Reviewerly/exHarmony huggingface dataset:
SIFNet: Free-form image inpainting using color split-inpaint-fuse approach

Recent deep learning-based approaches have shown outstanding performance in generating visually plausible and refined contents for the missing regions in free-form image inpainting tasks. However, most of the existing methods employ a coarse-to-refine approach where the refinement process depends on a single coarse estimation, often leading to texture and structure inconsistencies. Though several existing methods focus on incorporating additional inputs to mitigate this problem, no learning-based studies have investigated the effects of decomposing input corrupted image into luma and chroma images and performing decoupled inpainting of the decomposed components. To this end, we propose a Split-Inpaint-Fuse Network (SIFNet), an end-to-end two-stage inpainting approach that uses a split-inpaint sub-network for separately inpainting the corrupted luma and chroma images using two decoupled branches in the coarse stage and a fusion sub-network for fusing the inpainted luma and chroma images into a refined image in the refinement stage. Additionally, we propose two attention mechanisms for the coarse stage – a progressive context module to find the patch-level feature similarity for the luma image reconstruction and a spatial-channel context module to find important spatial and channel features for the chroma image reconstruction. Experimental results reveal that our Split-Inpaint-Fuse approach outperforms the existing inpainting methods by comparative margins. In addition, extensive ablation studies confirm the effectiveness of the proposed approach, constituting modules and architectural choices.
Crypto-Currency Price Prediction using Machine Learning

The proposed research work attempts to predict the price values of cryptocurrencies. Cryptocurrencies are the computerized currencies, which are used for all sorts of transactions or used as a long-time investment. Most of the existing systems attention are exclusive on Bitcoin Cryptocurrency. But there are numerous popular crypto-currencies other than bitcoin. The suggested system will be able to predict all the vital crypto-currencies prices with great accuracy. Many parameters will be opted into account to predict the prices validly. The prime parameter will be the interrelation of crypto-currencies prices with the United States dollar value. Crypto-currency price trading is a sought-after type of exchange right now. The suggested system will be of used to great advantage for investors and day-to-day traders. The Machine Learning algorithm used to forecast these prices will be Facebook Prophet. Facebook Prophet has significant accuracy and speed in a Time series prediction.
EMOTE: An interactive online tool for designing real-time emotional AR visualizations

In a future when augmented vision becomes a part of our daily lives, some might want or need to highlight their emotions by visuals on or around their bodies like wearing a prosthesis or an expressive make-up. This might especially be useful for situations where one cannot express their own emotions or read others'. To help the design process of affective augmentations, we developed EMOTE; an online interactive tool that presents a framework to guide designers while designing emotion visualizations for AR. We conducted an online workshop with 24 participants to evaluate the framework and the online tool. In this paper, we share insights on user experience with this tool, the effect of framework on the design process and how the framework can be expanded.
Guidance on Application of Agile in Combined Hardware and Software Development Projects

Abstract Agile has its roots in software, improving the pace and quality of development projects. The application of Agile to hardware is less mature, and anecdotal evidence suggests there are specific challenges in this space. Based upon qualitative interviews with organizations working simultaneously across hardware and software applications, this paper presents three key challenges we identified. We also document several useful adjustments to Agile practice which provide a toolset that teams can use to more successfully apply Agile to hardware projects.
Single image dehazing algorithm based on optical diffraction deep neural networks

Single image dehazing is a challenging task because of the hue and brightness distortion problems due to the atmospheric scattering. These problems limit the perceptual fidelity, as well as information integrity, of a given image. In this paper, we propose an image dehazing method based on the optical neural networks dehazing by simulating optical diffraction. The algorithm is trained from a large number of hazy images and their corresponding clean images. The experimental results demonstrate that the proposed method has reached an advanced level in both PSNR and SSIM dehazing performance indicators, and the amount of calculation is less than most artificial neural networks.
Blockchain based Medical Record Storage and Retrieval using NFT Tracking System

In order to overcome all the flaws and limitations related to the existing system in the medical field regarding the storing, sharing and accessing the medical data or records of the users, this research study come up with a solution to solve this issue by creating a user friendly mobile application to store all the medical information of the users in a single platform, which enables easy sharing, storing and accessing of medical records of the users. This system brings up an easy way to store the medical records of the users in a mobile application using blockchain technology. Since blockchain technology is being used in the system, it enables quite a good amount of security and enables the safety of the data being stored in the application. Once the account is created for a particular patient and all the data is entered by the hospital admins, the user can then have an account for themselves and can also track their medical information in the app as to who are the ones viewing their data. The hospital can feed all kinds of patient information in the application and can save and store them. The data in the application is stored safely and securely ensuring high levels of security and thus keeping the data safe without getting lost and enabling a trustworthy interface to store people's health-related data. Medical data such as scans, x-rays, test reports, prescriptions etc., can be stored in the application in different formats such as images, pdf etc.
Dynamic random distribution learning rate for neural networks training

