Reviewerly/exHarmony huggingface dataset:
Development and analysis of IWT-SVD and DWT-SVD steganography using fractal cover

The assurance of secret data transmitted over the Internet and confining its access to explicit grouped information has become a significant security and protection issue. To hide the presence of such information, a robust and blind color image steganography algorithm, using fractal cover images, Singular Value Decomposition (SVD), Integer Wavelet Transform (IWT), and Discrete Wavelet Transform (DWT) is proposed in this paper. The proposed algorithm conceals the color secret image intelligently inside a fractal cover image thus creating a unique stego-image that is virtually a replica of the original cover image. The novelty of this proposed blind image hiding method lies in the fact that a small file size of a unique fractal image can be used to conceal a large amount of information payload. The fractal cover images can be implemented without debasing its quality to make the shrouded information sufficiently strong against image processing attacks. The algorithm possesses features like robustness, imperceptibility, security, and greater hiding capacity due to the application of fractal compression, DWT-SVD, and IWT-SVD hybrid transform schemes. The secret image is inserted in U and V components of the fractal cover with a scaling factor based on the HVS model and a stego-image with sufficiently large keyspace is created. Furthermore, the proposed work compares the performance analysis and security offered by IWT- SVD and DWT- SVD hybrid combinations for different fractal covers using certain metrics like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structural Similarity Index), BRISQUE (Blind/ Reference less Image Spatial Quality Evaluator) score and Bits per pixel (BPP). The results prove that image degradation is very less (less than 0.6%) in the proposed algorithm even when the secret image file size is more than double the file size of the fractal cover image which also saves the bandwidth and storage requirements.
Pairwise learning for medical image segmentation

Fully convolutional networks (FCNs) trained with abundant labeled data have been proven to be a powerful and efficient solution for medical image segmentation. However, FCNs often fail to achieve satisfactory results due to the lack of labelled data and significant variability of appearance in medical imaging. To address this challenging issue, this paper proposes a conjugate fully convolutional network (CFCN) where pairwise samples are input for capturing a rich context representation and guide each other with a fusion module. To avoid the overfitting problem introduced by intra-class heterogeneity and boundary ambiguity with a small number of training samples, we propose to explicitly exploit the prior information from the label space, termed as proxy supervision. We further extend the CFCN to a compact conjugate fully convolutional network (C2FCN), which just has one head for fitting the proxy supervision without incurring two additional branches of decoders fitting ground truth of the input pairs compared to CFCN. In the test phase, the segmentation probability is inferred by the learned logical relation implied in the proxy supervision. Quantitative evaluation on the Liver Tumor Segmentation (LiTS) and Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) datasets shows that the proposed framework achieves a significant performance improvement on both binary segmentation and multi-category segmentation, especially with a limited amount of training data. The source code is available at https://github.com/renzhenwang/pairwise_segmentation.
Multi-user certificateless public key encryption with conjunctive keyword search for cloud-based telemedicine

With the development of communication and information technologies, the telemedicine system has infiltrated many aspects of medicine field. It allows doctors to simultaneously diagnose patients in different areas, which provides great convenience to people. However, the increasing medical data brings serious challenges to people, such as data storage and processing. To reduce data management costs and enjoy convenient services, more and more individuals and medical institutions prefer to store data in the cloud. Recently, many public key encryption with keyword search (PEKS) schemes have been designed to address the security and privacy of the outsourced data. However, most of the existing schemes support only single-keyword search, and cannot suitable for multi-user environment. To resolve these issues, we construct a certificateless public key encryption with conjunctive keyword search scheme for multi-user system (mCLPECK), in which the same data only needs to be encrypted once and can be searched by multi-receiver. At the same time, the mCLPECK scheme supports multi-keyword search. Furthermore, we show the proposed scheme can resist chosen keyword attacks, and has lower storage and computation overhead compared to other related schemes.
Whole mammographic mass segmentation using attention mechanism and multiscale pooling adversarial network

