Reviewerly/exHarmony huggingface dataset:
Explore double-opponency and skin color for saliency detection

Recent advances in salient detection have exploited the foreground or background information to assist other saliency cues such as contrast to achieve state-of-the-art results. However the problem remains challenging. For example, human skin color is easily overlooked during saliency detection. Simulating the human visual mechanism to improve the current algorithm, we propose a saliency model based on the color-opponent mechanisms of a certain type of color-sensitive double-opponent (DO) cells in the primary visual cortex (V1) of human visual system. Firstly, DO cells with concentric receptive fields (RFs) can detect region contrast for yielding foreground saliency map. Our approach is intuitive and easy to interpret, and it allows fast implementation. Then, skin saliency map is built in a way that is different from high-level factors such as face detection, combining skin region and spatial Euclidean distance weight in the RGB space, and the significant skin features can be obtained effectively. Finally, a linear fusion strategy is proposed to integrate different saliency maps. Experimental results with three well-known benchmark databases demonstrate that the proposed method can achieve competitive performance when compared to state-of-the-art methods. Saliency regions contain important skin features compared to other traditional methods.
The Combination of IoT-Sensors in Appliances and block-chain Technology in Smart Cities Energy Solutions

The Internet of things is becoming an essential part of people's daily lives especially in smart cities where it is used to monitor processes and objects. The multifaceted procedures and decentralized nature of blockchain are integral for solving energy consumption challenges experienced in smart cities. The two technologies are the emerging techniques which can be integrated to bridge the gap between the physical and cyber world. This paper discusses the combination of blockchain technology and IoT-sensors to achieve sustainable futuristic energy solutions through effective monitoring of both the physical and electrical parameters. Consumption of electricity on a daily and weekly basis can be evaluated for home appliances and this solution helps in determining the power consumption rates and how energy costs can be reduced based on the real-time data provided. Some of the solutions include processing energy transactions efficiently, real-time monitoring and analysis of energy use, a decentralized system for self-consumption and lastly, allowing more efficient power distribution and allocation of resources based on data provided by the smart sensors.
A Modified Tiny Asymmetric Encryption for Secure Ftp to Network

The target of this venture is to give the protected correspondence among the associated frameworks in the system. It gives the vital validation to the record moving in the system transmission. It comprises of 3 modules in particular encryption and unscrambling module, secret key verification to the information that needs to transmit through system. In this system, File Transfer Protocol can be used to execute Server-client innovation and the document can be scrambled and unscrambled by sending the end client through attachment programming of the end client.
Exploring The Dimension of DNN Techniques For Text Categorization Using NLP

The natural language processing (NLP) area has been magically transformed by the Deep Neural Network (DNN). The two variations of Neural Networks, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) can handle the different NLP tasks effectively. CNN is based on feature extraction using N-grams at higher levels. The RNN can be effectively used to model the sequential information. Choosing a neural technique for the various NLP tasks is always a challenge. The paper focuses on the evaluating the performance of the two DNN techniques CNN and RNN for the various NLP tasks, so that appropriate technique can be selected.
Reliability Management Framework and Recommender System for Hyper-converged Infrastructured Data Centers

Cloud computing is a miracle of the 21st century that grasps its significance in almost every field of the digital world. In this era, most of the organizations look towards cloud service providers to acquire resources such as processing power, data storage, networks, servers, and etc. After the evolution of clouds, the paradigm shift towards the development and maintenance of data centers. The traditional infrastructure of data centers allows the user to select resources from different vendors. On the other hand, in converged data centers resources are merged and sold as a single unit. The recent trend is to build the data centers on the hyper-converged technique in which storage, computational and networking resources are merged together as a single unit with a layer of virtualization. In hyper-converged infrastructure (HCI) data centers reliability management is one of the challenging tasks because of modular dependency and complex layout. This paper presents, a hierarchical framework to measure the reliability of the HCI data centers. The proposed framework measures the reliability of networking, storage, and computational resources on the basis of their availability, outage, and downtime with the help of Neural Networks. In the case of an unreliable situation, the framework recommends the areas that need to be fixed. Moreover, the recommender system is developed based on the proposed framework for the data center to measure reliability. The experimental results show that the proposed framework gives 94% accuracy. The development of such a system enhances the reliability of the data center.
Application of RLSA for Skew Detection and Correction in Kannada Text Images

