Reviewerly/exHarmony huggingface dataset:
Generative image transformer (GIT): unsupervised continuous image generative and transformable model for [123I]FP-CIT SPECT images

Recently, generative adversarial networks began to be actively studied in the field of medical imaging. These models are used for augmenting the variation of images to improve the accuracy of computer-aided diagnosis. In this paper, we propose an alternative new image generative model based on transformer decoder blocks and verify the performance of our model in generating SPECT images that have characteristics of Parkinson's disease patients.Firstly, we proposed a new model architecture that is based on a transformer decoder block and is extended to generate slice images. From few superior slices of 3D volume, our model generates the rest of the inferior slices sequentially. Our model was trained by using [123I]FP-CIT SPECT images of Parkinson's disease patients that originated from the Parkinson's Progression Marker Initiative database. Pixel values of SPECT images were normalized by the specific/nonspecific binding ratio (SNBR). After training the model, we generated [123I]FP-CIT SPECT images. The transformation of images of the healthy control case SPECT images into PD-like images was also performed. Generated images were visually inspected and evaluated using the mean absolute value and asymmetric index.Our model was successfully generated and transformed into PD-like SPECT images. The mean absolute SNBR was mostly less than 0.15 in absolute value. The variation of the obtained dataset images was confirmed by the analysis of the asymmetric index.These results showed the potential ability of our new generative approach for SPECT images that the generative model based on the transformer realized both generation and transformation by a single model.
An enhanced separable reversible and secure patient data hiding algorithm for telemedicine applications

In telemedicine industry, a standout among the most significant issues is the exchange of Electronic Patient Information (EPI) between patient and a doctor that are remotely connected. A minute change to EPI may result in wrong diagnosis to the patient. With the aim to ensure secure and safe communications in telemedicine framework, an enhanced separable reversible data hiding technique in encrypted domain has been presented here that gives high embedding rate by embedding k, (k≥1) binary bits of patient data in base2k numeral framework using Average Pixel Repetition (APR) method. APR method converts original image of size M×N into encrypted scaled-up image of size (2×M−1)×(2×N−1) such that non-seed pixels are used for data embedding where as seed pixels remain as it is to facilitate reversibility. The experimental study shown that the proposed method gave extremely high embedding rates on both natural and medical images. For all test images, the proposed methodology altogether beated all the compared methodologies in its ability to embed patient information and precisely recover it with maintaining the visual quality of stego images too.
A Detection Method of Operated Fake-Images Using Robust Hashing

SNS providers are known to carry out the recompression and resizing of uploaded images, but most conventional methods for detecting fake images/tampered images are not robust enough against such operations. In this paper, we propose a novel method for detecting fake images, including distortion caused by image operations such as image compression and resizing. We select a robust hashing method, which retrieves images similar to a query image, for fake-image/tampered-image detection, and hash values extracted from both reference and query images are used to robustly detect fake-images for the first time. If there is an original hash code from a reference image for comparison, the proposed method can more robustly detect fake images than conventional methods. One of the practical applications of this method is to monitor images, including synthetic ones sold by a company. In experiments, the proposed fake-image detection is demonstrated to outperform state-of-the-art methods under the use of various datasets including fake images generated with GANs.
CNN Based Autoencoder Application in Breast Cancer Image Retrieval

Content Based Medical Image Retrieval (CBMIR) is considered as a common technique to retrieve relevant images by comparing the features contained in the query image with the features contained in the image located in the database. Currently, the study related to CBMIR on breast cancer image however remains challenging due to inadequate research in such area. Previous study has a low performance and misinformation emphasizing the feature extraction process. Therefore, this study aims to utilize the CNN based Autoencoder method to minimize misinformation in the feature extraction process and to improve the performance result. The dataset used in this study is the BreakHis dataset. Overall, the results of image retrieval in breast cancer applying the CNN based Autoencoder method achieved higher performance compared to the method used in the previous study with an average precision of 0.9237 in the mainclass dataset category and 0.6825 in the subclass dataset category.
HMM-BiMM: Hidden Markov Model-based word segmentation via improved Bi-directional Maximal Matching algorithm

