Reviewerly/exHarmony huggingface dataset:
Selective image encryption method based on dynamic DNA coding and new chaotic map

In order to improve the various security indicators and encryption and decryption speeds of dynamic DNA coding in the field of image encryption, a selective image encryption method based on new chaotic map and dynamic DNA coding is proposed. In this paper, we first constructed a one-dimensional chaotic map with higher Lyapunov exponent and better dynamic behavior, and then improved the local graph structure algorithm to make it better to select the image region, and use the constructed chaotic map generates pseudo-random sequences to dynamically encode and manipulate the selected area of DNA, and finally generate a complete encrypted image. Experimental results show that compared with the existing methods, the proposed algorithm has a sufficiently large key space, better statistics and differential characteristics, and can resist common attacks such as chosen plaintext attacks, noise attacks, and differential attacks. Therefore, the algorithm has higher security, and significantly improves the encryption and decryption speed of dynamic DNA coding in the field of image encryption, which has a broad application prospect in the field of image encryption.
Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.
RelationTrack: Relation-Aware Multiple Object Tracking With Decoupled Representation

Existing online multiple object tracking (MOT) algorithms often consist of two subtasks, detection and re-identification (ReID). In order to enhance the inference speed and reduce the complexity, current methods commonly integrate these double subtasks into a unified framework. Nevertheless, detection and ReID demand diverse features. This issue results in an optimization contradiction during the training procedure. With the target of alleviating this contradiction, we devise a module named Global Context Disentangling (GCD) that decouples the learned representation into detection-specific and ReID-specific embeddings. As such, this module provides an implicit manner to balance the different requirements of these two subtasks. Moreover, we observe that preceding MOT methods typically leverage local information to associate the detected targets and neglect to consider the global semantic relation. To resolve this limitation, we develop a module, referred to as Guided Transformer Encoder (GTE), by combining the powerful reasoning ability of Transformer encoder and deformable attention. Unlike previous works, GTE avoids analyzing all the pixels and only attends to capture the relation between query nodes and a few self-adaptively selected key samples. Therefore, it is computationally efficient. Extensive experiments have been conducted on the MOT16, MOT17 and MOT20 benchmarks to demonstrate the superiority of the proposed MOT framework, namely RelationTrack. The experimental results indicate that RelationTrack has surpassed preceding methods significantly and established a new state-of-the-art performance, e.g., IDF1 of 70.5% and MOTA of 67.2% on MOT20.
Slake: A Semantically-Labeled Knowledge-Enhanced Dataset For Medical Visual Question Answering

Medical visual question answering (Med-VQA) has tremendous potential in healthcare. However, the development of this technology is hindered by the lacking of publicly-available and high-quality labeled datasets for training and evaluation. In this paper, we present a large bilingual dataset, SLAKE, with comprehensive semantic labels annotated by experienced physicians and a new structural medical knowledge base for Med-VQA. Besides, SLAKE includes richer modalities and covers more human body parts than the currently available dataset. We show that SLAKE can be used to facilitate the development and evaluation of Med-VQA systems. The dataset can be downloaded from http://www.med-vqa.com/slake.
Multi-channel convolutional neural network for integration of meteorological and geographical features in solar power forecasting

The forecasting of potential photovoltaic power is essential to investigate suitable regions for power plant installation where high levels of electricity can be produced. However, it remains challenging to integrate the meteorological and geographical features at a regional level into the modeling process of solar forecasting, through which the model trained can be extended to predict at other regions. In particular, regional effects resulting from adjacent topography and weather conditions have rarely been considered in solar energy forecasting. Thus, this paper proposes a multi-channel convolutional neural network that is designed to forecast the monthly photovoltaic power with raster image data representing various regional effects. In particular, the network model with multi-channels allows for training with input data of elevation, solar irradiation, temperature, wind speed, and precipitation in a map format, and output data of corresponding photovoltaic power outputs from 164 sites. The results show that the proposed network model achieves a mean absolute percent error of 8.639%, which outperforms conventional methods such as multiple linear regression (e.g., 16.187%) and artificial neural networks (e.g., 15.991%). This implies that learning regional patterns of both geographical and meteorological features may lead to better performance in solar forecasting, and that the trained model can be applied to other regions—the data of which is not used for the training. Thus, this study may help to identify suitable regions with high electricity potential in a large area.
Cloud Security using Hybrid Cryptography Algorithms