The presence of the skew in a captured document image through a photographic camera, mobile camera or scanner is inevitable. In a document image detection and correction of skew are challenging phases before further processing like segmentation and analysis. In this paper, Run Length Smoothing Algorithm (RLSA) is proposed for the detection and correction of skew for handwritten Kannada document images. The proposed work has mainly two parts, the first part is preprocessing of a document using methods like thresholding, the maximum gradient for extraction of text and text line area with no loss of any data. The second part is skew detection and correction. The algorithm RLSA is used row and column-wise of a document image. The RLSA is applied for skew detection to determine skew (slant) angle further the document is turned in the anti-clockwise direction with the preferred angle, which will remove the skew of a document that has occurred while taking the photocopy of the document. The performance proposed method is evaluated for handwritten Kannada documents; the experiment outcomes are significantly better.
A Coarse-Grained Dual-Convolver Based CNN Accelerator with High Computing Resource Utilization

Deep learning technologies have been developed rapidly in recent years and have played an important role in our lives. Among them, convolutional neural network (CNN) performs well in many applications. The quality of result is generally getting better as the number of convolutional layers increases, which also increases the computational complexity. Hence, a highly resource-efficient accelerator is demanded. In this paper, we propose a new CNN accelerator that features a delay-chain-free input data aligner as well as a dual-convolver processing element (DCPE). Our architecture does not require delay chains with a large number of registers for input data alignment, which not only reduces the area and power but improves the overall resource utilization. In addition, a set of DCPEs shares the same input aligner to produce multiple output feature maps concurrently, which offers the desirable computing power and reduces the external memory traffic. An accelerator instance with 8 DCPEs (144 MACs) has been implemented using TSMC 40nm process. The internal logic only consumes 285K gates and the total internal memory size is merely 44KB. As running VGG-16, the average performance is 190GOPS (@750MHz), the resource (MAC) utilization reaches 8S.3%, and the energy efficiency is 481GOPS/W.
Fast Simulation of Additive Generalized Gaussian Noise Environments

Due to the complexity and associated inordinate computing time required to generate generalized Gaussian random variates, it is necessary to find faster ways to simulate events caused by additive generalized Gaussian noise. Following a concise review of the two known exact methods of generating generalized Gaussian random variates, an exact weighted counting approach is investigated for rapid estimation of the probability of events caused by the presence of additive generalized Gaussian noise. The weighted counting method reduces simulation times typically by 104 times, and will allow simulation of complex systems not previously feasible.
Evaluating Onset Times of Acoustic Emission Signals Using Histogram Distances

Determining the onset of a transient signal, for example in seismograms, acoustic emission (AE) signals, or ultrasonic signals is very important in non-destructive process monitoring and geophysics. In some cases, the AE from a malfunction is relatively weak, with a low signal-to-noise ratio. Thus, the signals are often hidden, making it rather difficult to separate them from the background noise. The present work proposes a new method of evaluating onset times based on bin-to-bin histogram distances measured with the Bhattacharyya coefficient. A criterion and a standard procedure for determining an onset are formulated. A key parameter, window length, was discussed in detail. Tests on AE signals from a pencil-lead break, single-grit scratching, and filament breakage reveal the feasibility and effectiveness of the proposed method. This method is believed to be especially appropriate when the emission signal strength of the target malfunction is lower than environmental AE signals generated by other stationary sources. It can also be used as an alternative method for identifying onsets in AE signals in process monitoring and other fields.
EPPR: Using Blockchain For Sharing Educational Records

There have been recent, marked increases in the challenges of privacy, data interoperability and quality of Educational Professional Personal Record (EPPR). This calls into question the current model, in which different parties generate, exchange and monitor massive amounts of personal data related to EPPR. Ethereum blockchain has demonstrated that trusted, auditable transactions is visible using a decentralized network of nodes accompanied by a general ledger. Thus, the rapid development of educational and professional data generators such as online universities and distance learning, learners need to engage in detail into their EPPR as well as the educational and professional data generators. In this paper, we propose a novel decentralized approach to manage EPPR using Ethereum blockchain technology. The decentralized approach provides the owner of the EPPR a comprehensive immutable log and ease of access to their educational records across the educational record editors and consumers. Utilizing Ethereum blockchain features, can provide solutions with the main concerns of exchanging data between parties such as privacy, accountability and data interoperability. The aim of this approach is to also facilitate educational stakeholders (universities and employing agencies) to participate in the network as blockchain miners rewarded by pseudonymized data in compliance with General Data Protection Rules (GDPR) in United Arab Emirates (UAE).
Monetary policy and financial development in Africa: Do governance mechanisms matter?

