Reviewerly/exHarmony huggingface dataset:
BBC++: Enhanced Block Boundary Continuity on Defining Non-Additive Distortion for JPEG Steganography

Recently, Li et al. proposed an effective non-additive distortion model for JPEG steganography by preserving Block Boundary Continuity (BBC) in the spatial domain. However, the method based on BBC only explored how the modifications of DCT coefficient pairs at the same mode in adjacent blocks will impact on BBC. In this paper, we propose a method to enhance the BBC, called BBC++, by considering the mutual impact on the BBC from all DCT coefficients in adjacent blocks. To do that, we design updating strategies for both covers and costs in multi-round embedding processions. During the embedding, the cover is updated to repair the BBC after embedding and the corresponding costs are updated to keep the BBC from being destroyed in the next embedding. Experimental results show that the BBC++ can better maintain BBC and outperform previous non-additive distortion steganography when resisting modern JPEG steganalyzers.
Deep Reinforcement Learning Empowered Adaptivity for Future Blockchain Networks

Recently, blockchain has elicited escalating attention from academia to industry. However, blockchain is still in its initial stage, and remains a great number of non-trivial problems to be delved before being used as a generic platform. The most intractable one is the scalability problem. The deep reinforcement learning empowered adaptivity can help the blockchain network break through the bottleneck. In this paper, we study a deep reinforcement learning empowered adaptivity approach for future blockchain networks, so as to improve the scalability and meet the requirements of different users. Specifically, rather than using one consensus protocol as the best fit one, the blockchain networks launch different consensus protocols, based on users’ quality of service (QoS) requirements. To this end, we quantify four consensus protocols. Additionally, the blockchain networks are heavily hampered by the limited computation and bandwidth resources. We also dynamically allocate computation and bandwidth resources to the blockchain networks. Then we formulate these thress items, i.e., the selection of consensus protocols, computation resource, and network bandwidth resource, as a joint optimization problem. A deep reinforcement learning approach is used to solve this problem. Simulation results are presented to show the effectiveness of our proposed scheme.
Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle

Transfer learning algorithms have been widely studied for machine learning in recent times. In particular, in image recognition and classification tasks, transfer learning has shown significant benefits, and is getting plenty of attention in the research community. While performing a transfer of knowledge among source and target tasks, homogeneous dataset is not always available, and heterogeneous dataset can be chosen in certain circumstances. In this article, we propose a way of improving transfer learning efficiency, in case of a heterogeneous source and target, by using the Hebbian learning principle, called Hebbian transfer learning (HTL). In computer vision, biologically motivated approaches such as Hebbian learning represent associative learning, where simultaneous activation of brain cells positively affect the increase in synaptic connection strength between the individual cells. The discriminative nature of learning for the search of features in the task of image classification fits well to the techniques, such as the Hebbian learning rule—neurons that fire together wire together. The deep learning models, such as convolutional neural networks (CNN), are widely used for image classification. In transfer learning, for such models, the connection weights of the learned model should adapt to new target dataset with minimum effort. The discriminative learning rule, such as Hebbian learning, can improve performance of learning by quickly adapting to discriminate between different classes defined by target task. We apply the Hebbian principle as synaptic plasticity in transfer learning for classification of images using a heterogeneous source-target dataset, and compare results with the standard transfer learning case. Experimental results using CIFAR-10 (Canadian Institute for Advanced Research) and CIFAR-100 datasets with various combinations show that the proposed HTL algorithm can improve the performance of transfer learning, especially in the case of a heterogeneous source and target dataset.
A gated piecewise CNN with entity-aware enhancement for distantly supervised relation extraction

The piecewise convolutional neural network (PCNN) is an important method for distant supervision relation extraction. However, the existing methods based on the PCNN still have the following shortcomings: these methods lack the consideration of the impacts of entity pairs and the sentence context on word encoding and do not distinguish the different contributions of the three segments in PCNN to relation classification. To solve these problems, we propose a novel gated piecewise CNN with entity-aware enhancement for distantly supervised relation extraction. First, we use a multi-head self-attention mechanism to combine the word embedding with the head/tail entity embedding and relative position embedding to generate an entity-aware enhanced word representation, which is capable of capturing the semantic dependency between each word and entity pair. Then we introduce a global gate to combine each entity-aware enhanced word representation with their average in the input sentence to form the final word representation of the PCNN input. Moreover, to determine the key segments where the most important information for relation classification appears, we design another gate mechanism to assign a different weight to each sentence segment to highlight the effects of key segments on the PCNN. Experiments on New York Times dataset demonstrate that our model significantly outperforms most of the state-of-the-art models.
On a Symmetric Image Encryption Algorithm Based on the Peculiarity of Plaintext DNA Coding