Combining with the Hidden Markov Model and Bi-directional Maximal Matching algorithm, a new word segmentation algorithm, HMM-BiMM, is presented. In terms of the sub-dictionary matching, it can implement a fast word segmentation. After segmenting the text by the Bi-directional Maximal Matching (BiMM), the remaining text connected by the remaining single words will be segmented again by the strategy of the Hidden Markov Model (HMM). By the HMM, this algorithm can realize the dictionary dynamic update by the new segmentation words and improve the segmentation accuracy accordingly. Compared with five representative algorithms in the real-world clinical text (symptom), we show that the HMM-BiMM algorithm achieves the highest efficiency and accuracy for symptom text segmentation. In detail, this algorithm has around 3% in precision and 70% in running time improved to the BiMM.
A New Simple Technique to Bootstrap Various Lattice Zero-Knowledge Proofs to QROM Secure NIZKs

Many of the recent advanced lattice-based $$\varSigma $$ -/public-coin honest verifier (HVZK) interactive protocols based on the techniques developed by Lyubashevsky (Asiacrypt'09, Eurocrypt'12) can be transformed into a non-interactive zero-knowledge (NIZK) proof in the random oracle model (ROM) using the Fiat-Shamir transform. Unfortunately, although they are known to be secure in the classical ROM, existing proof techniques are incapable of proving them secure in the quantum ROM (QROM). Alternatively, while we could instead rely on the Unruh transform (Eurocrypt'15), the resulting QROM secure NIZK will incur a large overhead compared to the underlying interactive protocol. In this paper, we present a new simple semi-generic transform that compiles many existing lattice-based $$\varSigma $$ -/public-coin HVZK interactive protocols into QROM secure NIZKs. Our transform builds on a new primitive called extractable linear homomorphic commitment protocol. The resulting NIZK has several appealing features: it is not only a proof of knowledge but also straight-line extractable; the proof overhead is smaller compared to the Unruh transform; it enjoys a relatively small reduction loss; and it requires minimal background on quantum computation. To illustrate the generality of our technique, we show how to transform the recent Bootle et al.'s 5-round protocol with an exact sound proof (Crypto'19) into a QROM secure NIZK by increasing the proof size by a factor of 2.6. This compares favorably to the Unruh transform that requires a factor of more than 50.
Real-Time Block-Based Embedded CNN for Gesture Classification on an FPGA

This paper presents a block-based embedded convolutional neural network (CNN) for gesture classification on field-programmable gate array (FPGA) in real time. Gesture recognition is an important tool to spontaneous interact with human machine interface. Many CNN architectures using RGB images have been proposed for gesture classification. RGB based gesture classification may cause incorrect results under insufficient light or similar gestures. In addition, most of the CNN architectures cannot run in real time on edge devices due to their large number of parameters and DRAM data access. In this paper, a block-based CNN using RGB-D data is proposed for gesture classification. Adding depth images to RGB images boots the classification accuracy. A CNN architecture with block-based feature maps is built for embedded FPGA implementations. The total number of parameters of the proposed RGB-D embedded CNN (eCNN) model is only 0.17M and it achieves 99.96% and 99.88% accuracy with 32-bit floating point and 8-bit fixed point implementation for America Sign Language (ASL) data set. The RTL simulation of the proposed eCNN model has the average inference speed of 0.171 milliseconds at frequency of 250MHz for a single pair RGB-D image. Implemented on a FPGA integrated with Microsoft Kinect v2 achieve an inference time in 19.42 ms which achieves high accuracy and real-time performance.
Collaborative filtering grounded on knowledge graphs

Matrix Factorization (MF) is a widely used collaborative filtering technique for effectively modeling a user-item interaction in recommender system. Despite the successful application of MF and its variants, the method proves to be effective only in situations where there is an abundance of user-item interactions. However, user-item interaction data are usually sparse, limiting the effectiveness of the method. In addressing this problem, recent methods have proposed to use knowledge graphs (KGs) as additional information to complement the sparse user-item interaction data. This has proved challenging given the complexity of the KG structure. In this paper, we propose a collaborative filtering method that takes advantage of knowledge graphs. More specifically, the embedding of a user and item are both grounded on the item's attributes in the knowledge graph, and are aggregated with generic user and item representations modeled by MF for implicit recommendation. Our model has demonstrated to outperform the recent state-of-the-art method KGCN [18] in very sparse settings, showing an effective integration of KGs in recommender systems.
Object-based cluster validation with densities