Security in cloud computing is the emerging research issues nowadays. A lot of organization are moving from their traditional data storage to cloud storage, which provides an efficient way to access the data anywhere and anytime. But, the main hitch of organization to use cloud computing is data security. This paper proposed a multilevel cryptography based security system for cloud computing. The model is hybrid approach of symmetric and asymmetric key cryptography algorithms. In this Data Encryption Standard (DES) and RSA are implemented to provide the multilevel of encryption and decryption at both sender and receiver side which increase the security of the cloud storage. This security model gives the transparency to the cloud user as well as cloud service provider in order to reduce the security threats. The proposed model is implemented in java and cloudsim cloud simulator tool. This model increases the data security up to a maximum extant and it takes less time in uploading and downloading the text file as compare to existing system.
Mitotic nuclei analysis in breast cancer histopathology images using deep ensemble classifier

Mitotic nuclei estimation in breast tumour samples has a prognostic significance in analysing tumour aggressiveness and grading system. The automated assessment of mitotic nuclei is challenging because of their high similarity with non-mitotic nuclei and heteromorphic appearance. In this work, we have proposed a new Deep Convolutional Neural Network (CNN) based Heterogeneous Ensemble technique “DHE-Mit-Classifier” for analysis of mitotic nuclei in breast histopathology images. The proposed technique in the first step detects candidate mitotic patches from the histopathological biopsy regions, whereas, in the second step, these patches are classified into mitotic and non-mitotic nuclei using the proposed DHE-Mit-Classifier. For the development of a heterogeneous ensemble, five different deep CNNs are designed and used as base-classifiers. These deep CNNs have varying architectural designs to capture the structural, textural, and morphological properties of the mitotic nuclei. The developed base-classifiers exploit different ideas, including (i) region homogeneity and feature invariance, (ii) asymmetric split-transform-merge, (iii) dilated convolution based multi-scale transformation, (iv) spatial and channel attention, and (v) residual learning. Multi-layer-perceptron is used as a meta-classifier to develop a robust and accurate classifier for providing the final decision. The performance of the proposed ensemble “DHE-Mit-Classifier” is evaluated against state-of-the-art CNNs. The performance evaluation on the test set suggests the superiority of the proposed ensemble with an F-score (0.77), recall (0.71), precision (0.83), and area under the precision-recall curve (0.80). The good generalisation of the proposed ensemble with a considerably high F-score and precision suggests its potential use in the development of an assistance tool for pathologists.
Semantic-Guided Attention Refinement Network for Salient Object Detection in Optical Remote Sensing Images

Although remarkable progress has been made in salient object detection (SOD) in natural scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant challenges due to various spatial resolutions, cluttered backgrounds, and complex imaging conditions, mainly for two reasons: (1) accurate location of salient objects; and (2) subtle boundaries of salient objects. This paper explores the inherent properties of multi-level features to develop a novel semantic-guided attention refinement network (SARNet) for SOD of NSI. Specifically, the proposed semantic guided decoder (SGD) roughly but accurately locates the multi-scale object by aggregating multiple high-level features, and then this global semantic information guides the integration of subsequent features in a step-by-step feedback manner to make full use of deep multi-level features. Simultaneously, the proposed parallel attention fusion (PAF) module combines cross-level features and semantic-guided information to refine the object’s boundary and highlight the entire object area gradually. Finally, the proposed network architecture is trained through an end-to-end fully supervised model. Quantitative and qualitative evaluations on two public RSI datasets and additional NSI datasets across five metrics show that our SARNet is superior to 14 state-of-the-art (SOTA) methods without any post-processing.
House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects

This paper proposes a generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement. A surprising discovery of our research is that a simple non-iterative training process, dubbed component-wise GT-conditioning, is effective in learning such a generator. The iterative generator further allows us to improve a metric of choice via meta-optimization techniques by controlling when to pass which input constraints during iterative refinement. Our qualitative and quantitative evaluation based on the three standard metrics demonstrate that the proposed system makes significant improvements over the current state-of-the-art, even competitive against the ground-truth floorplans, designed by professional architects. Code, model, and data are available at https://ennauata.github.io/houseganpp/page.html.
How escapism leads to behavioral intention in a virtual reality store with background music?

