Reviewerly/exHarmony huggingface dataset:
Adaptive Gaussian notch filter for removing periodic noise from digital images

Periodic noise corrupts digital images during acquisition and transmission stages by adding repetitive patterns. This study introduces a new adaptive Gaussian notch filter (AGNF) in Fourier transform domain for effectively restoring images contaminated with periodic, quasi-periodic and Moiré pattern noises. Since periodic noises are sinusoidal functions added to the uncorrupted images, Fourier transform of images make these noisy functions to concentrate as easily distinguishable conjugate peaks in frequency domain. The proposed AGNF effectively identifies the noisy peak positions and adaptively quantifies the associated noisy areas by analysing the ratio of averages of frequencies from adaptively varying neighbourhoods. These identified noisy peaks are then diffused by Gaussian notch filter of adaptively varying sizes. The proposed filter ensures maximum diffusion of identified noisy peak areas by maintaining the minimum frequency values from the outputs of overlapping notch filters. Visual and quantitative experimental analysis of the proposed algorithm with mean absolute error, peak signal-to-noise ratio, mean structural similarity index measure and computation time reveals that AGNF is better in restoring images contaminated with periodic noises when compared to other methods.
DeepSplit: Dynamic Splitting of Collaborative Edge-Cloud Convolutional Neural Networks

CNNs (Convolutional Neural Networks) can have a large number of parameters, thereby having high storage and computational requirements. These requirements are not typically satisfied by resource-constrained edge devices. Thus, current industry practice for making decisions at edge include transferring visual data from edge to cloud nodes, making prediction on that data with a CNN processed in the cloud and return the output to edge devices. There are two problems with this approach - Sending visual data from edge to cloud requires high bandwidth between edge and cloud, and we are not making use of the computational resources available at edge. One solution to this problem is to split the CNN between edge and cloud. The efficient way to split CNN has yet to be investigated in detail. In this paper, we propose a novel CNN splitting algorithm that efficiently splits CNN between edge and cloud with the sole objective of reducing bandwidth consumption. We consider various parameters such as task load at edge, input image dimensions and bandwidth constraints in order to choose the best splitting layer. Through experiments, we show that to optimize our objective function, CNN splitting should only be made at layers whose output dimensions are lower than input image dimensions. A random partitioning of layers between edge and cloud might result in increased bandwidth consumption. The algorithm proposed in this paper dynamically chooses the best CNN splitting layer and moves CNN layers between edge and cloud as and when required, thus allowing multitasking at edge while optimizing bandwidth consumption. We are able to perform such tasks without any loss of prediction accuracy since we do not modify the pretrained CNN architecture that we use.
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models

This paper proposes a method for detecting anomalies in video data.A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles.An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences.Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.
Deep generative variational autoencoding for replay spoof detection in automatic speaker verification

Automatic speaker verification (ASV) systems are highly vulnerable to presentation attacks, also called spoofing attacks. Replay is among the simplest attacks to mount — yet difficult to detect reliably. The generalization failure of spoofing countermeasures (CMs) has driven the community to study various alternative deep learning CMs. The majority of them are supervised approaches that learn a human-spoof discriminator. In this paper, we advocate a different, deep generative approach that leverages from powerful unsupervised manifold learning in classification. The potential benefits include the possibility to sample new data, and to obtain insights to the latent features of genuine and spoofed speech. To this end, we propose to use variational autoencoders (VAEs) as an alternative backend for replay attack detection, via three alternative models that differ in their class-conditioning. The first one, similar to the use of Gaussian mixture models (GMMs) in spoof detection, is to train independently two VAEs — one for each class. The second one is to train a single conditional model (C-VAE) by injecting a one-hot class label vector to the encoder and decoder networks. Our final proposal integrates an auxiliary classifier to guide the learning of the latent space. Our experimental results using constant-Q cepstral coefficient (CQCC) features on the ASVspoof 2017 and 2019 physical access subtask datasets indicate that the C-VAE offers substantial improvement in comparison to training two separate VAEs for each class. On the 2019 dataset, the C-VAE outperforms the VAE and the baseline GMM by an absolute 9 - 10% in both equal error rate (EER) and tandem detection cost function (t-DCF) metrics. Finally, we propose VAE residuals — the absolute difference of the original input and the reconstruction as features for spoofing detection. The proposed frontend approach augmented with a convolutional neural network classifier demonstrated substantial improvement over the VAE backend use case.
A Flexible and Generic Gaussian Sampler With Power Side-Channel Countermeasures for Quantum-Secure Internet of Things

