Reviewerly/exHarmony huggingface dataset:
Lattice-based remote user authentication from reusable fuzzy signature

In this paper, we introduce a new construction of reusable fuzzy signature based remote user authentication that is secure against quantum computers. We investigate the reusability of fuzzy signature, and we prove that the fuzzy signature schemes provide biometrics reusability (aka. reusable fuzzy signature). We define formal security models for the proposed construction, and we prove that it achieves user authenticity and user privacy. The proposed construction ensures: 1) a user’s biometrics can be securely reused in remote user authentication; 2) a third party having access to the communication channel between a user and the authentication server cannot identify the user.
AEDBSCAN—Adaptive Epsilon Density-Based Spatial Clustering of Applications with Noise

The objectives of this research are related to study the DBSCAN algorithm and engineer an enhancement to this algorithm addressing its flaws. DBSCAN is criticized for its requirement to input two parameters, namely—epsilon radius (ϵ) and minimum number of points (MinPts). It is difficult to know beforehand the optimum value of both parameters, and hence many trials are required until desired clusters are obtained. Also, in a dataset, a cluster's density can vary. DBSCAN fails to identify clusters with density variations present. The proposed algorithm Adaptive Epsilon DBSCAN (AEDBSCAN), generates epsilon dynamically in accordance with the neighborhood of a point and thereafter adopts DBSCAN clustering with the corresponding epsilon to obtain the clusters. Experimental results are obtained from testing AEDBSCAN on artificial datasets. The experimental results confirm that the proposed AEDBSCAN algorithm efficiently carries out multi-density clustering than the original DBSCAN.
CS-GANomaly: A Supervised Anomaly Detection Approach with Ancillary Classifier GANs for Chromosome Images

Although anomaly detection is an urgent and significant task in various applications, there are few good enough methods to meet the requirements. The challenge of anomaly detection lies in how to distinguish the diverse or even unknown anomalies. Therefore, previous studies have focused on imagelevel anomaly detection tasks, and mostly based on unsupervised methods. These methods only consider the characteristics of normal samples and are confined to the “normal” category, ignoring information between samples. In particular, the limitations of unsupervised methods are more obvious for pixellevel abnormalities such as the chromosomal structural anomalies investigated in this paper, because the similarity between normal and abnormal samples is relatively high. In this work, we propose a supervised anomaly detection approach Classification - Enhanced GANomaly (abbreviated as CS-GANomaly). By fusing the category information with the sample, the spatial distribution of the sample has regional characteristics and then combining the anomaly detection strategies to carry out multidimension anomaly detection. The CS-GANomaly adopts the GAN framework for adversarial training and adapts the GAN output to anomaly detection. The experimental results prove the superiority of our approach, whether it is a pixel-level anomaly in abnormal chromosome dataset or image-level anomaly in the benchmark dataset.
A secure and efficient group signature scheme based on multivariate public key cryptography

Group signatures are significant primitive for anonymity, which allow group members to sign messages while hiding in the group, however, the signers remain accountable. Most of the existing schemes on group signature are relying on traditional cryptographic primitives, whereas rapid advancements in quantum computing suggest an originating threat to usual cryptographic primitives. This makes the necessity of quantum computer resistant cryptographic primitives. Multivariate public key cryptography (MPKC) is one of the promising options that may withstand quantum attacks. Its constructions are potential candidates for post-quantum (PQ) cryptography as they are very fast and require only modest computational resources. There are many existing secure and practical multivariate digital signatures. However, there is a deficiency of more advanced multivariate group signature scheme. The existing multivariate group signature has weaknesses in terms of security and efficiency. This paper introduces a new multivariate group signature scheme employing a 5-pass identification protocol and multivariate signature scheme as its building blocks. The proposed signature scheme possesses unforgeability, user’s anonymity, unlinkability, exculpability and traceability property. Unlike most of the existing post-quantum group signatures, the sizes of the signatures and the public parameters are not dependent on the number of group users in our construction. It depends only on the security parameters. In particular, our construction is the first MPKC based group signature, where signature size and public parameter size are independent of the number of group users.
Learning Guided Attention Masks for Facial Action Unit Recognition

Humans have the innate ability to rapidly spot and react to a person's emotional response. For computers to be able to understand expressions in a similar way, the gap between the perception of expressions between the humans and computers needs to be minimized. Inspired by human visual fixations, we propose a guided attention mechanism that facilitates the network to `look' at the most important features of a face. Rather than imposing hard attention, we learn the attention maps from the intermediate representation for Action Units (AUs). We propose a joint attention learning and AU classification module with minimal increase in the network parameters. We demonstrate the efficiency of our approach on three standard datasets: BP4D, MMSE and DISFA and obtain state of the art and near state of the art results respectively.
Virtual Character Animation based on Data-driven Motion Capture using Deep Learning Technique