Despite the technological advances, empirical studies to understand how VR influences consumer behavior have been limited to date. This study aims (i) to extend the S(Stimuli)-O (Organism)-R (Response) framework by considering escapism as stimuli and incorporating vividness and telepresence as an organism and (ii) examine the role of calm versus upbeat music tempo in the background. Based on an experimental study of between-groups factorial design with two hundred participants, the results offer support to our conceptual framework. Escapism, which functions as experience, stimulates the consumers’ cognitive and affective state that increase pleasure. Subsequently, the consumers’ sense of pleasure heightens the vividness and presence of the virtual reality store, which positively affects their intentions. The link between presence and behavioral intention is stronger with calm music in the background. On the contrary, the link between arousal and pleasure is stronger when consumers listen to upbeat music in the virtual store.
A Proof-of-Stake public blockchain based pricing scheme for peer-to-peer energy trading

Peer-to-peer (P2P) energy trading allows prosumers to trade energy directly without intermediaries. To provide a payment system and record transaction information, public blockchain is designed to match the decentralized feature of the P2P market. The incentive for nodes outside the microgrid is removed but it is maintained for the prosumers within the microgrid. Therefore the number of miner competitors is limited to decrease the mining difficulty and its power consumption. Proof-of-Stake (PoS) consensus protocol defines the function of blockchain with its mining mechanism. Miners sacrifice part of their stake to compensate for the power losses and reduce the price gap from the traditional prosumer-to-grid trading (Feed-in-tariff). Moreover, the proposed model also contributes to increase the social welfare by improving producers’ income and consumers’ cost-saving through the designed pricing scheme, which eliminates the price gap between buying and selling. Successful mining is encouraged by rewards accordingly. A case study is introduced where a microgrid model with 27 prosumers is tested with the PoS public blockchain-based pricing scheme. The process of model implementation and smart contract creation are specifically demonstrated. Numerical results prove the feasibility and effectiveness of the proposed method.
Extended evaluation of the effect of real and simulated masks on face recognition performance

Face recognition is an essential technology in our daily lives as a contactless and convenient method of accurate identity verification. Processes such as secure login to electronic devices or identity verification at automatic border control gates are increasingly dependent on such technologies. The recent COVID-19 pandemic has increased the focus on hygienic and contactless identity verification methods. The pandemic has led to the wide use of face masks, essential to keep the pandemic under control. The effect of mask-wearing on face recognition in a collaborative environment is currently a sensitive yet understudied issue. Recent reports have tackled this by using face images with synthetic mask-like face occlusions without exclusively assessing how representative they are of real face masks. These issues are addressed by presenting a specifically collected database containing three sessions, each with three different capture instructions, to simulate real use cases. The data are augmented to include previously used synthetic mask occlusions. Further studied is the effect of masked face probes on the behaviour of four face recognition systems-three academic and one commercial. This study evaluates both masked-to-non-masked and masked-to-masked face comparisons. In addition, real masks in the database are compared with simulated masks to determine their comparative effects on face recognition performance.
Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach

Analysis of subjective texts like offensive content or hate speech is a great challenge, especially regarding annotation process. Most of current annotation procedures are aimed at achieving a high level of agreement in order to generate a high quality reference source. However, the annotation guidelines for subjective content may restrict the annotators' freedom of decision making. Motivated by a moderate annotation agreement in offensive content datasets, we hypothesize that personalized approaches to offensive content identification should be in place. Thus, we propose two novel perspectives of perception: group-based and individual. Using demographics of annotators as well as embeddings of their previous decisions (annotated texts), we are able to train multimodal models (including transformer-based) adjusted to personal or community profiles. Based on the agreement of individuals and groups, we experimentally showed that annotator group agreeability strongly correlates with offensive content recognition quality. The proposed personalized approaches enabled us to create models adaptable to personal user beliefs rather than to agreed offensiveness understanding. Overall, our individualized approaches to offensive content classification outperform classic data-centric methods that generalize offensiveness perception and it refers to all six tested models. Additionally, we developed requirements for annotation procedures, personalization and content processing to make the solutions human-centered.
Tail-GNN

