Reviewerly/exHarmony huggingface dataset:
Web Image Authentication Using Embedding Invisible Watermarking

In this paper, we present web service for image authentication using the third party network by embedding an invisible watermark digital signature image into color image. The client will provide the handwritten signature and host image (bicolor image), which will send to server end. At server end, we embed user signature to host image which gains high-end security and authentication of user as well. Earlier attempts provided only stand-alone user interface, as well as the resultant image, produced distortion in image which is a serious threat to user reliability. This paper explores the idea of image protection mechanism by using data hiding. This approach also enables reliability by accessing authentication by user hand signature instead of getting ASCII and UNICODE formats which are serious threats for security. This paper opens a million ways for the intellectual property rights of user over multimedia (here image). The future work involved video authentication by encoding stream of images.
Coarse-to-Fine Deep Metric Learning for Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR) is the process of searching for identical areas by investigating the similarities between a query image and the database images. RSIR is a challenging task owing to the time difference, viewpoint, and coverage area depending on the shooting circumstance, resulting in variations in the image contents. In this paper, we propose a novel method based on a coarse-to-fine strategy, which makes a deep network more robust to the variations in remote sensing images. Moreover, we propose a new triangular loss function to consider the whole relation within the tuple. This loss function improves the retrieval performance and demonstrates better performance in terms of learning the detailed information in complex remote sensing images. To verify our methods, we experimented with the Google Earth South Korea dataset, which contains 40,000 images, using the evaluation metric Recall@n. In all experiments, we obtained better performance results than those of the existing retrieval training methods. Our source code and Google Earth South Korea dataset are available online.
Coarse-to-fine sample-based background subtraction for moving object detection

Background subtraction is crucial for detecting moving objects in videos. However, most background subtraction methods usually fail to deal well with illumination changes, static foreground, ghosts, and dynamic background. In this paper, we propose an efficient coarse-to-fine sample-based algorithm to address these issues simultaneously. Specifically, a rough motion region estimation method, is first proposed by combining an improved frame difference method, the block-based image partition algorithm and multi-scale region-based technique to locate the possible area of moving objects. It can compensate the global illumination change and suppress dynamic background. Then, an improved Vibe algorithm with adaptive distance threshold and time sub-sampling factor parameters for each pixel is proposed to detect precise moving objects. It can not only retain static foreground for a long time while eliminating ghosts rapidly, but also detect camouflaged foreground efficiently. Experimental results demonstrate that our proposed algorithm can extract moving objects more accurately with less time cost than several state-of-the-art background subtraction approaches.
Unblocking the Potential of Blockchain

Technology has become an integral part of our life. One of the most emerging technologies of the 21st century is blockchain. It is a revolution for the digital world as it brings a new perspective of security, efficiency, and productivity of different frameworks. It works as a distributed database that is shared among a decentralized network of computers. It stores information about the transaction that is made with cryptocurrency and is maintained with the help of different computers which are linked in a peer-to-peer network. No single user can control the transactions, but everyone can inspect it. The information about the transactions is assembled as a “block” and these blocks are linked with each other as a “chain”. That's why it is called blockchain. The information inside blocks is immutable. The technology of blockchain is so efficient that it is not only used in securing financial information but also in credit history, real estate, public transport, music streaming, healthcare and so forth. According to Don and Alex Tapscott (2016), blockchain records not just financial transactions but virtually everything valuable. Currently, there is a wide spectrum of blockchain applications. The purpose of the research paper is to discuss some of the utilization of blockchain and they are Cybersecurity, Internet of Things (IoT), Fintech, Cloud Storage, Media, Travel, and Education. Furthermore, this paper examines both aspects, advantages, and disadvantages, of the blockchain. Finally, it identifies the problem areas of the technology that can be studied further to increase the effectiveness of blockchain.
Security analysis of DNA based steganography techniques

This study investigates the most recent data hiding techniques based on DNA steganography, including the highly improved DNA-based steganography technique, the data hiding using double DNA sequences method, and the enhanced DNA-based steganography technique. The strengths and weaknesses of these techniques are discussed. Additionally, the security of these techniques is analyzed based on several security parameters that measure the quality of DNA steganography with respect to many factors, including, but not limited to, cracking probability, blindness, modification rate and expansion rate, and layers of security. The goal of the comparison between the investigated techniques is to highlight the advantages and disadvantages of the existing data hiding algorithms and to motivate future research in this field. Moreover, the paper evaluates the discussed techniques based on some parameters, including capacity, payload, and bit per nucleotide (bpn). The result shows that the enhanced DNA-based steganography technique hides 2 bpn, whereas the highly improved method can hide on average 1.46 bpn, which is higher than data hiding using double DNA sequences method can hide .The paper also presents suggestions for how each technique can be optimized to to achieve a higher security level for hiding data within DNA sequences.
Artificial bee colony algorithm for content‐based image retrieval