Post-quantum cryptography (PQC) great potential in providing reliable communication security for Internet-of-Things (IoT) devices against the quantum computer in the future. The Gaussian sampler is a crucial part in lattice-based post-quantum cryptosystems, thus being the most vulnerable module to side-channel attack as well. However, research on the countermeasures for the Gaussian sampler against power side-channel attacks is almost blank. In this article, a flexible and generic cumulative distribution table (CDT)-based Gaussian sampler using the hardware-software approach is proposed. The proposed CDT sampler has an AHB interface and can be reconfigured to support various parameter sets, while utilizing just 77 Slices on a Xilinx Spartan-6 FPGA with constant response time. Additionally, the first simple power analysis (SPA) attack on the CDT sampler is presented. The presented attack mainly takes advantage of the chosen input and the SPA vulnerability associated with the binary search method, hence the attacker is able to recover every sampled value by comparing a few pairs of power consumption traces. To further protect against chosen input SPA attack, this article identifies the vulnerability associated with three main operations in every binary search state and construct an effective countermeasure based on randomization at the cost of only extra 58.4% Slices. Compared to other related works, the merits of the proposed CDT sampler are the high hardware flexibility, side-channel security, and suitability for resource-constrained IoT nodes.
Virtualization vs. Containerization: Towards a Multithreaded Performance Evaluation Approach

Increasingly, hardware visualizations are becoming the norm. This opened the door for new possibilities but placed a new set of challenges that are to be faced by software developers, mainly when it comes to developing different applications in different programming languages and deploying them on various environments; often virtually hosted environments. The performance expectations and requirements might be easily affected by the settings of these virtual machines. This paper aims at evaluating the performance of these virtual environments and measure their impacts on the performance of multithreaded applications. In particular, we evaluate the performance of multithreaded applications and assess how it is influenced by the various virtualization technologies. A multithreaded application is developed in both C and Java and the performance is measured on different platforms; each of which is supported by either a virtualized system or a containerized environment. Certainly, C will keep outperforming Java in its execution speed; however, simple variations in the virtualization technique easily affects the performance of these multithreaded applications; a situation that should be taken into consideration by the software developers when deploying into such environments.
Automatic highlighting of the region of interest in computed tomography images of the lungs

This article discusses the creation of masks for highlighting the lungs in computed tomography images using three methods – the Otsu method, a simple convolutional neural network consisting of 10 identical layers, and the convolutional neural network U-Net. We perform a study and comparison of methods used for automatically highlighting the region of interest (ROI) in computed tomography images of the lungs, which were provided as a courtesy from the Clinics of Samara State Medical University. The solution to this problem is relevant, because medical workers have to manually select the ROI as the first step of the automated processing of lung CT images. An algorithm for post-processing images based on the search for contours, which allows one to improve the quality of segmentation, is proposed. It is concluded that the U-Net highlights the ROI relating to the lung better than the other two methods. At the same time, the simple convolutional neural network highlights the ROI with an accuracy of 97.5%, which is better than the accuracy of 96.7% of the Otsu method and 96.4% of the U-Net.
Encrypted Cloud-based Control using Secret Sharing with One-time Pads

Encrypted control enables confidential controller evaluations in cloud-based or networked control systems. Technically, an encrypted controller is a modified control algorithm that is capable of computing encrypted control actions based on encrypted system states without intermediate decryption. Up to now, the key technology to realize encrypted control is homomorphic encryption that allows simple mathematical operations to be carried out on encrypted data. While homomorphic cryptosystems are tailored for encrypted control, they are also numerically demanding. In this paper, we show that encrypted cloud-based control can also be realized by significantly less demanding methods if multiple clouds and secret sharing are considered. More precisely, we demonstrate that encrypted linear (or affine) state feedback can be efficiently implemented using two independent clouds and a tailored encryption scheme inspired by one-time pads. We further discuss extensions to nonlinear control schemes.
Combining Learning and Model Based Multivariable Control

Artificial neural networks have traditionally been used to implement machine learning algorithms. There are, however, alternatives to these biologically inspired machine learning architectures that offer potentially lower complexity and stronger theoretical underpinnings. One such option in the context of control is based on using a generic input-output model known as a Chen-Fliess functional series. The main goal of the paper is to describe a specific architecture that can be used in the multivariable setting to combine both learning and model based control. It builds on recent work by the authors showing that a certain monoid structure underlies any recursive implementation of such a system. The method is demonstrated using a two-input, two-output Lotka-Volterra system.
Planning-based security testing of web applications with attack grammars

