Reviewerly/exHarmony huggingface dataset:
Essential secret image sharing scheme with small and equal sized shadows

In the literature of secret sharing, the property of essential secret sharing scheme ensures that no set of participants, missing at least one essential participant, should be able to get any information regarding the secret. However, there are essential secret image sharing schemes available in the literature in which we have shown mathematically as well as through experiments that any k or more non-essential participants together can get information about the secret image. It is true that in most of these schemes, preprocessing steps such as random permutations or chaotic maps are used on the secret image to avoid this problem. But that will surely introduce an overhead to the schemes. Moreover, the security of these schemes mainly depends on the preprocessing step and not on the secret sharing schemes. However, our proposed (t,k,n)-ESIS scheme for grayscale images over the finite field GF(pm) does not require any preprocessing step to secure the scheme. Though, the proposed scheme over GF(pm) with pm>28 is little lossy, it has the advantage over most of the ESIS schemes in the sense that the scheme works fine, even if the number of participants is more than 255. Most importantly, our proposed scheme over GF(28) is completely lossless. Moreover, our proposed scheme, does not have the limitations such as different size of shadows, concatenation of sub-shadows, use of derivative polynomials etc. Finally, our scheme has reduced share size and work fine without any preprocessing steps on secret image, making our scheme efficient.
Topic level summary generation using BERT induced Abstractive Summarization Model

In this paper, the implemented system channels an idea called Topic level summary. The topic level summary is a collective summary in text format which consists of relevant information on a topic where a topic can be an idea, concept or a term user wants to know about. This system successfully manages to generate Topic Level Summary on the user's input. This system accomplishes that in the following fashion. Relevant information about the keyword is scraped from ‘Wikipedia' and ‘The Hindu' and user's input is also validated through the same. This information in text format is passed on to the abstractive summarization model which uses advanced NLP capabilities of the bidirectional encoder representation transformers (BERT) language model to generate a topic level summary. The mentioned summarization model has ROUGE scores of 41.72, 19.39, and 38.76 for ROUGE-1, ROUGE-2, and ROUGE-L respectively. This paper also encapsulates the need for a topic level summary.
A Robust Copy-Move Forgery Detection In Digital Image Forensics Using SURF

In recent years, due to the technological revolution in editing digital images, various advanced image manipulating software has been used to build new unrealistic images without leaving traces of what happens, therefore tampering will be hard to detect visually. Digital image forgeries have many forms but still recognizing copy-move forgery is very challenging. Hence, this paper introduces a new robust algorithm to detect copy-move forgery based on Speeded Up Robust Feature (SURF) descriptor, Approximate Nearest Neighbor (ANN) as a feature matching, Simple Linear Iterative Clustering (SLIC) used as a clustering algorithm to divide the whole image into superpixel blocks. The doubted regions are determined by replacing the matched feature points with corresponding superpixel blocks then the neighboring blocks have been merged based on similar Local Color Features (LCF). Finally, morphological close operation applied to elicit the doubted forged regions. Proposed algorithm recorded a running time of 3.84 seconds with 91.95% localization accuracy applied on various datasets such as CoMoFoD, MICC-F2000, MICC-F220, and MICC-F600 for detecting tampered plain copy-move, duplicate regions, post-processing and pre-processing attacks like color reduction, blurring, brightness modifications, noise addition, geometric attacks, and JPEG compression as an evaluation of robustness.
3D-Aware Scene Change Captioning From Multiview Images

In this letter, we propose a framework that recognizes and describes changes that occur in a scene observed from multiple viewpoints in natural language text. The ability to recognize and describe changes that occurred in a 3D scene plays an essential role in a variety of human-robot interaction applications. However, most current 3D vision studies have focused on understanding the static 3D scene. Existing scene change captioning approaches recognize and generate change captions from single-view images. Those methods have limited ability to deal with camera movement, object occlusion, which are common in real-world settings. To resolve these problems, we propose a framework that observes every scene from multiple viewpoints and describes the scene change based on an understanding of the underlying 3D structure of scenes. We build three synthetic datasets consisting of primitive 3D object and scanned real object models for evaluation. The results indicate that our method outperforms the previous state-of-the-art 2D-based method by a large margin in terms of sentence generation and change understanding correctness. In addition, our method is more robust to camera movements compared to the previous method and also performs better for scenes with occlusions. Moreover, our method also shows encouraging results in a realistic scene-setting, which indicates the possibility of adapting our framework to a more complicated and extensive scene-settings.
Early anomaly detection and localisation in distribution network: a data‐driven approach