Orientation: The financial system performs essential role in the mechanisms through which economic activities translate into economic growth and development of African economies, especially through their role in the allocation of finance from the surplus units to productive activities. Research purpose: This article examines how monetary policy instruments impact the level of financial development in 37 African countries over the period 2002–2015. The article analyses how the governance systems in these countries can have both first- and second-order effects on the level of financial development through monetary policy mechanisms. Motivation for the study: The need for the study emanates from the move toward monetary integration enshrined in the charter which culminated in the formation of the African Union. Inadequate discussion on the topic within the African continent also engineered interests in this investigation. Research approach/design and method: To deal with any endogeneity issues, we perform the estimations using the dynamic general method of moment model. Main findings: The results show that monetary policy instruments in Africa promote higher level of financial development. Also, financial development is stronger in the wake of weak governance systems. However, the interplay between effective governance and effective monetary policy has stronger positive impact on the level of financial development in Africa. Practical/managerial implications: The study calls for the institution of responsive monetary policies that harness strong institutions to promote financial development. Contribution/value-add: The article highlights the contributions of strong institutions that stimulate the contribution of monetary policy in effective financial intermediation.
Design and implementation of intelligent creation platform based on artificial intelligence technology

In this article, we have designed a fusion media platform that integrates intelligent creation and editing based on Artificial Intelligence Technology. The system includes a content acquisition and intelligent creation module, a multimedia editing module, and a multi-channel integrated publishing and operation module. The content collection and intelligent creation module utilizes the audio and video capture technology of the mobile computer and natural language processing technology to achieve rich media content acquisition and smart embellishment. The multimedia editing module can realize text matching and modification trace preservation, associated content intelligent extraction, and audio and video processing. The multi-channel integrated publishing module utilizes crawling technology and auth2.0 authentication technology to realize seamless interfacing of new media channels such as WeChat, Weibo, headline, Sohu, TV production system, radio production system, newspaper typesetting system, etc. At the same time, the platform can also capture operational data through crawler technology to meet the needs of intelligent operations. The platform has been piloted in a number of press and publication media organizations. Through the results of the operation and the user feedback, the platform can provide a good service for news media workers from content creation, editorial review to channel release. This research will provide a comprehensive working platform for the news publishing media industry, which is of great value for promoting the comprehensive fusion of the media.
Neural Network based Block-Level Detection of Same Quality Factor Double JPEG Compression

Detecting double JPEG compression with the same quality factor is an open research problem in image forensics. The subtle artifacts of double JPEG compression with the same quantization matrices are often indistinguishable by traditional approaches such as histogram-based feature extraction. Existing approaches fail to effectively classify smaller size patches, due to the absence of sufficient information at smaller scales. To tackle this, we propose a new feature based on the difference of quantized DCT coefficients, which is relatively invariant to the size of the image or image patch. For classification, we utilize the multi-layer-perceptron (MLP) network. We compare our results on multiple patch sizes and quality factors, on the UCID dataset with the existing approaches. Using our proposed feature in conjugation with the existing feature vector along with the use of MLP, we observed a maximum of 1.52% increase in accuracy for smaller sized patches (128 × 128), compressed with a quality factor of 60.
Multi-class Breast Cancer Classification by a Novel Two-Branch Deep Convolutional Neural Network Architecture

One of the main reasons for death among women is breast cancer. The traditional diagnosis process of breast cancer is time-consuming and expensive. Also, an early cancer diagnosis may reduce the breast cancer death rate. With the help of computer-aided diagnosis system, the efficiency increased and the cost is reduced of the cancer diagnosis. Traditional classification methodologies are based on feature extraction techniques. Currently, deep learning techniques have become the alternative solution for diagnosis and overcame the problems of the handcrafted features methods. Increasing the depth in a deep convolutional neural network makes the network suffer from gradient vanishing problems, which are not caused by overfitting but instead by an increase in depth. Therefore, our proposed network is designed based on the idea of increasing the width of the network. A novel two-branch deep convolutional neural network is proposed for the classification of histopathological breast images. The proposed network is trained on the dataset of ICIAR-2018 to classify images into four classes; invasive carcinoma, in situ carcinoma, benign lesion, and normal tissue image. The proposed network is beneficial for gradient propagation as the error can be back-propagated through multiple paths. It also helps to combine different levels of features at each step of the network since it is a two-branch network. The proposed network is superior to the existing methods by achieving a patch-wise classification accuracy of 83.6% and image-wise classification accuracy of 91.3% on the divided part from the training set. Moreover, we have achieved an image-wise classification accuracy of 89.4% on the unseen test images of ICIAR-2018.
An Efficient Public Key Searchable Encryption Scheme for Mobile Smart Terminal

