crisis situations such as the corona virus pandemic. Generalizations are drawn in order to identify contemporary processes and phenomena in the behavior of users in libraries when analyzing Chat GPT, software, algorithms, artificial intelligence. Key words: information, user, library, communication model, information behavior model, Chat GPT.
Modelling and Analysis of Unmanned Aerial Vehicles (UAV’s) for Medical Applications

Abstract: The need for unmanned aerial vehicles in our case hexacopters is broadly increasing in the field of aerospace and medical technology. It is significant to make them strong and robust for them to operate in rough and harsh environments to overcome failures while flying as o respond to emergencies and disasters. The design analysis and modelling of the physical structure play a major role in these vehicles as they are capable of hovering over small areas. For the drone to fly in unexpected weather conditions and handle a certain amount of weight keeping in mind the safety of the object’s weight is a complex problem statement. And for the drone to have a stable and steerable fly until it reaches its destination, the object being carried along must be fixed at a centric position so that the wobbling of the drone can be controlled. As a result, the main goal of this project is to fabricate a drone which can deliver medicines, blood samples and organs to the needed. It can also be equipped with cameras and sensors to collect and transmit data, allowing healthcare professionals to remotely monitor patients and track the progress of their treatment. To observe and predict climate changes at a specific altitude, a thermally insulated medical box is used for carrying medicines, vaccines, etc to deliver supply goods.
A training system on smart phones to develop fashion and textile concepts for the female students with severe hearing impairment

Purpose The purpose of this study is to present a training system on smart phones to develop fashion and textile concepts among the female students with severe hearing impairment and then to determine the effectiveness of the training system in developing fashion and textile concepts among the female students with severe hearing impairment. Design/methodology/approach The analytical descriptive methodology and experimental methodology were used to examine the training system in developing fashion and textile concepts among the female students with severe hearing impairment. Findings The findings of this study indicate that the training system on smart phones was effective in developing fashion and textile concepts among the female students with severe hearing impairment. Research limitations/implications This study has some limitations such as limited sample size, lack of control group and lack of long-term. Practical implications This study provides practical implications for educators, researchers and practitioners in terms of using smart phone applications in educational processes for people with disabilities. Social implications This study has social implications for people with disabilities as it provides them with access to education through smart phone applications which can help them develop their skills in fashion and textile design. Originality/value This study presents an original model of a training system on smart phones for developing fashion and textile concepts among the female students with severe hearing impairment, which can be used as a reference for other studies related to this field.
Design and Improvement of Caesar Cipher

Abstract: Cyber security is the application of technologies, processes and controls to guard systems, networks, programs, devices and data from cyber-attacks. Cryptology is the study of securing Computer systems that allow only sender and receiver to read it.Cryptology comes from Greek word ‘Kryptos’ which means ‘hidden’ and ‘logos’ means ‘to study of’. Even though security is important, numerous applications have been created without considering fundamental points of data security that is confidentiality, authentication, and availability. As we depend more on the internet, security issues and problems will also increase. To prevent alteration or access of data by unauthorized persons, cryptography is required. A methodology is proposed to increase the efficiency of the Caesar cipher which is the simplest cipher. This research introduces a new hybrid secure Caesar cipher by combining the three most important Ciphers (Caesar Cipher, Vigenère Cipher, Polybius Cipher) and Diffie-Hellman technique This hybrid encryption cipher provides better security as compared to normal Caesar ciphers.
Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck Muscle Contraction

Ergonomic efficiency is essential to the mass and prolonged adoption of VR/AR experiences. While VR/AR head-mounted displays unlock users’ natural wide-range head movements during viewing, their neck muscle comfort is inevitably compromised by the added hardware weight. Unfortunately, little quantitative knowledge for understanding and addressing such an issue is available so far.
E-Voting using Blockchain

Abstract: In the era of Industry 4.0, blockchain technology is quickly gaining popularity due to its unique features, such as decentralization, transparency, immutability, and security. These features make it suitable for building various applications in different industries. One such application is E-Voting. Voting is an essential tool for any democratic government. It is the most important factor that makes a government "for the people and by the people". Even though the current system has a strong foundation, there are some issues with it. One of the major issues is that if a voter stays outside of his or her home constituency on election day, it's not simple to vote in any nearby constituency. Some of the other common issues are security breaches like data leaks, vote tampering, etc. Furthermore, it requires a lot of paperwork and time to conduct an election. Therefore, this paper provides an overview of developing a reliable and secure e-voting system using blockchain technology that can help in reducing fraud and ensuring the integrity of the election process, and it also provides an added advantage of mobility
The Influence of Structural Parameters of Georgian Natural and Modified Mordenite over its Adsorption Properties Towards Lead Ions

The potential of Georgian natural Mordenite has been studied as a low-cost, eco-friendly adsorbent for the removal of lead (II) ions from aqueous solutions in various experimental environments.The natural and modified Mordenite structural characterization was carried out using XRD, SEM-EDS, XRF, methods.The following parameters were studied for adsorption: dependence of solution pH, adsorbent dose, and initial concentration of lead (II) ions.The results of the optimization study showed that a high dose of adsorbent and a low concentration of Pb (II) ion increase the sorption.The optimum pH was found to be 6-8.Sorption capacity increased along with pH.Langmuir and Freundlich isotherms were used to model the adsorption data.Correlation coefficient values (R 2 ) were 0.975 and 0.922, respectively.Isotherm studies showed that the uptake equilibrium is best described by the Langmuir adsorption isotherm.The maximum sorption capacity was found to be 11.439 mg/g.The research results showed that natural Georgian Mordenite can be used to remove Pb (II) ions from aqueous solutions.
ChatAvatar: Creating Hyper-realistic Physically-based 3D Facial Assets through AI-Driven Conversations

ChatAvatar revolutionizes the process of creating hyper-realistic 3D facial assets with physically-based rendering (PBR) textures through AI-driven conversations, specifically text-to-avatar interactions. By leveraging advanced diffusion technology and our comprehensive Production-Ready Facial Assets dataset, we generate CG-friendly assets that adhere to industry standards and seamlessly integrate into popular platforms like Unity, Unreal Engine, and Maya. This groundbreaking technology opens up new horizons for immersive virtual experiences, pushing the boundaries of realism and interactivity.
Algorithmic Stability Analysis

In practical applications, we typically solve the empirical risk minimization problem using optimization methods such as stochastic gradient descent (SGD). Such an algorithm searches a model parameter along a path that does not cover the entire model space. Therefore the empirical process analysis may not be optimal to analyze the performance of specific computational procedures. In recent years, another theoretical tool, which we may refer to as stability analysis, has been proposed to analyze such computational procedures.
Flamenco: The Simple Open Source Render Farm

Flamenco is the Open Source render farm software, developed by Blender Studio. It is aimed at performing tasks such as frame rendering and video encoding. This hands-on class will teach how to install and use it, and most importantly, how to adjust and extend it for your specific needs.