Purpose: Since breast mass is a clear sign of breast cancer, its precise segmentation is of great significance for the diagnosis of breast cancer. However, the current diagnosis relies mainly on radiologists who spend time extracting features manually, which inevitably reduces the efficiency of diagnosis. Therefore, designing an automatic segmentation method is urgently necessary for the accurate segmentation of breast masses. Approach: We propose an effective attention mechanism and multiscale pooling conditional generative adversarial network (AM-MSP-cGAN), which accurately achieves mass automatic segmentation in whole mammograms. In AM-MSP-cGAN, U-Net is utilized as a generator network by incorporating attention mechanism (AM) into it, which allows U-Net to pay more attention to the target mass regions without additional cost. As a discriminator network, a convolutional neural network with multiscale pooling module is used to learn more meticulous features from the masses with rough and fuzzy boundaries. The proposed model is trained and tested on two public datasets: CBIS-DDSM and INbreast. Results: Compared with other state-of-the-art methods, the AM-MSP-cGAN can achieve better segmentation results in terms of the dice similarity coefficient (Dice) and Hausdorff distance metrics, achieving top scores of 84.49% and 5.01 on CBIS-DDSM, and 83.92% and 5.81 on INbreast, respectively. Therefore, qualitative and quantitative experiments illustrate that the proposed model is effective and robust for the mass segmentation in whole mammograms. Conclusions: The proposed deep learning model is suitable for the automatic segmentation of breast masses, which provides technical assistance for subsequent pathological structure analysis.
Cross-Modal Omni Interaction Modeling for Phrase Grounding

Phrase grounding aims to localize the objects described by phrases in a natural language specification. Previous works model the interaction of inputs from text modality and visual modality only in the intra-modal global level and consequently lacks the ability to capture the precise and complete context information. In this paper, we propose a novel Cross-Modal Omni Interaction network (COI Net) composed of a neighboring interaction module, a global interaction module, a cross-modal interaction module and a multilevel alignment module. Our approach formulates the complex spatial and semantic relationship among image regions and phrases through multi-level multi-modal interaction. We capture the local relationship using the interaction among neighboring regions and then collect the global context through the interaction among all regions using a transformer encoder. We further use a co-attention module to apply the interaction between two modalities to gather the cross-modal context for all image regions and phrases. In addition to the omni interaction modeling, we also leverage a straightforward yet effective multilevel alignment regularization to formulate the dependencies among all grounding decisions. We extensively validate the effectiveness of our model. Experiments show that our approach outperforms existing state-of-the-art methods by large margins on two popular datasets in terms of accuracy: 6.15% on Flickr30K Entities (71.36% increased to 77.51%) and 21.25% on ReferItGame (44.91% increased to 66.16%). The code of our implementation is available at https://github.com/yiranyyu/Phrase-Grounding.
Comparative Layouts Revisited: Design Space, Guidelines, and Future Directions

We present a systematic review on three comparative layouts-juxtaposition, superposition, and explicit-encoding-which are information visualization (InfoVis) layouts designed to support comparison tasks. For the last decade, these layouts have served as fundamental idioms in designing many visualization systems. However, we found that the layouts have been used with inconsistent terms and confusion, and the lessons from previous studies are fragmented. The goal of our research is to distill the results from previous studies into a consistent and reusable framework. We review 127 research papers, including 15 papers with quantitative user studies, which employed comparative layouts. We first alleviate the ambiguous boundaries in the design space of comparative layouts by suggesting lucid terminology (e.g., chart-wise and item-wise juxtaposition). We then identify the diverse aspects of comparative layouts, such as the advantages and concerns of using each layout in the real-world scenarios and researchers' approaches to overcome the concerns. Building our knowledge on top of the initial insights gained from the Gleicher et al.'s survey [19], we elaborate on relevant empirical evidence that we distilled from our survey (e.g., the actual effectiveness of the layouts in different study settings) and identify novel facets that the original work did not cover (e.g., the familiarity of the layouts to people). Finally, we show the consistent and contradictory results on the performance of comparative layouts and offer practical implications for using the layouts by suggesting trade-offs and seven actionable guidelines.
Facilitating Temporal Synchronous Target Selection through User Behavior Modeling

Temporal synchronous target selection is an association-free selection technique: users select a target by generating signals (e.g., finger taps and hand claps) in sync with its unique temporal pattern. However, classical pattern set design and input recognition algorithm of such techniques did not leverage users' behavioral information, which limits their robustness to imprecise inputs. In this paper, we improve these two key components by modeling users' interaction behavior. In the first user study, we asked users to tap a finger in sync with blinking patterns with various period and delay, and modeled their finger tapping ability using Gaussian distribution. Based on the results, we generated pattern sets for up to 22 targets that minimized the possibility of confusion due to imprecise inputs. In the second user study, we validated that the optimized pattern sets could reduce error rate from 23% to 7% for the classical Correlation recognizer. We also tested a novel Bayesian, which achieved higher selection accuracy than the Correlation recognizer when the input sequence is short. The informal evaluation results show that the selection technique can be effectively scaled to different modalities and sensing techniques.
Neural network-based modelling of wind/solar farm siting: a case study of East-Azerbaijan