Abstract Image retrieval is the process of searching for digital images from a large database. There exist two distinctive research groups, which employ the content‐based and description‐based approaches, respectively. However, research in the content‐based domain is currently dominant in the field, while the other approaches are not as widely utilized. Although there are a number of different techniques that are available for image retrieval, the development of more effective methods is still necessary. In recent years, previous research has shown that biologically inspired metaheuristic algorithms have great potential for use in solving problems in many science and engineering domains. The artificial bee colony (ABC) algorithm is one of the more promising biologically inspired metaheuristic approaches used to find optimal solutions as it has the advantages of convenient implementation and efficient performance. In this article, a new efficient method based on a combination of the gray‐level cooccurrence matrix (GLCM) with the ABC, referred to as “GLCM‐ABC,” is proposed for use in content‐based image retrieval (CBIR). The experimental results demonstrate that the proposed approach works well for CBIR and can classify specific types of material surfaces in images with a reasonably high level of accuracy as well as outperform other existing algorithms.
Learning from Examples to Find Fully Qualified Names of API Elements in Code Snippets

Developers often reuse code snippets from online forums, such as Stack Overflow, to learn API usages of software frameworks or libraries. These code snippets often contain ambiguous undeclared external references. Such external references make it difficult to learn and use those APIs correctly. In particular, reusing code snippets containing such ambiguous undeclared external references requires significant manual efforts and expertise to resolve them. Manually resolving fully qualified names (FQN) of API elements is a non-trivial task. In this paper, we propose a novel context-sensitive technique, called COSTER, to resolve FQNs of API elements in such code snippets. The proposed technique collects locally specific source code elements as well as globally related tokens as the context of FQNs, calculates likelihood scores, and builds an occurrence likelihood dictionary (OLD). Given an API element as a query, COSTER captures the context of the query API element, matches that with the FQNs of API elements stored in the OLD, and rank those matched FQNs leveraging three different scores: likelihood, context similarity, and name similarity scores. Evaluation with more than 600K code examples collected from GitHub and two different Stack Overflow datasets shows that our proposed technique improves precision by 4-6% and recall by 3-22% compared to state-of-the-art techniques. The proposed technique significantly reduces the training time compared to the StatType, a state-of-the-art technique, without sacrificing accuracy. Extensive analyses on results demonstrate the robustness of the proposed technique.
Server Security in Cloud Computing Using Block-Chaining Technique

Data security is a key concern in cloud computing . With the increase in attempts to hack a virtual cloud server in order to access the data stored there privately, more and more measures are being taken to ensure a reliable and secure cloud. Enhancements are made on traditional cryptography algorithms so that they are more suited to the distributed, virtual and multi-tenant environment of the cloud. Block-chaining is the newest technique in the fields of computing. It is basically a growing list of records, called as "blocks", which are linked and secured using cryptography. In this project, we are aiming to implement an encryption algorithm which works on the principles of block-chains, to ensure the security and integrity of a cloud-based system. The infallible nature of block-chains is what makes it perfect for cloud server security.
Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot

As a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human–robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a large amount of implementation work from human engineers. In addition, the written results cannot be accurately evaluated. To overcome these limitations, in this paper, we propose a robotic calligraphy model that combines a generative adversarial network (GAN) and deep reinforcement learning to enable a calligraphy robot to learn to write Chinese character strokes directly from images captured from Chinese calligraphic textbooks. In our proposed model, to automatically establish an aesthetic evaluation system for Chinese calligraphy, a GAN is first trained to understand and reconstruct stroke images. Then, the discriminator network is independently extracted from the trained GAN and embedded into a variant of the reinforcement learning method, the "actor-critic model", as a reward function. Thus, a calligraphy robot adopts the improved actor-critic model to learn to write multiple character strokes. The experimental results demonstrate that the proposed model successfully allows a calligraphy robot to write Chinese character strokes based on input stroke images. The performance of our model, compared with the state-of-the-art deep reinforcement learning method, shows the efficacy of the combination approach. In addition, the key technology in this work shows promise as a solution for robotic autonomous assembly.
Probabilistic Interpolation Recoder for Energy-Error-Product Efficient DBNs With p-Bit Devices