Clustering validity indices are typically used as tools to find the correct number of clusters in a data set and/or to evaluate the quality of the clusters formed by clustering algorithms. Clustering validity indices measure separation and compactness of clusters. Typically, when applying a clustering algorithm, the input includes the number of clusters. After applying the algorithm with several different numbers of clusters, we determine the number of clusters to be the one with the best validity index. There are two types of clustering validity indices: external indices that are supervised, and internal indices that are unsupervised. The focus of this paper is on internal validity indices. Some existing internal validity indices capture the properties of the clusters by using representative statistics such as mean, variance, diameter, etc., however, these do not perform well when clusters have arbitrary shapes. One approach to overcome this issue is to use the density of the data objects in each cluster. That provides the advantage of capturing the full characteristics of the cluster which is most beneficial when there are clusters with arbitrary shapes. In the literature, a few density-based clustering validity indices have been proposed. However, some of them show poor performance when the clusters are not perfectly separated. Some others perform poorly because they use only representative objects from each cluster instead of all objects. The contribution of this paper is an internal validity index named the object-based clustering validity index with densities (OCVD). OCVD is a single number that averages the density-based contribution of individual data objects to both separation and compactness of clusters. The methodology behind calculating the density-based contributions of the objects is kernel density estimation. We show through several experiments that OCVD performs well in detecting the correct number of clusters in data sets with different cluster shapes including arbitrary shapes.
Exploiting Facial Symmetry to Expose Deepfakes

In this paper, we introduce a new approach to detect synthetic portrait images and videos. Motivated by the observation that the symmetry of synthetic facial area would be easily broken, this approach aims to reveal the tampering trace by features learned from symmetrical facial regions. To do so, a two-stream learning framework is designed which uses a hard sharing Deep Residual Networks as the backbone network. The feature extractor maps the pair of symmetrical face patches to an angular distance indicating the difference of symmetry features. Extensive experiments are carried out to test the effectiveness in detecting synthetic portrait images and videos, and corresponding results show that our approach is effective even on heterogeneous data and re-compression data that were not used to train the detection model.
Multi-Level Optimization Process for Rationalizing the Distribution Logistics Process of Companies Selling Dietary Supplements

The commission sales form is a very significant channel of sales today, which is especially true in the field of dietary supplements. In parallel, the prevailing digitalization trends have opened up further new opportunities for this form of distribution. The multi-level optimization process presented in the publication makes it possible to optimize the distribution logistics processes of companies producing food supplements at a high level by exploiting these new possibilities. The operation of the procedure is also illustrated through a practical example.
Automatic generation of atomic multiplicity-preserving search operators for search-based model engineering

Abstract Recently, there has been increased interest in combining model-driven engineering and search-based software engineering. Such approaches use meta-heuristic search guided by search operators (model mutators and sometimes breeders) implemented as model transformations. The design of these operators can substantially impact the effectiveness and efficiency of the meta-heuristic search. Currently, designing search operators is left to the person specifying the optimisation problem. However, developing consistent and efficient search-operator rules requires not only domain expertise but also in-depth knowledge about optimisation, which makes the use of model-based meta-heuristic search challenging and expensive. In this paper, we propose a generalised approach to automatically generate atomic multiplicity-preserving search operators for a given optimisation problem. This reduces the effort required to specify an optimisation problem and shields optimisation users from the complexity of implementing efficient meta-heuristic search mutation operators. We evaluate our approach with a set of case studies and show that the automatically generated rules are comparable to, and in some cases better than, manually created rules at guiding evolutionary search towards near-optimal solutions.
TPM-Based Post-Quantum Cryptography

The prospect of large-scale quantum computers necessitates the design, development, and standardization of post-quantum cryptography (PQC). Industrial control systems (ICS) and critical infrastructures are expected to be among the first industrial environments to adopt PQC. As their components have a long life span (≥ 10 years) and are increasingly interconnected to form an Industrial Internet of Things (IIoT), they require strong and long-lasting security guarantees. Because of these high-security requirements, IIoT products are also increasingly equipped with additional hardware security elements — often Trusted Platform Modules (TPMs).
The fight against malware as a service

Malware developers and distributors wasted no time in exploiting the Covid-19 pandemic for their own malicious objectives. In 2020, the number of new malware samples nearly doubled compared with 2019.1 More concerningly, cyber criminals are also becoming smarter. Rather than merely relying upon a larger quantity of threats, they are also leveraging technology innovations to grow their criminal organisations and generate new revenue streams.
Edge Detection in Digital Images Using Guided L0 Smoothen Filter and Fuzzy Logic