Perceptions in motion capture (mocap) technology are increasing every day as the variety of applications using it is doubling. By leveraging the resources offered by mocap technology, human activity characteristics are captured and can be used as the source for animation. The devices involved in the technology are therefore very costly and hence not practical for personal use. In this scenario, we implement a framework capable of producing mocap data from standard RGB video and use it to animate a character in 3D space, based on the action of person in the original video with the help of deep learning techniques. The Human Mesh Recovery (HMR) scheme is used to extract mocap data from the input video to determine where joints of the person in the input video are located in 3D space, using 2D pose estimation. The locations of 3D joints are used as mocap data and transferred to Blender with a simple 3D character using which the character can be animated. A subjective evaluation of our framework based on the metric called observation factor was performed and yielded an accuracy value of 73.5%.
Semantic Face Image Inpainting based on Generative Adversarial Network

With the popularity of Internet technology and camera equipment, people are accustomed to using images and videos to record life. Image deletion is one of the most important degradation directions when image degradation occurs. The repair process of the digital image repair method is to use the information of the missing part of the image, according to certain repair rules to repair and fill the missing part of the image, so that the repaired image is complete and natural. At present, the existing image inpainting algorithms still have some shortcomings in visual effect and algorithm efficiency. In order to solve the problems of fuzzy details and poor visual perception of the existing technology in the implementation of face image inpainting results, as well as the problem that the whole model could not be controlled due to the mode collapse caused by the use of the generative adversarial network, this paper provides a semantic inpainting method of face image based on multi-scale feature fusion. Using suppression enhancement unit to suppress useless channels, enhance useful channels, acquire long-range and multi-level dependency interaction without increasing parameters, coordinate the details of each position and the details of the far end when repairing the image, expand the receptive field, make up for the lack of information when generating the missing image edge, balance the learning ability of generating network and discriminating network to improve the inpainting effect of missing face image.
A musical reading of a contemporary installation and back: mathematical investigations of patterns in <i>Qwalala</i>

Mathematical music theory helps us investigate musical compositions in mathematical terms.Some hints can be extended towards the visual arts.Mathematical approaches can also help formalize a "translation" from the visual domain to the auditory one and vice versa.Thus, a visual artwork can be mathematically investigated, then translated into music.The final, refined musical rendition can be compared to the initial visual idea.Can an artistic idea be preserved through these changes of media?Can a non-trivial pattern be envisaged in an artwork, and then still be identified after the change of medium?Here, we consider a contemporary installation and an ensemble musical piece derived from it.We first mathematically investigate the installation, finding its patterns and structure, and then we compare them with structure and patterns of the musical composition.In particular, we apply two concepts of mathematical music theory, the Quantum GestART and the gestural similarity conjecture, to the analysis of Qwalala, realized for the Venice Biennale by Pae White, comparing it to its musical rendition in the homonymous piece for harp and ensemble composed by Federico Favali.Some sketches of generalizations follow, with the "Souvenir Theorem" and the "Art Conjecture.
Adversarial Training for Session-based Item Recommendations

Deep neural networks (DNN) have become the mainstream in session-based recommendations (SR) due to their capability of learning feature representations from scratch and capturing the dynamic non-linear user-item relationships. Despite their popularity, these DNN-based SR models tend to be unstable, and small scale perturbations to the inputs may lead to incorrect predictions. In this paper, we propose two adversarial training (AT) methods to construct adversarial perturbations and add them to the DNN-based SR models so as to improve their robustness against adversarial perturbations and overfitting. The generated adversarial perturbations are applicable to any DNN-based SR models, which can be treated as common techniques for further improving the performance of DNN-based SR models. We investigate the effectiveness of the constructed adversarial perturbations by applying them to the two most important DNN architectures (recurrent neural network and convolutional neural network) on two benchmark datasets. Experimental results demonstrate that the AT can significantly improve the performance of the baseline methods.
Digital Forensics Analysis of Ubuntu Touch on PinePhone