In this article, a probabilistic interpolation recoder (PIR) circuit is developed for deep belief networks (DBNs) with probabilistic spin logic (p-bit)-based neurons. To verify the functionality and evaluate the performance of the PIRs, we have implemented a <inline-formula><tex-math notation="LaTeX">$784 \times 200 \times 10$</tex-math></inline-formula> DBN circuit in SPICE for a pattern recognition application using the MNIST dataset. The PIR circuits are leveraged in the last hidden layer to interpolate the probabilistic output of the neurons, which are representing different output classes, through sampling the p-bit's output values and then counting them in a defined sampling time window. The PIR circuit is proposed as an alternative for conventional interpolation methods which were based on using a resistor-capacitor tank to integrate each neuron's output, followed by an analog-to-digital converter to generate the digital output. The circuit simulation results of PIR circuit exhibit at least 54, 81, and 78 percent reductions in power, energy, and energy-error-product, respectively, compared to previous techniques, without using any of the area-consuming analog components in the interpolation circuit. In addition, PIR circuits provide an inherent single stuck-at fault tolerant feature to mitigate both transient and permanent faults at the circuit's output. Reliability properties of the PIR circuits for single stuck-at faults are shown to be enhanced relative to conventional interpolation without requiring hardware redundancy.
Explicit Filterbank Learning for Neural Image Style Transfer and Image Processing

Image style transfer is to re-render the content of one image with the style of another. Most existing methods couple content and style information in their network structures and hyper-parameters, and learn it as a black-box. For better understanding, this paper aims to provide a new explicit decoupled perspective. Specifically, we propose StyleBank, which is composed of multiple convolution filter banks and each filter bank explicitly represents one style. To transfer an image to a specific style, the corresponding filter bank is operated on the intermediate feature produced by a single auto-encoder. The StyleBank and the auto-encoder are jointly learnt in such a way that the auto-encoder does not encode any style information. This explicit representation also enables us to conduct incremental learning to add a new style and fuse styles at not only the image level, but also the region level. Our method is the first style transfer network that links back to traditional texton mapping methods, and provides new understanding on neural style transfer. We further apply this general filterbank learning idea to two different multi-parameter image processing tasks: edge-aware image smoothing and denoising. Experiments demonstrate that it can achieve comparable results to its single parameter setting counterparts.
Japanese abstractive text summarization using BERT

In this study, we developed an automatic abstractive text summarization algorithm in Japanese using a neural network. We used a sequence-to-sequence encoder-decoder model for experimentation purposes. The encoder obtained a feature-based input vector of sentences using BERT. A transformer-based decoder returned the summary sentence from the output as generated by the encoder. This experiment was performed using the livedoor news corpus with the above model. However, issues arose as the same texts were repeated in the summary sentence.
Immersive Technologies in Higher Education

Immersive technologies are becoming increasingly popular in a wide range of fields from gaming to therapy. While there is a high number of case studies in education with promising results in terms of students' performance and engagement, there has still not been widespread adoption. Indeed, more research focused on large-size samples, assessing and discussing the effects on the medium and large term is required, as well as comparisons among different hardware and software, and guidelines for instructors that want to introduce these technologies in their classrooms. In this paper, we focus on the use of immersive technologies applications in higher education. In particular, we explain the basics of immersive technologies, review the main applications in higher education, discuss benefits and challenges, and describe good practices for instructors.
Predictive temporal embedding of dynamic graphs

In recent years, substantial effort has been devoted to learning to represent the static graphs and their substructures. A few studies explored utilizing temporal information available in a dynamic setting in order to address the node representation learning. However, the representation learning problem for the entire graph in a dynamic context is yet to be addressed. In this paper, we propose an unsupervised encoder-decoder framework that projects a dynamic graph at each time step into a d-dimensional space, taking into account both the graph's topology and dynamics. We investigate two different strategies. First, we address the representation learning problem by auto-encoding the graph dynamics. Second, we formulate a graph prediction problem and enforce the encoder to learn the representation that an autoregressive decoder then uses to predict the future of a dynamic graph. Gated graph neural networks (GGNNs) are incorporated to learn the topology of the graph at each time step and Long short-term memory networks (LSTMs) are leveraged to propagate the temporal information among the nodes through time. We demonstrate the efficacy of our approach with a graph classification task using two real-world datasets of animal behaviour and brain networks.
Fast and Secure Steganography Based on J-UNIWARD