Image segmentation is an important process in computer vision. Recently fuzzy logic based edge detection is heavily investigated as by changing the number of rules edge detection can be improved. However, due to large colour variations in the images false edges are detected and even using fuzzy rules they cannot be reduced significantly. These falsely detected edges can be controlled by using smoothen filter while controlling the degree of smoothness. This paper, presents fuzzy logic based edge detection mechanism while using Guided L0 smoothen filter for the smoothening of image under various degree of smoothens. Simulation results for edge detection is presented for Canny, Sobel, Fuzzy logic based edge detection and finally fuzzy logic edge detection with inclusion of L0 smoothen filter. The results are compared with classical and modern methods. Simulation is performed on Berkley Segmentation Database (BSD) and USC-SIPI Image Database while considering more than 100 images. The obtained F-measure is as high as 0.848.
Anti-Distractor Active Object Tracking in 3D Environments

In active object tracking, given a visual observation as input, the goal is to lockup the target by autonomously adjusting camera’s position and posture. Previous works on active tracking assume that there is only one object (person) in the environment without distractors. In this work, towards realistic setting, we move forward to a more challenging scenario, where the tracker moves freely in 3D space like unmanned aerial vehicles (UAV) to track a person in various complex scenes with multiple distractors. To this end, we propose a novel end-to-end anti-distractor active object tracking framework by introducing multiple attention modules. On one hand, we take the target template to learn an embedding as channel-wise attention for current observation to distinguish the target from the distractors. On the other hand, temporal attention is introduced to fuse the observation history to extract a feature representation, which is then fed into a reinforcement learning network to output the action of the tracker. To evaluate our method, we build several multi-object 3D environments in Unreal Engine and extensive experiments demonstrate the effectiveness of our approach.
Quantum process in probability representation of quantum mechanics

In this work, the operator-sum representation of a quantum process is extended to the probability representation of quantum mechanics. It is shown that each process admitting the operator-sum representation is assigned a kernel, convolving of which with the initial tomogram set characterizing the system state gives the tomographic state of the transformed system. This kernel, in turn, is broken into the kernels of partial operations, each of them incorporating the symbol of the evolution operator related to the joint evolution of the system and an ancillary environment. Such a kernel decomposition for the projection to a certain basis state and a Gaussian-type projection is demonstrated as well as qubit flipping and amplitude damping processes.
Where's the germs? The effects of using virtual reality on nursing students' hospital infection prevention during the <scp>COVID</scp>‐19 pandemic

Virtual reality can be a useful substitute to improve nursing education outside of the hospital setting. In the wake of COVID-19, an opportunity to compare a virtual reality activity for nursing students in the traditional classroom and in an online environment took place.The goal of this study was to examine the effects of incorporating a virtual reality activity into the traditional classroom and in an online environment to enhance nursing education.Fifty nursing students participated in an exploratory descriptive study. Twenty-five students participated face-to-face while in the traditional classroom setting and 25-students participated in an online environment using a communication platform. Through a virtual reality activity, nursing students successfully created a tour which demonstrated identifying infectious sites in a hospital room setting.Results of the pre-and post-test showed students' positive perceptions and performances in both traditional classroom and online settings. However, the study revealed a statistical difference between the two group perceptions towards the use of the virtual reality tour creator. Nursing students who were in the traditional classroom group found that the use the virtual reality tour creator was easier.Designing a meaningful virtual reality activity has a positive impact of the student learning experience. Students may benefit from a more through orientation to the technology prior to engaging in the activity. A carefully constructed activity can enhance student learning outside of the hospital room.
Dialogue History Matters! Personalized Response Selection in Multi-Turn Retrieval-Based Chatbots

Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context utterances and response. However, in real-place conversation scenarios, whether a response candidate is suitable not only counts on the given dialogue context but also other backgrounds, e.g., wording habits, user-specific dialogue history content. To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN). Our contributions are two-fold: (1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information; (2) we perform hybrid representation learning on context-response utterances and explicitly incorporate a customized attention mechanism to extract vital information from context-response interactions so as to improve the accuracy of matching. We evaluate our model on two large datasets with user identification, i.e., personalized Ubuntu dialogue Corpus (P-Ubuntu) and personalized Weibo dataset (P-Weibo). Experimental results confirm that our method significantly outperforms several strong models by combining personalized attention, wording behaviors, and hybrid representation learning.