The location of wind/solar power plants is a critical part of design process. Multi-criteria decision making (MCDM), the well-known procedure of site selection, suffers from the local-scoring property. This paper proposes a combined approach of MCDM and artificial neural networks (ANN) to alleviate this deficiency. Here, the weighting of site selection criteria has been performed using the analytic hierarchy process (AHP), and then a multi-layer perceptron (MLP) is used for implementing the global scoring capability. By using this procedure, adding any new alternative site location cannot affect the scores of the others. In other words, the proposed procedure is global-scale and robust. Scores derived by this procedure for two candidate sites can be interpreted as real differences in these sites.
SalGCN

The non-Euclidean geometry characteristic poses a challenge to the saliency prediction for 360-degree images. Since spherical data cannot be projected onto a single plane without distortion, existing saliency prediction methods based on traditional CNNs are inefficient. In this paper, we propose a saliency prediction framework for 360-degree images based on graph convolutional networks (SalGCN), which directly applies to the spherical graph signals. Specifically, we adopt the Geodesic ICOsahedral Pixelation (GICOPix) to construct a spherical graph signal from a spherical image in equirectangular projection (ERP) format. We then propose a graph saliency prediction network to directly extract the spherical features and generate the spherical graph saliency map, where we design an unpooling method suitable for spherical graph signals based on linear interpolation. The network training process is realized by modeling the node regression problem of the input and output spherical graph signals, where we further design a Kullback-Leibler (KL) divergence loss with sparse consistency to make the sparseness of the saliency map closer to the ground truth. Eventually, to obtain the ERP format saliency map for evaluation, we further propose a spherical crown-based (SCB) interpolation method to convert the output spherical graph saliency map into a saliency map in ERP format. Experiments show that our SalGCN can achieve comparable or even better saliency prediction performance both subjectively and objectively, with a much lower computation complexity.
CODAN: Counting-driven Attention Network for Vehicle Detection in Congested Scenes

Although recent object detectors have shown excellent performance for vehicle detection, they are incompetent for scenarios with a relatively large number of vehicles. In this paper, we explore the dense vehicle detection given the number of vehicles. Existing crowd counting methods cannot directly applied for dense vehicle detection due to insufficient description of density map, and the lack of effective constraint for mining the spatial awareness of dense vehicles. Inspired by these observations, a conceptually simple yet efficient framework, called CODAN, is proposed for dense vehicle detection. The proposed approach is composed of three major components: (i) an efficient strategy for generating multi-scale density maps (MDM) is designed to represent the vehicle counting, which can capture the global semantics and spatial information of dense vehicles, (ii) a multi-branch attention module (MAM) is proposed to bridging the gap between object counting and vehicle detection framework, (iii) with the well-designed density maps as explicit supervision, an effective counting-awareness loss (C-Loss) is employed to guide the attention learning by building the pixel-level constrain. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art methods. The impressive results indicate that vehicle detection and counting can be mutually supportive, which is an important and meaningful finding.
Camera identification of multi-format devices

Photo Response Non-Uniformity (PRNU) based source camera attribution is an effective method to determine an image or a video's origin camera. However, modern devices, especially smartphones, capture images and videos at different resolutions using the same sensor array, PRNU attribution can become ineffective as the camera fingerprint and query object can be misaligned. While capturing visual objects (either image or video), cameras may use different in-camera operations as well as they may use different parts of the sensor. In this paper, we investigate the problem of source camera attribution of a visual object by doing a thorough investigation of a comprehensive dataset, NYUAD Mixed Media Dataset. This investigation takes many factors into accounts, such as the fact that visual objects may have been captured using different resolution and aspect ratios. Furthermore, the visual objects may use different regions of the sensor, including the usage of boundary pixels for videos. Taking these various cases into account, we propose an efficient search which not only gives the state-of-the-art results but also performs significantly faster compared to existing methods.
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing

Few-shot learning (FSL) aims at recognizing novel classes given only few training samples, which still remains a great challenge for deep learning. However, humans can easily recognize novel classes with only few samples. A key component of such ability is the compositional recognition that human can perform, which has been well studied in cognitive science but is not well explored in FSL. Inspired by such capability of humans, to imitate humans' ability of learning visual primitives and composing primitives to recognize novel classes, we propose an approach to FSL to learn a feature representation composed of important primitives, which is jointly trained with two parts, i.e. primitive discovery and primitive enhancing. In primitive discovery, we focus on learning primitives related to object parts by self-supervision from the order of image splits, avoiding extra laborious annotations and alleviating the effect of semantic gaps. In primitive enhancing, inspired by current studies on the interpretability of deep networks, we provide our composition view for the FSL baseline model. To modify this model for effective composition, inspired by both mathematical deduction and biological studies (the Hebbian Learning rule and the Winner-Take-All mechanism), we propose a soft composition mechanism by enlarging the activation of important primitives while reducing that of others, so as to enhance the influence of important primitives and better utilize these primitives to compose novel classes. Extensive experiments on public benchmarks are conducted on both the few-shot image classification and video recognition tasks. Our method achieves the state-of-the-art performance on all these datasets and shows better interpretability.
Agile Application of Digital Health Interventions during the COVID-19 Refugee Response