Adaptive steganography based on minimizing additive distortion model with Syndrome-Trellis Codes (STCs) is considered as a state-of-the-art technique of covert communication. However, the running time on mobile phone with large size image is not acceptable. The current approach only accelerates STCs embedding. J-UNIWARD, as one of the most secure distortion function, its calculation time is ten times longer than STCs embedding time. Therefore, just accelerating STCs embedding will only result in a small decrease in total running time. In this letter, we propose a fast and secure steganography based on properly simplifying J-UNIWARD. A simplified distortion function based on J-UNIWARD is designed to reduce time complexity. Besides, we propose a segmented STCs, which increases the computational parallelism of STCs embedding by several times without significantly degrading the security. Experimental results demonstrate that the proposed method can be three times faster than J-UNIWARD at only a very slight price of security.
Gaussian process approach for dose mapping in radiation fields

In this work, a Gaussian Process (Kriging) approach is proposed to provide efficient dose mapping for complex radiation fields using limited number of responses. Given a few response measurements (or simulation data points), the proposed approach can help the analyst in completing a map of the radiation dose field with a 95% confidence interval, efficiently. Two case studies are used to validate the proposed approach. The First case study is based on experimental dose measurements to build the dose map in a radiation field induced by a D-D neutron generator. The second, is a simulation case study where the proposed approach is used to mimic Monte Carlo dose predictions in the radiation field using a limited number of MCNP simulations. Given the low computational cost of constructing Gaussian Process (GP) models, results indicate that the GP model can reasonably map the dose in the radiation field given a limited number of data measurements. Both case studies are performed on the nuclear engineering radiation laboratories at the University of Sharjah.
An Attention Ensemble Based Approach for Multilabel Profanity Detection

The amount of user-generated content in the cyberspace keeps increasing in the 21st century. However, it has also meant an increase in the number of cyber abuse and bullying incidents being reported. Use of profane text by individuals threatens the liberty and integrity of the digital space. Manual moderation and reporting mechanisms have been traditionally used to keep a check on such profane text. Dependency on human interpretation and delay in results have been the biggest obstacles in this system. Previous deep learning-based approaches to automate the process have involved use of traditional convolution and recurrence based sequential models. However, these models tend to be computationally expensive and have higher memory requirement. Further, they tend to produce state of the art results in binary classification but perform relatively poorly on multilabel tasks, owing to less flexibility in architecture. In today's world, classifying text in a binary way is no longer sufficient and thus a flexible solution able to generalize well on multilabel text is the need of the hour. In this paper, we propose a multihead attention-based approach for detection of profane text. We couple our model with power weighted average ensembling techniques to further improve the performance. The proposed approach does not have additional memory requirement and is less complex as compared to previous approaches. The improved results obtained by our model on publicly available real-world data further validate the same. Flexible, lightweight models which can handle multilabel text well can prove to be crucial in cracking down on social evils in the digital space.
Beyond Coding: Detection-driven Image Compression with Semantically Structured Bit-stream

With the development of 5G and edge computing, it is increasingly important to offload intelligent media computing to edge device. Traditional media coding scheme codes the media into one binary stream without a semantic structure, which prevents many important intelligent applications from operating directly in bit-stream level, including semantic analysis, parsing specific content, media editing, etc. Therefore, in this paper, we propose a learning based Semantically Structured Coding (SSC) framework to generate Semantically Structured Bit-stream (SSB), where each part of bit-stream represents a certain object and can be directly used for aforementioned tasks. Specifically, we integrate an object detection module in our compression framework to locate and align the object in feature domain. After applying quantization and entropy coding, the features are re-organized according to detected and aligned objects to form a bit-stream. Besides, different from existing learning-based compression schemes that individually train models for specific bit-rate, we share most of model parameters among various bit-rates to significantly reduce model size for variable-rate compression. Experimental results demonstrate that only at the cost of negligible overhead, objects can be completely reconstructed from partial bit-stream. We also verified that classification and pose estimation can be directly performed on partial bit-stream without performance degradation.