The learning rate is the most crucial hyper-parameter of a neural network that has a significant impact on its performance. In this article, a novel learning rate setting idea termed randomness distribution learning rate (RDLR) is presented to regulate the learning rate value. The proposed RDLR shifts the learning rate from deterministic to random and sets the value based on the state of the network. The RDLR uses the distance between neurons rather than the covariance matrix to get the redundancy of the network, as well as the Monte Carlo method, and to simplify the neuron to a point to reduce calculation costs. The proposed algorithms do not regulate the learning rate value of each epoch but rather the mathematical expectation and distribution of the learning rate during the training process. The neural network can jump out of the local minimum or unstable area using our algorithms and obtain the minimum point of the area in gradient space. The RDLR algorithms reduce the impact of tiny changes in learning rate value and streamline the tuning process of neural networks. The RDLR saves calculation costs and can work independently or cooperate with the traditional algorithms. In conjunction with traditional learning rate algorithms, the RDLR can set the same learning rate strategy for all layers in a neural network or keep the same mathematical expectation of the learning rate of each layer while adjusting their impulse. The experiments show that the RDLR can improve the performance of a neural network while keeping other hyper-parameters not changed. It is a novel method for adjusting the training process by dynamically changing the random distribution of the learning rate. Our algorithm can monitor the state of the neural network and keep injecting randomness into the neural network training based on the redundancy of the neurons. Furthermore, our algorithm does not require any additional hyper-parameters. The experiments show that our RDLR can improve the performance of multiple structure neural networks in various tasks when applied to a variety of loss functions and data augment methods. • A novel learning rate algorithm based on the random distribution. It adjusts the mathematical expectation and learning rate distribution, separately. • A novel idea to adjust the learning rate based on the current state of the neurons. If the gradient descent increases the redundancy of the neurons too much, the RDLR will try to jump out of the current state. • A calculation cost saved algorithm. Our algorithm uses the Monte Carlo method and dimensional reduction algorithm to save the calculation.
A Research Agenda for Smarter Cyber-Physical Systems

With the advancing digitisation of society and industry we observe a progressing blending of computational, physical, and social processes. The trustworthiness and sustainability of these systems will be vital for our society. However, engineering modern computing systems is complex as they have to: i) operate in uncertain and continuously changing environments, ii) deal with huge amounts of data, and iii) require seamless interaction with human operators. To that end, we argue that both systems and the way we engineer them must become smarter. With smarter we mean that systems and engineering processes adapt and evolve themselves through a perpetual process that continuously improves their capabilities and utility to deal with the uncertainties and amounts of data they face. We highlight key engineering areas: cyber-physical systems, self-adaptation, data-driven technologies, and visual analytics, and outline key challenges in each of them. From this, we propose a research agenda for the years to come.
A State-Of-Art Model of Encrypting Medical Image Using DNA Cryptography and Hybrid Chaos Map - 2d Zaslavaski Map: Review

E-health, smart health and telemedicine are examples of sophisticated healthcare systems. For end-to-end communication, these systems rely on digital medical information. Although this digitizing saves much time, it is open source. As a result, hackers could potentially manipulate the digital medical image as it is being transmitted. It is harder to diagnose an actual disease from a modified digital medical image in medical diagnostics. As a result, ensuring the security and confidentiality of clinical images, as well as reducing the computing time of encryption algorithms, appear to be critical problems for research groups. Conventional approaches are insufficient to ensure high-level medical image security. So this review paper focuses on depicting advanced methods like DNA cryptography and Chaotic Map as advanced techniques that could potentially help in encrypting the digital image at an effective level. This review acknowledges the key accomplishments expressed in the encrypting measures and their success indicators of qualitative and quantitative measurement. This research study also explores the key findings and reasons for finding the lessons learned as a roadmap for impending findings.
Two-layers robust data hiding scheme for highly compressed image exploiting AMBTC with difference expansion

