Reviewerly/exHarmony huggingface dataset:
Hierarchical multilevel framework using RDWT-QR optimized watermarking in telemedicine

A hierarchical multilevel medical image watermarking framework is introduced in this paper. First, robust watermarking is performed to verify the copyright of the ownership. Later, an improved fragile watermarking is implemented on the robust watermarked image to achieve tamper localization along with self-recovery capability. The robust insertion is also optimized with the Particle Swarm Bacterial Foraging Optimization algorithm to ensure improved robustness and imperceptibility metrics of watermarking. During robust watermarking, multiple watermarks are concealed in the first column of upper triangular matrix ‘R,’ of the host image, transformed by a hybrid combination of Redundant Discrete Wavelet Transform–QR. This hybrid combination exploits the redundancy in Redundant Discrete Wavelet Transform to improve the payload capacity, imperceptibility and the low computational QR transform is used to make watermarking scheme free from the false positive problem. First, the cover image is fragmented into blocks of 2 × 2 dimension, and then, on each block, QR decomposition is carried out. The upper triangular matrix pattern is utilized to figure out the tamper localization bits for each block. Fragile watermarking is implemented on the robust watermarked image, in which the watermark is formed by encoding all 2 × 2 blocks with QR coefficients which are hidden into blocks reserved for self recovery. Authentication of watermark bits, which is different for each watermarked image, helps in resisting the block-based attacks such as Vector Quantization, Collage and Constant Average Attacks. In comparison with the existing works in literature, the proposed framework offers high average Peak Signal-to-Noise Ratio and Normalized Cross Correlation in the robust watermarking. In addition to this, excellent results is achieved in the fragile watermarking with an overall tamper detection rate of 99.7% and an overall recovery of 57 dB Peak Signal-to-Noise Ratio for a 50% tampering.
CFD: Communication-Efficient Federated Distillation via Soft-Label Quantization and Delta Coding

Communication constraints are one of the majorchallenges preventing the wide-spread adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new algorithmic paradigm for Federated Learning with fundamentally different communication properties, emerged. FD methods leverage ensemble distillation techniques and exchange model outputs, presented as soft labels on an unlabeled public data set, between the central server and the participating clients. In this work, we investigate FD from the perspective of communication efficiency by analyzing the effects of active distillation-data curation, soft-label quantization, and delta-coding techniques. Based on the insights gathered from this analysis, we present Compressed Federated Distillation (CFD), an efficient Federated Distillation method. Extensive experiments, on Federated image classification and language modeling problems, at different levels of data heterogeneity, demonstrate that our method can reduce the amount of communication necessary to achieve fixed performance targets by more than two orders of magnitude when compared to FD, and by more than four orders of magnitude when compared to parameter averaging based techniques like Federated Averaging.
Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.
Discrete-Time Signatures and Randomness in Reservoir Computing

A new explanation of the geometric nature of the reservoir computing (RC) phenomenon is presented. RC is understood in the literature as the possibility of approximating input-output systems with randomly chosen recurrent neural systems and a trained linear readout layer. Light is shed on this phenomenon by constructing what is called strongly universal reservoir systems as random projections of a family of state-space systems that generate Volterra series expansions. This procedure yields a state-affine reservoir system with randomly generated coefficients in a dimension that is logarithmically reduced with respect to the original system. This reservoir system is able to approximate any element in the fading memory filters class just by training a different linear readout for each different filter. Explicit expressions for the probability distributions needed in the generation of the projected reservoir system are stated, and bounds for the committed approximation error are provided.
Two-Round n-out-of-n and Multi-signatures and Trapdoor Commitment from Lattices