Abstract Web applications are deployed on machines around the globe and offer almost universal accessibility. These applications assure functional interconnectivity between different components on a 24/7 basis. One of the most important requirements is data confidentiality and secure authentication. However, implementation flaws and unfulfilled requirements often result in security leaks that malicious users eventually exploited. In this context, the application of different testing methods is of utmost importance in order to detect software defects during development and to prevent unauthorized access in advance. In this paper, we contribute to test automation for web applications. In particular, we focus on using planning for testing where we introduce underlying models covering attacks and their use in testing of web applications. The planning model offers a high degree of extendibility and configurability and as well overcomes limits of traditional graphical representations. New testing possibilities emerge that eventually lead to better vulnerability detection, therefore ensuring more secure web services and applications.
Design and implementation of a head motion-controlled semi-autonomous wheelchair for quadriplegic patients based on 3-axis accelerometer

Quadriplegic patients are affected by paralysis of all four limbs, and therefore they cannot move without the assistance of other people. Fortunately, they can move their heads, and it could be a way to control the navigation of a wheelchair. To assist the paraplegic patients, a head motion-controlled semi-autonomous wheelchair is proposed in this paper. This wheelchair can help any paraplegic patients as well as disable persons to move indoor or outdoor places without the assistance of any other people. Here a 3-axis accelerometer sensor is used to detect the head movement, and based on the movement of head; two DC motors are controlled to navigate the wheelchair. The system also has two sonar sensors to detect the obstacle in the front or back direction of the wheelchair to avoid clash or accident. In case of an accident, it has the ability to detect the collision and to inform the family member by sending an SMS through GSM modem with location information. The detailed methodology to develop a prototype of the proposed wheelchair, the performances of the prototype, and the advantages of this wheelchair compared to the existing systems are presented in this paper.
A chaos-based image encryption algorithm based on multiresolution singular value decomposition and a symmetric attractor

This paper proposes a new image cryptosystem using multiple chaotic maps and multiresolution singular value decomposition (MR-SVD). The encryption process starts with implementing the MR-SVD to decompose the original image into the four fundamental sub-bands, i.e., Approximation (A), Vertical (V), Horizontal (H), Diagonal (D) sub-bands respectively. Since the approximation part gives the most information about the image, this sub-band is selected to perform permutation and diffusion. The permutation of all the four sub-bands is done by deploying the Baker map. The diffusion of pixels in the permuted approximation part is done by our neighbourhood diffusion scheme that uses the numerical solution of the chaotic Thomas' cyclically symmetric attractor. The final cipher is obtained by combining all the four (A, H, V, D) partial ciphers by performing inverse MR-SVD. The experimental results of our proposed scheme on various benchmarks tests indicate that the algorithm is highly secure and can withstand various attacks.
Domain-Guided Task Decomposition with Self-Training for Detecting Personal Events in Social Media

Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons. To reduce the burden of creating extensive labeled data and improve classification performance, we propose to perform these tasks in two steps: 1. Decomposing the task into domain-specific sub-tasks by identifying key concepts, thus utilizing human domain understanding; and 2. Combining the results of learners for each key concept using co-training to reduce the requirements for labeled training data. We empirically show the effectiveness and generality of our approach, Co-Decomp, using three representative social media mining tasks, namely Personal Health Mention detection, Crisis Report detection, and Adverse Drug Reaction monitoring. The experiments show that our model is able to outperform the state-of-the-art text classification models–including those using the recently introduced BERT model–when small amounts of training data are available.
Using federated data sources and Varian Learning Portal framework to train a neural network model for automatic organ segmentation

Purpose In this study we trained a deep neural network model for female pelvis organ segmentation using data from several sites without any personal data sharing. The goal was to assess its prediction power compared with the model trained in a centralized manner. Methods Varian Learning Portal (VLP) is a distributed machine learning (ML) infrastructure enabling privacy-preserving research across hospitals from different regions or countries, within the framework of a trusted consortium. Such a framework is relevant in the case when there is a high level of trust among the participating sites, but there are legal restrictions which do not allow the actual data sharing between them. We trained an organ segmentation model for the female pelvic region using the synchronous data distributed framework provided by the VLP. Results The prediction performance of the model trained using the federated framework offered by VLP was on the same level as the performance of the model trained in a centralized manner where all training data was pulled together in one centre. Conclusions VLP infrastructure can be used for GPU-based training of a deep neural network for organ segmentation for the female pelvic region. This organ segmentation instance is particularly difficult due to the high variation in the organs’ shape and size. Being able to train the model using data from several clinics can help, for instance, by exposing the model to a larger range of data variations. VLP framework enables such a distributed training approach without sharing protected health information.
A Survey on SVM Hyper-Parameters Optimization Techniques