With the wide application of the mobile smart terminals, the data privacy protection of the mobile smart terminals stored in the cloud is more and more important.Public key encryption with keyword search (PEKS) and secure channel free PEKS (SCF-PEKS) have been proposed for public key searchable encryption previously.However, the security of keyword search is far from enough.In addition, these schemes are mostly based on bilinear pairing and the computational efficiency is relatively low.In this paper, we propose a novel non-bilinear pairs SCF-PEKS schemes for mobile smart terminal that offer a high computational efficiency along with better security assurances than that of the existing alternatives.Without random oracle model, we prove the security and privacy of the scheme's keyword ciphertext and keyword trapdoor through the game hopping method.Therefore, the scheme is capable of resisting outside online keyword guessing attack and inside offline keyword guessing attack.Based on the comparison and experimental results, the scheme turns out to be secure and practicable.
Real-time Scene Change Detection with Object Detection for Automated Stock Verification

Automation is a process of utilizing technology to reduce human efforts. In this paper, we propose a computer vision-based automated system for stock management in the supermarkets. The proposed system will help to reduce the manpower required in a supermarket by continuously monitoring the availability product in the supermarket and reporting the useful information to the concerned person automatically. The key idea behind the proposed scheme is that a few low-cost cameras will be placed in the supermarket which will help to capture the videos of the product racks in the supermarket. The presence of human beings are identified by using a structural similarity index (SSIM) based scene change detection technique, further, an object detection technique will be used to count the number of items present in the specific p roduct r ack. If t he number of items present in a particular rack goes below a threshold limit, a short message service (SMS) and/or email will go the concerned authority. To make it more comfortable, a product identifier ( printed) w ill b e k ept j ust b elow t he p roduct racks. An optical character recognition module in the proposed scheme will identify the product identifier a nd i t w ill b e m entioned in the SMS or email which will help the supervisor for scheduling the replacement of the items in the racks. The experimental study is carried out by placing sample items on a rack and the mobile camera is used as an IP camera with the help IP webcam android application for the monitoring purpose. The experimental study shows that the proposed scheme will work reliably in a supermarket environment.
Enhanced Image Steganography with PVD and Edge Detection

Steganography is the concept of concealing information or data by embedding it as secret data into various digital medias in order to achieve higher security. Many Steganographic algorithms are proposed for this. The ability of human eyes as well as invisibility remain the most important and prominent factor for the security and protection. The most commonly used security measure of data hiding within images (Image Steganography) is the Least Significant Bit (LSB) system.Yet it is ineffective against steganalysis, and lacks proper verifications. Thus the proposed system of Image Steganography using PVD (Pixel Value Differentiating) proves to be a better choice. It compresses and embeds data in images at the pixel value difference calculated between two consecutive pixels. To increase the security, another technique called Edge Detection is used along with PVD to embed data at the edges. Edge Detection techniques like Canny algorithm are used to find the edges in an image horizontally as well as vertically. The edge pixels in an image can be used to handle more bits of messages, because more pixel value shifts can be handled by the image edge area.
Verification Method for Accumulative Event Relation of Message Passing Behavior with Process Tree for IoT Systems

In this paper, we proposed a verification method for the message passing behavior of IoT systems by checking the accumulative event relation of process models. In an IoT system, it is hard to verify the behavior of message passing by only looking at the sequence of packet transmissions recorded in the system log. We proposed a method to extract event relations from the log and check for any minor deviations that exist in the system. Using process mining, we extracted the variation of a normal process model from the log. We checked for any deviation that is hard to be detected unless the model is accumulated and stacked over time. Message passing behavior can be verified by comparing the similarity of the process tree model, which represents the execution relation between each message passing event. As a result, we can detect minor deviations such as missing events and perturbed event order with occurrence probability as low as 3%.