New smartphones made by small companies enter the technology market everyday. These new devices introduce new challenges for mobile forensic investigators as these devices end up becoming pertinent evidence during an investigation. One such device is the PinePhone from Pine Microsystems (Pine64). These new devices are sometimes also shipped with OSes that are developed by open source communities and are otherwise never seen by investigators. Ubuntu Touch is one of these OSes and is currently being developed for deployment on the PinePhone. There is little research behind both the device and OS on what methodology an investigator should follow to reliably and accurately extract data. This results in potentially flawed methodologies being used before any testing can occur and contributes to the backlog of devices that need to be processed. Therefore, in this paper, the first forensic analysis of the PinePhone device with Ubuntu Touch OS is performed using Autopsy, an open source tool, to establish a framework that can be used to examine and analyze devices running the Ubuntu Touch OS. The findings include analysis of artifacts that could impact user privacy and data security, organization structure of file storage, app storage, OS, etc. Moreover, locations within the device that stores call logs, SMS messages, images, and videos are reported. Interesting findings include forensic artifacts, which could be useful to investigators in understanding user activity and attribution. This research will provide a roadmap to the digital forensic investigators to efficiently and effectively conduct their investigations where they have Ubuntu Touch OS and/or PinePhone as the evidence source.
Security Model for Internet of Things Based on Blockchain

The Internet of Things has emerged as an area of incredible potential and growth, complemented by the advent of smart devices with increased computational power. However, these complex systems have large attack surfaces making them susceptible to various security attacks. Most common among these are data forgery and identity theft. Further, once the IoT is compromised, the smart devices may be controlled remotely by the attacker and used as botnets in Distributed Denial of Service attacks. The size and scale of the complex system result in technical challenges in management, synchronization, and security. In this paper, a blockchain model is proposed and implemented to secure the IoT system. Using Ethereum, a private blockchain model is developed, verified its functionality in a simulated IoT environment, and analyzed the performance of the model. Further, a web-based interface to the IoT system is developed to demonstrate the working of the blockchain.
Inductive Granger Causal Modeling for Multivariate Time Series

Granger causal modeling is an emerging topic that can uncover Granger causal relationship behind multivariate time series data. In many real-world systems, it is common to encounter a large amount of multivariate time series data collected from different individuals with sharing commonalities. However, there are ongoing concerns regarding Granger causality's applicability in such large scale complex scenarios, presenting both challenges and opportunities for Granger causal structure reconstruction. Existing methods usually train a distinct model for each individual, suffering from inefficiency and over-fitting issues. To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals. In particular, we train one global model for individuals with different Granger causal structures through a novel attention mechanism, called prototypical Granger causal attention. The model can detect common causal structures for different individuals and infer Granger causal structures for newly arrived individuals. Extensive experiments, as well as an online A/B test on an E-commercial advertising platform, demonstrate the superior performances of InGRa.
LarvaNet: Hierarchical Super-Resolution via Multi-exit Architecture

In recent years, image super-resolution (SR) methods using convolutional neural networks (CNNs) have achieved successful results. Nevertheless, it is often difficult to apply them in resource-constrained environments due to the requirement of heavy computation and huge storage capacity. To address this issue, we propose an efficient network model for SR, called LarvaNet. First, we investigate a number of architectural factors for a baseline model and find optimal settings in terms of performance, number of parameters, and running time. Based on that, we design our model using a multi-exit architecture. Our experiments show that the proposed method achieves state-of-the-art SR performance with a reasonable number of parameters and running time. We also show that the multi-exit architecture of the proposed model allows us to control the trade-off between resource consumption and SR performance by selecting which exit point to be used.
On the Cryptanalysis of a Latin Cubes-Based Image Cryptosystem

Based on orthogonal Latin cubes, an image cryptosystem with confusion–diffusion–confusion cipher architecture has been proposed recently (Inf. Sci.2019, 478, 1–14). However, we find that there are four fatal vulnerabilities in this image cryptosystem, which leave open doors for cryptanalysis. In this paper, we propose a reference-validation inference algorithm and design screening-based rules to efficiently break the image cryptosystem. Compared with an existing cryptanalysis algorithm, the proposed method requires fewer pairs of chosen plain-cipher images, and behaves stably since different keys, positions of chosen bits and contents of plain images will not affect the cryptanalysis performance. Experimental results show that our cryptanalysis algorithm only requires 8×H×W3+3 pairs of chosen plain-cipher images, where H×W represents the image’s resolution. Comparative studies demonstrate effectiveness and superiority of the proposed cryptanalysis algorithm.
A new COVID-19 medical image steganography based on dual encrypted data insertion into minimum mean intensity window of LSB of X-ray scans

play a very important role in the prevention and treatment of this disease.These scans include X-rays and CT scans of the chest of the affected person.Nowadays, its a pandemic situation where people are very much worried about social distancing.In this scenario, it is like a social stigma to be affected by the disease.Hence, it is mandatory to secure the personal data of the patient affected and if necessary securely transmit it over networks.This is not only for COVID-19 but is a necessary prerequisite for any type of medical data like ECG, EEG, EMG, PET scans, MRI scans, and lots more.The patient details should always be kept confidential.This is where cryptography and steganography plays a major role.
Representing Real-Time Multi-User Collaboration in Visualizations