The measurement data collected from the supervisory control and data acquisition (SCADA) system installed in distribution network can reflect the operational state of the network effectively. In this study, a random matrix theory-based approach is developed for early anomaly detection and localisation by using the data. For every feeder in the distribution network, a corresponding data matrix is formed. Based on the Marchenko–Pastur law for the empirical spectral analysis of covariance ‘signal+noise’ matrix, the linear eigenvalue statistics are introduced to indicate the anomaly, and the outliers and their corresponding eigenvectors are analysed for locating the anomaly. As for the low observability feeders in the distribution network, an increasing data dimension algorithm is designed for the formulated low-dimensional matrices being more accurately analysed. The developed approach can detect and localise the anomaly at an early stage, and it is robust to random disturbance and measurement error. Cases on Matpower simulation data and real SCADA data corroborate the feasibility of the approach.
Integrating Haze Density Features for Fast Nighttime Image Dehazing

To date, much progress has been achieved on daytime image dehazing, yet the nighttime dehazing problem is still not well addressed.Different from the imaging conditions in the daytime, the ambient illumination in the nighttime hazy scene is usually not globally isotropic due to the non-uniform incident lights from multiple artificial light sources.Currently, almost all the existing nighttime dehazing methods use a certain kind of image priors, whereby these spatial filtering based priors are not widely applicable in nighttime hazy scenes.For example, the maximum reflectance prior (MRP) cannot handle the dark regions well and the dark channel prior (DCP) is not valid in the light source regions.In this paper, we propose an efficient and fast method for nighttime image dehazing.By exploring the visual properties of hazy images, we construct an effective linear model to build the connection between the transmission and multiple haze-relevant features.Towards solving this model, a data-driven approach is adopted to learn the unknown coefficients.Operating on the pixel level, this novel approach requires no further refinement of transmission map as used in those prior-based methods.In addition, aiming at solving the problem of halo effect around the light sources caused by MRP, we introduce a color-dependant MRP method for color correction.We demonstrate the effectiveness of our method on a number of experiments compared to the state-of-the-art nighttime dehazing methods.
A comparative study of estimating solar radiation using machine learning approaches: DL, SMGRT, and ANFIS

Solar energy has a key role in producing clean and emissions-free power compare to conventional methods. However, sustainable development also requires a reliable and predictable energy source. It also needs methods to measure and predict predictable supply. The main aim of the study is to improve reliable and precise solar radiation prediction models on monthly mean daily basis using various machine learning techniques. Simple Membership Function and Fuzzy Rule Generating Technique (SMGRT), which does not require error and trial for model adjustment, is the first-choice model in this study. Experience and observations about the model will greatly reduce the volume of processing for the fuzzy SMGRT model. On the other hand, Deep Learning (DL) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have become increasingly popular in understanding nonlinear data structures and solving complex problems. Therefore, DL and ANFIS were also applied to estimate solar radiation. The data set used in the study were created using sunshine duration (s), extra-terrestrial solar radiation (H0), relative humidity (RH), cloudiness (C), air temperature (T) and soil temperature (ST) parameters. Estimation performance of models was evaluated by using several statistical indicators which are Mean Bias Error (MBE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation Coefficient (R2). When the performances of the models were compared, it was seen that all three models obtained remarkable results. In addition, it was shown that the models performed well based on the metrics in the testing phase. The SMGRT model has slightly better performance than DL and ANFIS for different input combinations. SMGRT Model 1 (with inputs H0, s, and T) shows the best statistical performance (MBE = 0.156, MSE = 1.878, RMSE = 1.371, and R2 = 0.960) not only in SMGRT models but also in others.
Malaria Disease Recognition through Adaptive Deep Learning Models of Convolutional Neural Network

Malaria is a disease from blood generated by the Plasmodium parasites spread through the bite of female Anopheles mosquito. Medical personnel diagnose and checks for malaria infection and compute parasitemia through blood smears. Yet, their accuracy and validity in classifying and detecting malaria infection relies on the quality of the smear. This kind of examination may result in inaccurate and poor validation of result especially in a massive scale of diagnoses. Convolutional Neural Networks (CNN) is a division of Deep Neural Networks that is commonly used to analyzed images through learning of image patterns. Deep learning techniques applied to malaria screening will be useful diagnostic aid. In this study, the author was able to evaluate the performance of several CNN architectures in detecting and classifying malaria infected disease and not infected with the malaria disease. The author experimentally determines the ideal CNN model for extracting features from the cell image data. Results shows that ResNet, GoogleNet and VGGNet models are the best CNN models achieving an accuracy rate ranging from 90% to 96% for malaria disease detection in an adaptive deep learning.
Blockchain-Based IoT Application Using Smart Contracts: Case Study of M2M Autonomous Trading