The prevalence of graph structures in real-world scenarios enables important tasks such as node classification and link prediction. Graphs in many domains follow a long-tailed distribution in their node degrees, i.e., a significant fraction of nodes are tail nodes with a small degree. Although recent graph neural networks (GNNs) can learn powerful node representations, they treat all nodes uniformly and are not tailored to the large group of tail nodes. In particular, there is limited structural information (i.e., links) on tail nodes, resulting in inferior performance. Toward robust tail node embedding, in this paper we propose a novel graph neural network called Tail-GNN. It hinges on the novel concept of transferable neighborhood translation, to model the variable ties between a target node and its neighbors. On one hand, Tail-GNN learns a neighborhood translation from the structurally rich head nodes (i.e., high-degree nodes), which can be further transferred to the structurally limited tail nodes to enhance their representations. On the other hand, the ties with the neighbors are variable across different parts of the graph, and a global neighborhood translation is inflexible. Thus, we devise a node-wise adaptation to localize the global translation w.r.t. each node. Extensive experiments on five benchmark datasets demonstrate that our proposed Tail-GNN significantly outperforms the state-of-the-art baselines.
Astronomaly: Personalised active anomaly detection in astronomical data

Survey telescopes such as the Vera C. Rubin Observatory and the Square Kilometre Array will discover billions of static and dynamic astronomical sources. Properly mined, these enormous datasets will likely be wellsprings of rare or unknown astrophysical phenomena. The challenge is that the datasets are so large that most data will never be seen by human eyes; currently the most robust instrument we have to detect relevant anomalies. Machine learning is a useful tool for anomaly detection in this regime. However, it struggles to distinguish between interesting anomalies and irrelevant data such as instrumental artefacts or rare astronomical sources that are simply not of interest to a particular scientist. Active learning combines the flexibility and intuition of the human brain with the raw processing power of machine learning. By strategically choosing specific objects for expert labelling, it minimises the amount of data that scientists have to look through while maximising potential scientific return. Here we introduce Astronomaly: a general anomaly detection framework with a novel active learning approach designed to provide personalised recommendations. Astronomaly can operate on most types of astronomical data, including images, light curves and spectra. We use the Galaxy Zoo dataset to demonstrate the effectiveness of Astronomaly, as well as simulated data to thoroughly test our new active learning approach. We find that for both datasets, Astronomaly roughly doubles the number of interesting anomalies found in the first 100 objects viewed by the user. Astronomaly is easily extendable to include new feature extraction techniques, anomaly detection algorithms and even different active learning approaches. The code is publicly available at https://github.com/MichelleLochner/astronomaly.
Repurposing GANs for One-shot Semantic Part Segmentation

While GANs have shown success in realistic image generation, the idea of using GANs for other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural parts of objects during their attempt to reproduce those objects? In this work, we test this hypothesis and propose a simple and effective approach based on GANs for semantic part segmentation that requires as few as one label example along with an unlabeled dataset. Our key idea is to leverage a trained GAN to extract a pixel-wise representation from the input image and use it as feature vectors for a segmentation network. Our experiments demonstrate that this GAN-derived representation is "readily discriminative" and produces surprisingly good results that are comparable to those from supervised baselines trained with significantly more labels. We believe this novel repurposing of GANs underlies a new class of unsupervised representation learning, which can generalize to many other tasks. More results are available at https://RepurposeGANs.github.io/.
Learning 3D Shape Feature for Texture-insensitive Person Re-identification

It is well acknowledged that person re-identification (person ReID) highly relies on visual texture information like clothing. Despite significant progress has been made in recent years, texture-confusing situations like clothing changing and persons wearing the same clothes receive little attention from most existing ReID methods. In this paper, rather than relying on texture based information, we propose to improve the robustness of person ReID against clothing texture by exploiting the information of a person’s 3D shape. Existing shape learning schemas for person ReID either ignore the 3D information of a person, or require extra physical devices to collect 3D source data. Differently, we propose a novel ReID learning framework that directly extracts a texture-insensitive 3D shape embedding from a 2D image by adding 3D body reconstruction as an auxiliary task and regularization, called 3D Shape Learning (3DSL). The 3D reconstruction based regularization forces the ReID model to decouple the 3D shape information from the visual texture, and acquire discriminative 3D shape ReID features. To solve the problem of lacking 3D ground truth, we design an adversarial self-supervised projection (ASSP) model, performing 3D reconstruction without ground truth. Extensive experiments on common ReID datasets and texture-confusing datasets validate the effectiveness of our model.