Establishing common ground and maintaining shared awareness amongst participants is a key challenge in collaborative visualization. For real-time collaboration, existing work has primarily focused on synchronizing constituent visualizations - an approach that makes it difficult for users to work independently, or selectively attend to their collaborators' activity. To address this gap, we introduce a design space for representing synchronous multi-user collaboration in visualizations defined by two orthogonal axes: situatedness, or whether collaborators' interactions are overlaid on or shown outside of a user's view, and specificity, or whether collaborators are depicted through abstract, generic representations or through specific means customized for the given visualization. We populate this de-sign space with a variety of examples including generic and custom synchronized cursors, and user legends that collect these cursors together or reproduce collaborators' views as thumbnails. To build common ground, users can interact with these representations by peeking to take a quick look at a collaborator's view, tracking to follow along with a collaborator in real-time, and forking to independently explore the visualization based on a collaborator's work. We present a reference implementation of a wrapper library that converts interactive Vega-Lite charts into collaborative visualizations. We find that our approach affords synchronous collaboration across an expressive range of visual designs and interaction techniques.
Pearson Correlation Coefficient Based Attribute Weighted k-NN for Air Pollution Prediction

This paper proposes a formula based on Pearson Correlation Coefficient for the calculation of weights of attributes. These weights are used in a version of k-Nearest Neighbor (k-NN) method called Attribute Weighted k-NN with the aim to achieve better performance than simple k-NN and Distance Weighted k-NN by eliminating the dominating effect of irrelevant attributes while calculating the distance between instances. The proposed formula of weights computation in Attribute Weighted k-NN also performs better than when weights are assigned the same as Pearson Correlation Coefficient values. Weights computed using the proposed formula decrease the Root Mean Square Error (RMSE) by 9.26%, 2.63% and 2.51% and increase the R-Squared by 1.36%, 0.25% and 0.2% for NO, NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and NOX, respectively, in comparison to the weights assigned the same as Pearson Correlation Coefficient values in Attribute Weighted k-NN. This paper has also been able to reduce the execution time of Attribute Weighted k-NN and make it near to that of simple k-NN and Distance Weighted k-NN. Before applying k-NN, data preprocessing steps like Data Cleaning (Missing Values and Noisy Data Removal) and Attribute Reduction are applied to the data to transform it into an efficient and useful format.
Developing UNYSA Chatbot as Information Services about Yogyakarta State University

Abstract This study aims to (1) develop chatbot-based public relations communication media that are able to overcome the limitations of service in interacting and providing information about UNY to the public, (2) ensure the quality of software developed with the ISO/IEC 25010 model. The research method used is Research and Development (R&amp;D) with the Agile: Scrum software development model. The results of this study are (1) a chatbot-based PR communication media named UNYSA as an information service about UNY, (2) UNYSA meets ISO/IEC 25010 quality standards on functional suitability characteristics with software developer experts at 0.86 (most features successfully implemented), material experts of 98.82% (Very Eligible), UAT of 83.95% (Very Good); performance efficiency has a latency response of 15 ms (faster than standard speed: 2 seconds); 100% portability (supports all planned frontend apps); usability of 83.81% (Very Eligible); reliability of 92% (Very Feasible), and maintainability with a maintainability index of 83.21 (very easy to maintain).
Shadow Detection Of Moving Objects In Traffic Monitoring Video

Moving object detection is an important application of computer vision. Commonly used foreground separation algorithms such as Gaussian mixture modeling, ViBe, frame difference method, etc., do not consider the color of shadow and recognize the shadow of a moving object as a part of the moving object. In many cases, the shadow detection effect is not good. Focus on the detection of moving object shadows in traffic surveillance videos, this paper improves the existing ViBe algorithm, considers the color characteristics of the shadows, recognizes the shadows as part of the background, gains a smaller amount of calculation and better effect of shadow detection with the advantages of ViBe.