Many plaintext-related or non-plaintext-related image encryption algorithms based on chaotic systems have been found inefficient and insecure under chosen plaintext attacks. In this paper, a novel plaintext-related mechanism based on the peculiarity of plaintext DNA coding (PPDC) is presented and used to developed a symmetric image encryption algorithm. In our scheme, a hyper-chaotic Lorenz system is used to produce four chaotic sequences. Firstly, by using one chaotic sequence to control the DNA rules, the original image is encoded to obtain the image DNA coding and PPDC, and another chaotic sequence is encoded into a DNA sequence, which is used in the DNA XOR operation. Then, the processing of the remaining two chaotic sequences by using the PPDC is performed to obtain two key streams, which are used in the permutation stage. After performing the traditional permutation operation and DNA XOR operation, the cipher image is obtained. Because of the use of the PPDC, the key streams used in the permutation stage are related to the secret keys and plaintext image, which gives the encryption system higher plaintext sensitivity and security. The simulation experimental results and security analysis demonstrate that the proposed encryption system possesses high efficiency and security and can resist various typical attacks like exhaustive attack, statistical attack, and differential attack effectively.
Indoor Scene Change Captioning Based on Multimodality Data

This study proposes a framework for describing a scene change using natural language text based on indoor scene observations conducted before and after a scene change. The recognition of scene changes plays an essential role in a variety of real-world applications, such as scene anomaly detection. Most scene understanding research has focused on static scenes. Most existing scene change captioning methods detect scene changes from single-view RGB images, neglecting the underlying three-dimensional structures. Previous three-dimensional scene change captioning methods use simulated scenes consisting of geometry primitives, making it unsuitable for real-world applications. To solve these problems, we automatically generated large-scale indoor scene change caption datasets. We propose an end-to-end framework for describing scene changes from various input modalities, namely, RGB images, depth images, and point cloud data, which are available in most robot applications. We conducted experiments with various input modalities and models and evaluated model performance using datasets with various levels of complexity. Experimental results show that the models that combine RGB images and point cloud data as input achieve high performance in sentence generation and caption correctness and are robust for change type understanding for datasets with high complexity. The developed datasets and models contribute to the study of indoor scene change understanding.
InpaintFusion: Incremental RGB-D Inpainting for 3D Scenes

State-of-the-art methods for diminished reality propagate pixel information from a keyframe to subsequent frames for real-time inpainting. However, these approaches produce artifacts, if the scene geometry is not sufficiently planar. In this article, we present InpaintFusion, a new real-time method that extends inpainting to non-planar scenes by considering both color and depth information in the inpainting process. We use an RGB-D sensor for simultaneous localization and mapping, in order to both track the camera and obtain a surfel map in addition to RGB images. We use the RGB-D information in a cost function for both the color and the geometric appearance to derive a global optimization for simultaneous inpainting of color and depth. The inpainted depth is merged in a global map by depth fusion. For the final rendering, we project the map model into image space, where we can use it for effects such as relighting and stereo rendering of otherwise hidden structures. We demonstrate the capabilities of our method by comparing it to inpainting results with methods using planar geometric proxies.
Enhancing the Security of Deep Learning Steganography via Adversarial Examples

Steganography is a collection of techniques for concealing the existence of information by embedding it within a cover. With the development of deep learning, some novel steganography methods have appeared based on the autoencoder or generative adversarial networks. While the deep learning based steganography methods have the advantages of automatic generation and capacity, the security of the algorithm needs to improve. In this paper, we take advantage of the linear behavior of deep learning networks in higher space and propose a novel steganography scheme which enhances the security by adversarial example. The system is trained with different training settings on two datasets. The experiment results show that the proposed scheme could escape from deep learning steganalyzer detection. Besides, the produced stego could extract secret image with less distortion.
HARB: A Hypergraph-Based Adaptive Consortium Blockchain for Decentralized Energy Trading