When the secret message is placed inside a highly compressed image, the transmission will be more secure and cost efficient. In this article, two-layer data embedding and extraction approaches are provided that use a combination of Absolute Moment Block Truncation Coding (AMBTC) and Difference Expansion (DE) to enhance hiding capacity on a large scale while producing minimum distortion. The first layer embedding strategy is classified into three situations according to the amount of bit patterns that are concatenated to improve performance. DE is used to build the second layer algorithm. Bitmap has been retaining its previous layer values through DE. The two-layer embedding technique is effective for concealing large volumes of data. In comparison to existing popular AMBTC data hiding strategies, this suggested design provides a low-cost, high-capacity, two-layer efficient data embedding procedure. The anticipated consequence highlighted certain outstanding magnificent aspects in the fields of tampered detection, recovery and authentication schemes, all of which are essential to the modern technological life. This system benefits a wide range of government and business sectors, including health care, commercial security, defence, and intellectual property rights.
Artificial Intelligence-Based Tissue Phenotyping in Colorectal Cancer Histopathology Using Visual and Semantic Features Aggregation

Tissue phenotyping of the tumor microenvironment has a decisive role in digital profiling of intra-tumor heterogeneity, epigenetics, and progression of cancer. Most of the existing methods for tissue phenotyping often rely on time-consuming and error-prone manual procedures. Recently, with the advent of advanced technologies, these procedures have been automated using artificial intelligence techniques. In this paper, a novel deep histology heterogeneous feature aggregation network (HHFA-Net) is proposed based on visual and semantic information fusion for the detection of tissue phenotypes in colorectal cancer (CRC). We adopted and tested various data augmentation techniques to avoid computationally expensive stain normalization procedures and handle limited and imbalanced data problems. Three publicly available datasets are used in the experiments: CRC tissue phenotyping (CRC-TP), CRC histology (CRCH), and colon cancer histology (CCH). The proposed HHFA-Net achieves higher accuracies than the state-of-the-art methods for tissue phenotyping in CRC histopathology images.
Evaluating Performance Metrics in Classifying Bitcoin Mixing Services Using Decision Tree Algorithm

Bitcoin is a decentralized peer-to-peer (P2P) cryptocurrency system with an innovative payment network. For blockchain applications, ECDSA is formed with public and private keys, especially in Bitcoin, which uses an elliptic curve in a cryptography standard known as Secp256k1 to ensure funds are spent by legitimate owners. Despite the fact that ECDSA is a key component of Bitcoin transactions, today's criminals employ pseudonymous addresses, which make it impossible to track unlawful activity because they don't maintain real-world identities. Bitcoin Fog, Helix Mix, and other mixing services are supposed to provide transaction privacy. However, these services are commonly utilized for concealment, it is more difficult to track criminals. First, using time-frequency analysis, features can be recovered at the network, account, or transaction level to create attributed temporal heterogeneous network Motifs and characterize many forms of address patterns. To increase the performance of evaluation metrics, we introduced a decision-tree machine learning approach for the categorization of bitcoin mixing services from unlabeled addresses in this work. Experiments with bitcoin Kaggle datasets are frequently used to determine the success of our categorization approach.
OpenBioMaps – self-hosted data management platform and distributed service for biodiversity related data

Abstract Biodiversity related observational data are collected in a variety of ways and for a variety of purposes, mostly in the form of some sort of organised data collection action. Data management solutions are often developed to manage the data collection processes and organise the data, which may work well on their own but are less compatible with other data management tools. In a continuous development process, we have created the OpenBioMaps (OBM) biodiversity data management platform, which can be used as a self-hosted data management platform and as a free service, hosted by several institutions for biological database projects. OBM has the ability to integrate biological databases without any structural or functional constraints, allowing a high degree of flexibility in data management and development; it provides interfaces to facilitate communication between different end-user communities, including scientists, citizens, conservationists and educational staff. We have also established a network of OBM services based on collaboration between government, educational and scientific institutions and NGOs to provide a public service to those who lack the capacity or knowledge to set up or manage their own self-hosted servers. OpenBioMaps uniquely focuses on the entire data management process, from building the data structure to data collection, visualisation, sharing and processing.
CLICKA: Collecting and leveraging identity cues with keystroke dynamics

The way in which IT systems are usually secured is through the use of username and password pairs. However, these credentials are all too easily lost, stolen or compromised. The use of behavioural biometrics can be used to supplement these credentials to provide a greater level of assurance in the identity of an authenticated user. However, user behaviours can also be used to ascertain other identifiable information about an individual. In this paper we build upon the notion of keystroke dynamics (the analysis of typing behaviours) to infer an anonymous user's name and predict their native language. This work found that there is a discernible difference in the ranking of bigrams (based on their timing) contained within the name of a user and those that are not. As a result we propose that individuals will reliably type information they are familiar with in a discernibly different way. In our study we found that it should be possible to identify approximately a third of the bigrams forming an anonymous users name purely from how (not what) they type.
Knowledge distillation guided by multiple homogeneous teachers