Blockchain technology can be used to track billions of interconnected devices, enabling secure data exchange and data processing. The decentralized and autonomous ability of the blockchain makes it an ideal solution for Internet of Things(IoT) applications. In this paper, we explore a basic IoT-Blockchain fusion model with four layers which contains different types of IoT devices. Distributed file system is considered in the model to store huge amount of IoT data. Then, a case study for blockchain-based IoT application, a Machine-to-Machine(M2M) autonomous trading system, is proposed on the Ethereum blockchain. We build smart contracts for device registration, data storage, service provision and fair payment, and the proof-of-concept is implemented using two Raspberry Pis to interact with smart contracts. The proposed system verifies that blockchain could improve IoT applications in transparency, traceability and security.
Classification of Online Toxic Comments Using Machine Learning Algorithms

Toxic comments are disrespectful, abusive, or unreasonable online comments that usually make other users leave a discussion. The danger of online bullying and harassment affects the free flow of thoughts by restricting the dissenting opinions of people. Sites struggle to promote discussions effectively, leading many communities to limit or close down user comments altogether. This paper will systematically examine the extent of online harassment and classify the content into labels to examine the toxicity as correctly as possible. Here, we will use six machine learning algorithms and apply them to our data to solve the problem of text classification and to identify the best machine learning algorithm based on our evaluation metrics for toxic comments classification. We will aim at examining the toxicity with high accuracy to limit down its adverse effects which will be an incentive for organizations to take the necessary steps.
Exposure-Structure Blending Network for High Dynamic Range Imaging of Dynamic Scenes

This paper presents a deep end-to-end network for high dynamic range (HDR) imaging of dynamic scenes with background and foreground motions. Generating an HDR image from a sequence of multi-exposure images is a challenging process when the images have misalignments by being taken in a dynamic situation. Hence, recent methods first align the multi-exposure images to the reference by using patch matching, optical flow, homography transformation, or attention module before the merging. In this paper, we propose a deep network that synthesizes the aligned images as a result of blending the information from multi-exposure images, because explicitly aligning photos with different exposures is inherently a difficult problem. Specifically, the proposed network generates under/over-exposure images that are structurally aligned to the reference, by blending all the information from the dynamic multi-exposure images. Our primary idea is that blending two images in the deep-feature-domain is effective for synthesizing multi-exposure images that are structurally aligned to the reference, resulting in better-aligned images than the pixel-domain blending or geometric transformation methods. Specifically, our alignment network consists of a two-way encoder for extracting features from two images separately, several convolution layers for blending deep features, and a decoder for constructing the aligned images. The proposed network is shown to generate the aligned images with a wide range of exposure differences very well and thus can be effectively used for the HDR imaging of dynamic scenes. Moreover, by adding a simple merging network after the alignment network and training the overall system end-to-end, we obtain a performance gain compared to the recent state-of-the-art methods.
Optical image encryption and authentication scheme based on partial joint power spectrum and phase-retrieval technique

Abstract In this paper, a novel optical image encryption and authentication scheme based on partial joint power spectrum (JPS) and phase-retrieval technique has been proposed. In the proposed encryption process, a pure amplitude spatial light modulator (SLM) is utilized to modulate and display the entire input plane comprised with the real-valued plaintext and the amplitude key while the random phase mask (RPM) merged with the plaintext could be provided by the ground glass in the practical setup. The entire input plane is regarded as the input of a joint transform correlator (JTC)-based encryption structure while the sub-block (partial JPS) recorded by the charged-couple device (CCD) is the output (ciphertext). Since only the sub-block is recorded in the encryption process, the size of the plaintext could be larger than that of the CCD in a certain degree, which is a major advantage comparing to the conventional JTC-based cryptosystem. In addition, since the size of the ciphertext is irrelative to the size of the plaintext, the security of location key in the entire plane has been enhanced. In addition, the location keys of the chosen sub-block used as the ciphertext are regarded as additional private keys, which enlarges the key spaces of the conventional JTC-based cryptosystem. In the proposed authentication process, the sub-block (ciphertext) is used as the partial amplitude constraint in the iterative process based on phase-retrieval technique and then the decoded image is authenticated by the nonlinear optical verification operator. It is the first time that the optical image encryption and authentication using partial JPS as the ciphertext has been proposed. Numerical simulation is carried out to validate the feasibility and effectiveness of the proposed scheme.
Joint extraction of entities and relations using graph convolution over pruned dependency trees