Although they have been studied for a long time, distributed signature protocols have garnered renewed interest in recent years in view of novel applications to topics like blockchains. Most recent works have focused on distributed versions of ECDSA or variants of Schnorr signatures, however, and in particular, little attention has been given to constructions based on post-quantum secure assumptions like the hardness of lattice problems. A few lattice-based threshold signature and multi-signatureschemes have been proposed in the literature, but they either rely on hash-and-sign lattice signatures (which tend to be comparatively inefficient), use expensive generic transformations, or only come with incomplete security proofs.In this paper, we construct several lattice-based distributed signing protocols with low round complexity followingthe Fiat–Shamir with Aborts (FSwA) paradigm of Lyubashevsky (Asiacrypt 2009). Our protocols can be seen as distributed variants of the fast Dilithium-G signature scheme and the full security proof can be made assuming the hardness of module SIS and LWE problems. A key step to achieving security (unexplained in some earlier papers) is to prevent the leakage that can occur when parties abort after their first message—which can inevitably happen in the Fiat–Shamir with Aborts setting. We manage to do so using homomorphic commitments.Exploiting the similarities between FSwA and Schnorr-style signatures, our approach makes the most of observations from recent advancements in the discrete log setting, such as Drijvers et al.’s seminal work on two-round multi-signatures (S&P 2019). In particular, we observe that the use of commitment not only resolves the subtle issue with aborts, but also makes it possible to realize secure two-round n-out-of-n distributed signing and multi-signature in the plain public key model, by equipping the commitment with a trapdoor feature. The construction of suitable trapdoor commitment from lattices is a side contribution of this paper.
COVID-19 Risk Minimization Decision Making Strategy Using Data-Driven Model

In order to establish social resilient and sustainable cities during the pandemic outbreak, it is essential to forecast the epidemic trends and trace infection by means of data-driven solution addressing the requirements of local operational defense applications and global strategies. The smartphone based Digital Proximity Tracing Technology (DPTT) has obtained a great deal of interest with the ongoing COVID-19 pandemic in terms of mitigation, containing and monitoring with the population acceptance insights and effectiveness of the function. The DPTTs and Data-Driven Epidemic Intelligence Strategies (DDEIS) are compared in this paper to identify the shortcomings and propose a novel solution to overcome them. In terms of epidemic resurgence risk minimization, guaranteeing public health safety and quick return of cities to normalcy, a social as well as technological solution may be provided by incorporating the key features of DDEIS. The role of human behavior is taken into consideration while assessing its limitations and benefits for policy making as well as individual decision making. The epidemiological model of SEIR (Susceptible–Exposed–Infectious–Recovered) provides preliminary data for the preferences of users in a DPTT. The impact of the proposed model on the spread dynamics of Covid-19 is evaluated and the results are presented.
A Unified Conditional Disentanglement Framework For Multimodal Brain Mr Image Translation

Multimodal MRI provides complementary and clinically relevant information to probe tissue condition and to characterize various diseases. However, it is often difficult to acquire sufficiently many modalities from the same subject due to limitations in study plans, while quantitative analysis is still demanded. In this work, we propose a unified conditional disentanglement framework to synthesize any arbitrary modality from an input modality. Our framework hinges on a cycleconstrained conditional adversarial training approach, where it can extract a modality-invariant anatomical feature with a modality-agnostic encoder and generate a target modality with a conditioned decoder. We validate our framework on four MRI modalities, including T1-weighted, T1 contrast enhanced, T2-weighted, and FLAIR MRI, from the BraTS'18 database, showing superior performance on synthesis quality over the comparison methods. In addition, we report results from experiments on a tumor segmentation task carried out with synthesized data.
COVID-19, digital health technology and the politics of the unprecedented

The COVID-19 global pandemic has stretched the capacities of public health institutions and health systems around the world, opening the door to a range of technologically-driven solutions. In this article, we seek to historicize the expanding role of digital health technologies and examine the political-economic context from which they have emerged. Drawing on critical insights from science and technology studies, we maintain that the rise of digital health technologies has been catalyzed by broad shifts in global health governance that have expanded the role of market forces in public health and a unique set of political and economic crises that have accelerated the adoption of digital technologies—often under the guise of appeals to technological innovation to address “unprecedented” crises. These interrelated historical trends, we contend, are critical for understanding current state responses to the pandemic and possibilities for more equitable and democratic applications of technology in public health.
Improved Sensor Based Human Activity Recognition via Hybrid Convolutional and Recurrent Neural Networks

Non-intrusive sensor based human activity recognition (HAR) is utilized in a spectrum of applications including fitness tracking devices, gaming, health care monitoring, and smartphone applications. In this paper, we design a multi-layer hybrid architecture with Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM). Based on the exploration of a variety of multi-layer combinations, we present a lightweight, hybrid, and multi-layer model which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model which can achieve a 94.7% activity recognition rate on a benchmark dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between accuracy and efficiency.
A comparative study of multiple instance learning methods for cancer detection using T-cell receptor sequences