The intersection of digital health platforms and refugee health in the context of the novel 2019 coronavirus disease (COVID-19) has not yet been explored. We discuss the ability of a novel mobile health (mhealth) platform to be effectively adapted to improve health access for vulnerable displaced populations. In a preliminary analysis of 200 Syrian refugee women, we found positive user feedback and uptake of an mhealth application to increase access to preventive maternal and child health services for Syrian refugees under temporary protection in Turkey. Rapid adaptation of this application was successfully implemented during a global pandemic state to perform symptomatic assessment, disseminate health education, and bolster national prevention efforts. We propose that mhealth interventions can provide an innovative, cost-effective, and user-friendly approach to access the dynamic needs of refugees and other displaced populations, particularly during an emerging infectious disease outbreak.
Masked Contrastive Representation Learning for Reinforcement Learning

In pixel-based reinforcement learning (RL), the states are raw video frames, which are mapped into hidden representation before feeding to a policy network. To improve sample efficiency of state representation learning, recently, the most prominent work is based on contrastive unsupervised representation. Witnessing that consecutive video frames in a game are highly correlated, to further improve data efficiency, we propose a new algorithm, i.e., masked contrastive representation learning for RL (M-CURL), which takes the correlation among consecutive inputs into consideration. In our architecture, besides a CNN encoder for hidden presentation of input state and a policy network for action selection, we introduce an auxiliary Transformer encoder module to leverage the correlations among video frames. During training, we randomly mask the features of several frames, and use the CNN encoder and Transformer to reconstruct them based on context frames. The CNN encoder and Transformer are jointly trained via contrastive learning where the reconstructed features should be similar to the ground-truth ones while dissimilar to others. During policy evaluation, the CNN encoder and the policy network are used to take actions, and the Transformer module is discarded. Our method achieves consistent improvements over CURL on 14 out of 16 environments from DMControl suite and 23 out of 26 environments from Atari 2600 Games. The code is available at https://github.com/teslacool/m-curl.
Hierarchical Scene Graph Encoder-Decoder for Image Paragraph Captioning

When we humans tell a long paragraph about an image, we usually first implicitly compose a mental "script'' and then comply with it to generate the paragraph. Inspired by this, we render the modern encoder-decoder based image paragraph captioning model such ability by proposing Hierarchical Scene Graph Encoder-Decoder (HSGED) for generating coherent and distinctive paragraphs. In particular, we use the image scene graph as the "script" to incorporate rich semantic knowledge and, more importantly, the hierarchical constraints into the model. Specifically, we design a sentence scene graph RNN (SSG-RNN) to generate sub-graph level topics, which constrain the word scene graph RNN (WSG-RNN) to generate the corresponding sentences. We propose irredundant attention in SSG-RNN to improve the possibility of abstracting topics from rarely described sub-graphs and inheriting attention in WSG-RNN to generate more grounded sentences with the abstracted topics, both of which give rise to more distinctive paragraphs. An efficient sentence-level loss is also proposed for encouraging the sequence of generated sentences to be similar to that of the ground-truth paragraphs. We validate HSGED on Stanford image paragraph dataset and show that it not only achieves a new state-of-the-art 36.02 CIDEr-D, but also generates more coherent and distinctive paragraphs under various metrics.
“Hi This Is NESTORE, Your Personal Assistant”: Design of an Integrated IoT System for a Personalized Coach for Healthy Aging

In the context of the fourth revolution in healthcare technologies, leveraging monitoring and personalization across different domains becomes a key factor for providing useful services to maintain and promote well-being. This is even more crucial for older people, with aging being a complex multi-dimensional and multi-factorial process which can lead to frailty. The NESTORE project was recently funded by the EU Commission with the aim of supporting healthy older people to sustain their well-being and capacity to live independently. It is based on a multi-dimensional model of the healthy aging process that covers physical activity, nutrition, cognition, and social activity. NESTORE is based on the paradigm of the human-in-the-loop cyber-physical system that, exploiting the availability of Internet of Things technologies combined with analytics in the cloud, provides a virtual coaching system to support healthy aging. This work describes the design of the NESTORE methodology and its IoT architecture. We first model the end-user under several domains, then we present the NESTORE system that, analyzing relevant key-markers, provides coaching activities and personalized feedback to the user. Finally, we describe the validation strategy to assess the effectiveness of NESTORE as a coaching platform for healthy aging.