We present a novel end-to-end deep neural network model based on graph convolutional networks for simultaneous joint extraction of entities and relations among them. Our model captures context and syntactic information from sentence by stacking a graph convolutional layer over bidirectional sequential LSTM layers. We sequentially concatenate the subject, object and sentence representations for obtaining the directionality of relations. Besides, in order to address long entity-distances problem, we further apply a path-centric pruning procedure to input trees in order to preserve useful information while maximally removing irrelevant words. Experiments are conducted on NYT dataset, and the proposed model achieves the state-of-the-art results on entity and relation extraction task. Our source code is available on Github: https://github.com/michael-hon/LSTM-GCN-ER.
Learning Hadamard-Product-Propagation for Image Dehazing and Beyond

Image dehazing has evolved into an attractive research field in the computer vision community in the past few decades. Previous traditional approaches attempt to design energy-based objective functions. However, they cannot accurately express the intrinsic characteristics of the images, posing weak adaptation ability for real-world complex scenarios. More recently, deep learning techniques for image dehazing have matured and become more reliable, showing outstanding performance. Nevertheless, these methods heavily depend on training data, restricting their application ranges. More importantly, both traditional and deep learning approaches all ignore a common issue, noises/artifacts always appear in the recovery process. To this end, a new Hadamard-Product (HP) model is proposed, which consists of a series of data-driven priors. Based on this model, we derive a Learnable Hadamard-Product-Propagation (LHPP) by cascading a series of principle-inspired guidance and recovery modules. In which, the principle-inspired guidance related to transmission is endowed the smoothness property, the other recovery module satisfies the distribution of natural images. The Hadamard-product-based propagations is generated in our developed learnable framework for the task of image dehazing. In this way, we can eliminate noises/artifacts in the recovery procedure to obtain the ideal outputs. Subsequently, since the generality of our HP model, we successfully extend our LHPP to settle low-light image enhancement and underwater image enhancement problems. A series of analytical experiments are performed to verify our effectiveness. Plenty of performance evaluations on three complex tasks fully reveal our superiority against multiple state-of-the-art methods.
Voice of the Users: A Demographic Study of Software Feedback Behaviour

User feedback on mobile app stores, product forums, and on social media can contain product development insights. There has been a lot of recent research studying this feedback and developing methods to automatically extract requirement-related information. This feedback is generally considered to be the “voice of the users”; however, only a subset of software users provide online feedback. If the demographics of the online feedback givers are not representative of the user base, this introduces the possibility of developing software that does not meet the needs of all users. It is, therefore, important to understand who provides online feedback to ensure the needs of under-represented groups are not being missed.In this work, we directly survey 1040 software users about their feedback habits, software use, and demographic information. Their responses indicate that there are statistically significant differences in who gives feedback on each online channel, with respect to traditional demographics (gender, age, etc). We also identify key differences in what motivates software users to engage with each of the three channels. Our findings provide valuable context for requirements elicited from online feedback and show that considering information from all channels will provide a more comprehensive view of user needs.
A Blockchain-based Approach for Assessing Compliance with SLA-guaranteed IoT Services

Within cloud-based internet of things (IoT) applications, typically cloud providers employ Service Level Agreements (SLAs) to ensure the quality of their provisioned services. Similar to any other contractual method, an SLA is not immune to breaches. Ideally, an SLA stipulates consequences (e.g. penalties) imposed on cloud providers when they fail to conform to SLA terms. The current practice assumes trust in service providers to acknowledge SLA breach incidents and executing associated consequences. Recently, the Blockchain paradigm has introduced compelling capabilities that may enable us to address SLA enforcement more elegantly. This paper proposes and implements a blockchain-based approach for assessing SLA compliance and enforcing consequences. It employs a diagnostic accuracy method for validating the dependability of the proposed solution. The paper also benchmarks Hyperledger Fabric to investigate its feasibility as an underlying blockchain infrastructure concerning latency and transaction success/fail rates.
SW-DBSCAN: A Grid-based DBSCAN Algorithm for Large Datasets

Data clustering aims to discover the underlying structure of data. it has many applications in data analysis and it is one of the most widely used tools in data mining. DBSCAN is one of the most famous clustering algorithms. its advantages are to identify clusters of various shapes and define the number of clusters. Since DBSCAN is sensitive to its parameters which are ε and MinPts, it may perform poorly when the dataset is unbalanced. To solve this problem, this paper proposes a sliding window DBSCAN clustering algorithm that uses Gridding and local parameters for unbalanced data which we will refer to as SW-DBSCAN. The algorithm divides the dataset into several grids. The size and shape of each gird depends on the specimen density specification. Then, for each grid, the parameters are adjusted for local clustering and eventually merging data zones. Experimental results show that this algorithm can help to improve the performance of the DBSCAN algorithm and can deal with arbitrary data and asymmetric data.