Support vector machines can be considered as one of the most powerful classifiers. They are parameterized models build upon the support vectors extracted during the training phase. One of the crucial tasks in the modeling of SVM is to select optimal values for its hyper-parameters, because the effectiveness and efficiency of SVM depend upon these parameters. This task of tuning the values for the SVM hyper-parameters is called as the SVM model selection problem. Till now a lot of techniques have been proposed for optimizing values of hyper-parameters of SVM both in static and dynamic environment. Static environment is one where the knowledge about a problem does not change over time due to which static optimal values can be assigned to the hyper-parameters. On the other hand, due to the changing nature of the knowledge about a problem, in dynamic environment the optimization process has to be flexible enough to adapt the changes quickly. In dynamic environment, re-evaluation of the optimal values of the hyper-parameters is needed. This paper attempts to identify various optimization techniques used for SVM hyper-parameters tuning and recognize their pros and cons.
Legal document similarity: a multi-criteria decision-making perspective

The vast volume of documents available in legal databases demands effective information retrieval approaches which take into consideration the intricacies of the legal domain. Relevant document retrieval is the backbone of the legal domain. The concept of relevance in the legal domain is very complex and multi-faceted. In this work, we propose a novel approach of concept based similarity estimation among court judgments. We use a graph-based method, to identify prominent concepts present in a judgment and extract sentences representative of these concepts. The sentences and concepts so mined are used to express/visualize likeness among concepts between a pair of documents from different perspectives. We also propose to aggregate the different levels of matching so obtained into one measure quantifying the level of similarity between a judgment pair. We employ the ordered weighted average (OWA) family of aggregation operators for obtaining the similarity value. The experimental results suggest that the proposed approach of concept based similarity is effective in the extraction of relevant legal documents and performs better than other competing techniques. Additionally, the proposed two-level abstraction of similarity enables informative visualization for deeper insights into case relevance.
The Fast and The Frugal: Tail Latency Aware Provisioning for Coping with Load Variations

Small and medium sized enterprises use the cloud for running online, user-facing, tail latency sensitive applications with well-defined fixed monthly budgets. For these applications, adequate system capacity must be provisioned to extract maximal performance despite the challenges of uncertainties in load and request-sizes. In this paper, we address the problem of capacity provisioning under fixed budget constraints with the goal of minimizing tail latency.
Overcoming Impossibility Results in Composable Security Using Interval-Wise Guarantees

Composable security definitions, at times called simulation-based definitions, provide strong security guarantees that hold in any context. However, they are also met with some skepticism due to many impossibility results; goals such as commitments and zero-knowledge that are achievable in a stand-alone sense were shown to be unachievable composably (without a setup) since provably no efficient simulator exists. In particular, in the context of adaptive security, the so-called "simulator commitment problem" arises: once a party gets corrupted, an efficient simulator is unable to be consistent with its pre-corruption outputs. A natural question is whether such impossibility results are unavoidable or only artifacts of frameworks being too restrictive. In this work, we propose a novel type of composable security statement that evades the commitment problem. Our new type is able to express the composable guarantees of schemes that previously did not have a clear composable understanding. To this end, we leverage the concept of system specifications in the Constructive Cryptography framework, capturing the conjunction of several interval-wise guarantees, each specifying the guarantees between two events. We develop the required theory and present the corresponding new composition theorem. We present three applications of our theory. First, we show in the context of symmetric encryption with adaptive corruption how our notion naturally captures the expected confidentiality guarantee—the messages remain confidential until either party gets corrupted—and that it can be achieved by any standard semantically secure scheme (negating the need for non-committing encryption). Second, we present a composable formalization of (so far only known to be standalone secure) commitment protocols, which is instantiable without a trusted setup like a CRS. We show it to be sufficient for being used in coin tossing over the telephone, one of the early intuitive applications of commitments. Third, we reexamine a result by Hofheinz, Matt, and Maurer [Asiacrypt'15] implying that IND-ID-CPA security is not the right notion for identity-based encryption, unmasking this claim as an unnecessary framework artifact.