The emergence of the Internet of Things (IoT) and distributed energy resources (DERs), has given rise to collaborative communities that manage their energy production and consumption load through peer-to-peer decentralized energy trading (P2P DET). To address the issue of distributed trust in these communities, blockchain technology is widely considered as a promising solution due to its ability to provide records provenance and visibility. However, the current blockchain-based platforms are known to compromise on scalability and privacy in favor of trustless interactions and often do not support interoperability. In this work, we propose a hypergraph-based adaptive consortium blockchain (HARB) framework, which coordinates DERs through high-order relationships rather than P2P pairwise relationships. HARB is presented in a three-layered network architecture to address the aforementioned challenges. The bottom layer (Underlay) addresses the issue of scalability, by exploiting the rich representation ability of hypergraphs to describe complex relationships among DERs and end users. We use the described complex relations to form scalable network clusters with intra- and inter-community energy trading relationships. The middle layer (Overlay) presents a blockchain service model to describe adaptive blockchain modules that support interoperability between the network clusters. To preserve privacy, we present a data tagging and anonymization model in the top layer (Contract), which attributes transactions to specific network clusters. To evaluate our framework, we modeled various IoT devices with different computing resource profiles to simulate a distributed energy trading (DET) environment. The analyzed results have shown that our proposed framework can effectively improve the performance of blockchain-based DET systems.
A Robust Forgery Detection Method for Copy–Move and Splicing Attacks in Images

Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy–move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors’ physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.
HAA500: Human-Centric Atomic Action Dataset with Curated Videos

We contribute HAA500 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591K labeled frames. To minimize ambiguities in action classification, HAA500 consists of highly diversified classes of fine-grained atomic actions, where only consistent actions fall under the same label, e.g., "Baseball Pitching" vs "Free Throw in Basketball". Thus HAA500 is different from existing atomic action datasets, where coarse-grained atomic actions were labeled with coarse action-verbs such as "Throw". HAA500 has been carefully curated to capture the precise movement of human figures with little class-irrelevant motions or spatiotemporal label noises.The advantages of HAA500 are fourfold: 1) human-centric actions with a high average of 69.7% detectable joints for the relevant human poses; 2) high scalability since adding a new class can be done under 20–60 minutes; 3) curated videos capturing essential elements of an atomic action without irrelevant frames; 4) fine-grained atomic action classes. Our extensive experiments including cross-data validation using datasets collected in the wild demonstrate the clear benefits of human-centric and atomic characteristics of HAA500, which enable training even a baseline deep learning model to improve prediction by attending to atomic human poses. We detail the HAA500 dataset statistics and collection methodology and compare quantitatively with existing action recognition datasets.
Protecting Social Network With Differential Privacy Under Novel Graph Model

Online social networks (OSNs) contain sensitive information about individuals, so it's important to anonymize network data before releasing it.Recently, researchers introduced differential privacy to give a strict privacy guarantee.Graph abstraction models are essential to transform graph structural information into numerical type data, and the choice of models may influence the utility preservation of the published graph.In this paper, we propose a comprehensive differentially private graph model which combines the dK-1, dK-2, and dK-3 series together.The dK-1 series stores the degree frequency, the dK-2 series adds the joint degree frequency, and the dK-3 series contains the linking information between edges.In our scheme, low dimensional series data makes the regeneration process more executable and effective, while high dimensional data preserves additional utility of the graph.As the higher dimensional data is more sensitive to the noise, we carefully design the executing sequence and add three levels of rewiring algorithms to further preserve the structural information.The final released graph increases the graph utility under differential privacy.We also experimentally evaluate our approach on real-world OSNs and show that our scheme produces ready-to-be-shared graphs that are closely matched with the originals, while achieving differential privacy.
Application of remote sensing and GIS techniques for exploring potential areas of hydrothermal mineralization in the central Eastern Desert of Egypt