Knowledge distillation (KD) transfers knowledge from a heavy teacher network to a lightweight student network while maintaining the student’s performance closely to that of the teacher. However, the large gap between the teacher and the student in terms of capacity is not conducive to KD. Consequently, a large teacher network is not necessarily the most suitable teacher to guide the student. Therefore, this study proposes a multiple homogeneous teacher-guided KD method. First, multiple networks with the same structure as that of the student are pretrained to act as a teacher group, which is different from utilizing a large teacher network in traditional KD, to alleviate the capacity gap between the teacher and student. Second, a confidence-adaptive initialization strategy is developed to initialize the student network, which learns knowledge from the pretrained teacher group. Experiments are performed on CIFAR10, CIFAR100, and Tiny-ImageNet using three networks. The experimental results demonstrate that the proposed KD method outperforms existing advanced KD methods. Furthermore, a similarity loss function is introduced to optimize the parameters of the classifier in the student network. The experimental results indicate that this loss function improves the performance for basic classification tasks without KD and efficiently works in the proposed KD method.
HELPSE: Homomorphic Encryption-based Lightweight Password Strength Estimation in a Virtual Keyboard System

Recently, cyber-physical systems are actively using cloud servers to overcome the limitations of power and processing speed of edge devices. When passwords generated on a client device are evaluated on a server, the information is exposed not only on networks but also on the server-side. To solve this problem, we move the previous lightweight password strength estimation (LPSE) algorithm to a homomorphic encryption (HE) domain. Our proposed method adopts numerical methods to perform the operations of the LPSE algorithm, which is not provided in HE schemes. In addition, the LPSE algorithm is modified to increase the number of iterations of the numerical methods given depth constraints. Our proposed HE-based LPSE (HELPSE) method is implemented as a client-server model. As a client-side, a virtual keyboard system is implemented on an embedded development board with a camera sensor. A password is obtained from this system, encrypted, and sent over a network to a resource-rich server-side. The proposed HELPSE method is performed on the server. Using depths of about 20, our proposed method shows average error rates of less than 1% compared to the original LPSE algorithm. For a polynomial degree of 32K, the execution time on the server-side is about 5 seconds.
Semi-supervised Drifted Stream Learning with Short Lookback

In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i.i.d. and data drift over time gradually; 4) the storage of historical streams is limited. This learning setting limits the applicability and availability of many Machine Learning (ML) algorithms. We generalize the learning task under such setting as a semi-supervised drifted stream learning with short lookback problem (SDSL). SDSL imposes two under-addressed challenges on existing methods in semi-supervised learning and continuous learning: 1) robust pseudo-labeling under gradual shifts and 2) anti-forgetting adaptation with short lookback. To tackle these challenges, we propose a principled and generic generation-replay framework to solve SDSL. To achieve robust pseudo-labeling, we develop a novel pseudo-label classification model to leverage supervised knowledge of previously labeled data, unsupervised knowledge of new data, and, structure knowledge of invariant label semantics. To achieve adaptive anti-forgetting model replay, we propose to view the anti-forgetting adaptation task as a flat region search problem. We propose a novel minimax game-based replay objective function to solve the flat region search problem and develop an effective optimization solver. Experimental results demonstrate the effectiveness of the proposed method.
Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks

Despite the successful use of Gaussian-binary restricted Boltzmann machines (GB-RBMs) and Gaussian-binary deep belief networks (GB-DBNs), little is known about their theoretical approximation capabilities to represent distributions of continuous random variables. In this paper, we address the expressive properties of GB-RBMs and GB-DBNs, contributing theoretical insights to the optimal number of hidden variables. We first treat the GB-RBM's unnormalized log-likelihood as a sum of a special two-layer feedforward neural network and a negative quadratic term. Then, a series of simulation results are established, which can be used to relate GB-RBMs to general two-layer feedforward neural networks whose expressive properties are much better understood. On this basis, we show that a two-layer ReLU network with all weights in the second layer being 1, along with a negative quadratic term, can approximate all continuous functions. In addition, we provide qualified lower bounds for the number of hidden variables of GB-RBMs required to approximate distributions whose log-likelihood are given by some classes of smooth functions. Moreover, we further study the universal approximation of GB-DBNs with two hidden layers by providing a sufficient number of hidden variables O(ɛ-2) that are guaranteed to approximate any given strictly positive continuous distribution within a given error ɛ. Finally, numerical experiments are carried out to verify some of the proposed theoretical results.