As a branch of machine learning, multiple instance learning (MIL) learns from a collection of labeled bags, each containing a set of instances. The learning process is weakly supervised due to ambiguous instance labels. Since its emergence, MIL has been applied to solve various problems including content-based image retrieval, object tracking/detection, and computer-aided diagnosis. In biomedical research, the use of MIL has been focused on medical image analysis and molecule activity prediction. We review and apply 16 methods to investigate the applicability of MIL to a novel biomedical application, cancer detection using T-cell receptor (TCR) sequences. This important application can be a viable approach for large-scale cancer screening, as TCRs can be easily profiled from a subject’s peripheral blood. We consider two feasible data-generating mechanisms, and for the purpose of performance evaluation, we simulate data under each mechanism, where we vary potentially important factors to mimic realistic situations. We also apply the methods to sequencing data of ten cancer types from The Cancer Genome Atlas, as an early proof of concept for distinguishing tumor patients from healthy individuals via TCR sequencing of peripheral blood. We find that given an appropriate MIL method is used, satisfactory performance with Area Under the Receiver Operating Characteristic Curve above 80% can be achieved for five in the ten cancers. Based on our numerical results, we make suggestions about selection of a proper method and avoidance of any method with poor performance. We further point out directions of future research as well as identify a pressing need of new MIL methodologies for improved performance (for some cancer types) and more explainable outcomes.
Semantic-Guided Class-Imbalance Learning Model for Zero-Shot Image Classification

In this article, we focus on the task of zero-shot image classification (ZSIC) that equips a learning system with the ability to recognize visual images from unseen classes. In contrast to the traditional image classification, ZSIC more easily suffers from the class-imbalance issue since it is more concerned with the class-level knowledge transferring capability. In the real world, the sample numbers of different categories generally follow a long-tailed distribution, and the discriminative information in the sample-scarce seen classes is hard to transfer to the related unseen classes in the traditional batch-based training manner, which degrades the overall generalization ability a lot. To alleviate the class-imbalance issue in ZSIC, we propose a sample-balanced training process to encourage all training classes to contribute equally to the learned model. Specifically, we randomly select the same number of images from each class across all training classes to form a training batch to ensure that the sample-scarce classes contribute equally as those classes with sufficient samples during each iteration. Considering that the instances from the same class differ in class representativeness, we further develop an efficient semantic-guided feature fusion model to obtain the discriminative class visual prototype for the following visual-semantic interaction process via distributing different weights to the selected samples based on their class representativeness. Extensive experiments on three imbalanced ZSIC benchmark datasets for both traditional ZSIC and generalized ZSIC tasks demonstrate that our approach achieves promising results, especially for the unseen categories that are closely related to the sample-scarce seen categories. Besides, the experimental results on two class-balanced datasets show that the proposed approach also improves the classification performance against the baseline model.
A new chaotic map with large chaotic band for a secured image cryptosystem

A chaos based cryptosystem requires a chaotic map with complex nonlinearity and large chaotic regime for the generation of secret key. The purpose of this research is to enhance the robustness of a cryptosystem that lies not only in the design of the algorithm, but also the strength of the secret key used. This paper presents the construction and analysis of a new one-dimensional chaotic map, named as the JoanS-MuraliP’s (JSMP) map. The novelty of the map lies in the exhibition of a large complex chaotic band. Further, to investigate the efficacy of the map in cryptosystem, various performance metrics are examined and compared with the existing one-dimensional chaotic maps. The results prove that the proposed map is a promising one for a chaos based cryptosystem that can withstand various attacks. • Construction of a new one-dimensional chaotic map. • Map is named as JoanS-MuraliP’s (JSMP) map. • Dynamic analysis of the JSMP map is performed. • Presence of large complex chaotic regime detected. • Performance metrics of the map prove its suitability for cryptosystem.
Context-Aware Graph Inference With Knowledge Distillation for Visual Dialog

Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relational inference in a graphical model with sparse contextual subjects (nodes) and unknown graph structure (relation descriptor); how to model the underlying context-aware relational inference is critical. To this end, we propose a novel context-aware graph (CAG) neural network. We focus on the exploitation of fine-grained relational reasoning with object-level dialog-historical co-reference nodes. The graph structure (relation in dialog) is iteratively updated using an adaptive top- K message passing mechanism. To eliminate sparse useless relations, each node has dynamic relations in the graph (different related K neighbor nodes), and only the most relevant nodes are attributive to the context-aware relational graph inference. In addition, to avoid negative performance caused by linguistic bias of history, we propose a pure visual-aware knowledge distillation mechanism named CAG-Distill, in which image-only visual clues are used to regularize the joint dialog-historical contextual awareness at the object-level. Experimental results on VisDial v0.9 and v1.0 datasets show that both CAG and CAG-Distill outperform comparative methods. Visualization results further validate the remarkable interpretability of our graph inference solution.
Designers, the Stage Is Yours! Medium-Fidelity Prototyping of Augmented &amp; Virtual Reality Interfaces with 360theater

While augmented and virtual reality technologies are becoming mainstream, it is still technically challenging and time-consuming to create new applications. Many designers draw from traditional low-fidelity prototyping methods that do not lend themselves well to designing in 3D. Developers use high-end programming frameworks such as Unity and Unreal which require significant hardware/software setups and coding skills. We see a gap in the medium-fidelity range where there is an opportunity for new tools to leverage the advantages of 360° content for AR/VR prototyping. Existing tools, however, have only limited support for 3D geometry, spatial and proxemic interactions, puppeteering, and storytelling. We present 360theater, a new method and a tool for rapid prototyping of AR/VR experiences, which takes dioramas into the virtual realm by enhancing 360° video capture with 3D geometry and simulating spatial interactions via Wizard of Oz. Our comparative evaluation of techniques with novice and experienced AR/VR designers shows that 360theater can close the gap and achieve a higher fidelity and more realistic AR/VR prototypes than comparable methods.
An Ensemble Broad Learning Scheme for Semisupervised Vehicle Type Classification

Nowadays vehicle type classification is a fundamental part of intelligent transportation systems (ITSs) and is widely used in various applications like traffic flow monitoring, security enforcement, and autonomous driving, etc. However, vehicle classification is usually used in supervised learning, which greatly limits the applicability for real ITS. This article proposes a semisupervised vehicle type classification scheme via ensemble broad learning for ITS. This presented method contains two main parts. In the first part, a collection of base broad learning system (BLS) classifiers is trained by semisupervised learning to avoid time-consuming training process and alleviate the increasingly unlabeled samples burden. In the second part, a dynamic ensemble structure constructed by trained classifier groups with different characteristics obtains the highest type probability and determine which the vehicle belongs, so as to achieve superior generalization performance than a single base classifier. Several experiments conducted on the pubic BIT-Vehicle dataset and MIO-TCD dataset demonstrate that the proposed method outperforms single BLS classifier and some mainstream methods on effectiveness and efficiency.
Neural architecture search for deep image prior

We present a neural architecture search (NAS) technique to enhance image denoising, inpainting, and super-resolution tasks under the recently proposed Deep Image Prior (DIP). We show that evolutionary search can automatically optimize the encoder-decoder (E-D) structure and meta-parameters of the DIP network, which serves as a content-specific prior to regularize these single image restoration tasks. Our binary representation encodes the design space for an asymmetric E-D network that typically converges to yield a content-specific DIP within 10--20 generations using a population size of 500. The optimized architectures consistently improve upon the visual quality of classical DIP for a diverse range of photographic and artistic content.
PSTR: Per-Title Encoding Using Spatio-Temporal Resolutions

Current per-title encoding schemes encode the same video content (or snippets/subsets thereof) at various bitrates and spatial resolutions to find an optimal bitrate ladder for each video content. Compared to traditional approaches, in which a predefined, content-agnostic ("fit-to-all") encoding ladder is applied to all video contents, per-title encoding can result in (i) a significant decrease of storage and delivery costs and (ii) an increase in the Quality of Experience (QoE). In the current per-title encoding schemes, the bitrate ladder is optimized using only spatial resolutions, while we argue that with the emergence of high framerate videos, this principle can be extended to temporal resolutions as well. In this paper, we improve the per-title encoding for each content using spatio-temporal resolutions. Experimental results show that our proposed approach doubles the performance of bitrate saving by considering both temporal and spatial resolutions compared to considering only spatial resolutions.