The integration of remotely sensed data allowed the successful characterization of the hydrothermal alteration zones of the Abu–Gaharish area in the Central Eastern Desert of Egypt using image transformation techniques. The integration of several evidential maps highlighted the plausible areas with high concentrations of argillic and phyllic, key hydrothermal minerals that reflect the intensity of hydrothermal effects and the probable sites of ore bodies. The excellent prospective zone identified in the map occupies around 1.39% of the study area. Kaolinite–smectite minerals with correlation scores of around 0.97 were consistent with areas of hydrothermal alteration and mining activities; however, propylitic minerals were confined to basic metavolcanics and serpentinites. The resulting predictive map was assessed using existing mining data with significant consistency; therefore, ﬁeld veriﬁcation is essential. The overall results showed that remotely sensed data represent a valuable mapping tool for geological and mineral resources reconnaissance in the arid regions
General public’s attitude toward governments implementing digital contact tracing to curb COVID-19 – a study based on natural language processing

Purpose Governments worldwide are taking various measures to prevent the spreading of COVID virus. One such effort is digital contact tracing. However, the aspect of digital contact tracing was met with criticism, as many critics view this as an attempt of the government to control people and a fundamental breach of privacy. Using machine learning techniques, this study aims to deal with understanding the general public’s emotions toward contact tracing and determining whether there is a change in the attitude of the general public toward digital contact tracing in various months of crises. This study also analyzes the significant concerns voiced out by the general public regarding digital contact tracing. Design/methodology/approach For the analysis, data were collected from Reddit. Reddit posts discussing the digital contact tracing during COVID-19 crises were collected from February 2020 to July 2020. A total of 5,025 original Reddit posts were used for this study. Natural language processing, which is a part of machine learning, was used for this study to understand the sentiments of the general public about contact tracing. Latent Dirichlet allocation was used to understand the significant issues voiced out by the general public while discussing contact tracing. Findings This study was conducted in two parts. Study 1 results show that the percentage of general public viewing the aspect of contact tracing positively had not changed throughout the time period of Data frame (March 2020 to July 2020). However, compared to the initial month of the crises, the later months saw a considerable increase in negative sentiments and a decrease in neutral sentiments regarding the digital contact tracing. Study 2 finds out the significant issues public voices out in their negative sentiments are a violation of privacy, fear of safety and lack of trust in government. Originality/value Although numerous studies were conducted on how to implement contact tracing effectively, to the best of the authors’ knowledge, this is the first study conducted with an objective of understanding the general public’s perception of contact tracing.
Zero problems with compositional data of physical behaviors: a comparison of three zero replacement methods

Abstract Background Researchers applying compositional data analysis to time-use data (e.g., time spent in physical behaviors) often face the problem of zeros, that is, recordings of zero time spent in any of the studied behaviors. Zeros hinder the application of compositional data analysis because the analysis is based on log-ratios. One way to overcome this challenge is to replace the zeros with sensible small values. The aim of this study was to compare the performance of three existing replacement methods used within physical behavior time-use epidemiology: simple replacement, multiplicative replacement, and log-ratio expectation-maximization (lrEM) algorithm. Moreover, we assessed the consequence of choosing replacement values higher than the lowest observed value for a given behavior. Method Using a complete dataset based on accelerometer data from 1310 Danish adults as reference, multiple datasets were simulated across six scenarios of zeros (5–30% zeros in 5% increments). Moreover, four examples were produced based on real data, in which, 10 and 20% zeros were imposed and replaced using a replacement value of 0.5 min, 65% of the observation threshold, or an estimated value below the observation threshold. For the simulation study and the examples, the zeros were replaced using the three replacement methods and the degree of distortion introduced was assessed by comparison with the complete dataset. Results The lrEM method outperformed the other replacement methods as it had the smallest influence on the structure of relative variation of the datasets. Both the simple and multiplicative replacements introduced higher distortion, particularly in scenarios with more than 10% zeros; although the latter, like the lrEM, does preserve the ratios between behaviors with no zeros. The examples revealed that replacing zeros with a value higher than the observation threshold severely affected the structure of relative variation. Conclusions Given our findings, we encourage the use of replacement methods that preserve the relative structure of physical behavior data, as achieved by the multiplicative and lrEM replacements, and to avoid simple replacement. Moreover, we do not recommend replacing zeros with values higher than the lowest observed value for a behavior.