Reviewerly/exHarmony huggingface dataset:
Learning event guided network for salient object detection

Salient object detection (SOD) focuses on mimicking the attention mechanism in human vision system. Due to the limited information provided from the traditional image data, the image-based SOD advances are very challenging in some complex scenes. Inspired by the emerging event cameras that provide asynchronous measurements of local temporal contrast over a large dynamic range, we propose a new idea to extract more effective information from the combination of event flow and RGB images. In this paper, we construct an end-to-end joint network for salient object detection (ERSOD-Net), which simultaneously supervise the RGB image and the event data within the corresponding image exposure time. To fully exploit temporal information of event data, Long Short-Term Memory module is utilized to effectively process event and learn salient object event surfaces. Moreover, multi-level feature interaction is designed to fuse two complementary branches of image and event and to predict significance mapping. Finally, to demonstrate the effectiveness of our model, a real Event-RGB SOD dataset (ERSOD) is built by DAVIS camera. Experiments on both benchmark and ERSOD datasets show that the proposed event guided network greatly improves the SOD performance in different evaluation metrics. The code and datasets will be released later at https://github.com/jxr326/ERSOD-Net.
Bangla Spoken Numerals Recognition by Using HMM

Speech is one of the most natural forms of vocalized communication media. Nowadays with the advancement of machine learning, different doors are opened to us for finding several standard ways to step out in the real world. ASR is just like the door to explore the concept of communication through speech between human and digital devices that can recognize speech. In this paper, we have designed a Hidden Markov Model-based isolated Bangla numerals recognition system where the Short-Term Fourier Transform is used for collecting the feature vectors. The defined system achieved 91.50% accuracy for our own dataset of 2000 uttered samples for 10 classes, which gives a satisfied result for this Bangla numerals recognition.
Defense against Adversarial Attacks with an Induced Class

Though deep neural networks have succeeded in various real applications, the prediction performance is significantly degraded when facing adversarial attacks. In this work, we investigate the alternation of the prediction distribution pattern under adversarial attacks and argue that such alternation is the primary reason for performance drop. To this end, we propose a simple yet effective method by introducing an induced class to attract the adversarial attack and thus protect the original classes' prediction order. Experiments on two real-world datasets demonstrate that the proposed method can maintain the prediction performance for both natural and adversarial examples.
Effect of hexagonal synthetic aperture on the optical system with focal error

Abstract We introduce a new approach by using hexagonal synthetic aperture in an optical system and obtaining a high-resolution image. This paper illustrates two cases of an optical system with a hexagonal synthetic aperture where the study was included, First, study the effect of a hexagonal synthetic aperture on a diffraction-bound optical system, Second, study the effect of hexagonal synthetic aperture on an optical system with a focal error of 25 λ and 50 λ . The results showed a significant improvement in the central intensity, a clear decrease in the secondary peaks, and a decrease in the width of the central intensity curve, which gives a high-resolution image as a result of improving the signal to noise ratio.
QoS aware web service selection using orthogonal array learning on fruit fly optimization approach

Purpose This paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue. Design/methodology/approach In this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism. Findings To test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability. Originality/value In this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.
C-DeepTrust: A Context-Aware Deep Trust Prediction Model in Online Social Networks

Trust prediction provides valuable support for decision making, information dissemination, and product promotion in online social networks. As a complex concept in the social network community, trust relationships among people can be established virtually based on: 1) their interaction behaviors, e.g., the ratings and comments that they provided; 2) the contextual information associated with their interactions, e.g., location and culture; and 3) the relative temporal features of interactions and the time periods when the trust relationships hold. Most of the existing works only focus on some aspects of trust, and there is not a comprehensive study of user trust development that considers and incorporates 1)-3) in trust prediction. In this article, we propose a context-aware deep trust prediction model C-DeepTrust to fill this gap. First, we conduct user feature modeling to obtain the user's static and dynamic preference features in each context. Static user preference features are obtained from all the ratings and reviews that a user provided, while dynamic user preference features are obtained from the items rated/reviewed by the user in time series. The obtained context-aware user features are then combined and fed into the multilayer projection structure to further mine the context-aware latent features. Finally, the context-aware trust relationships between users are calculated by their context-aware feature vector cosine similarities according to the social homophily theory, which shows a pervasive property of social networks that trust relationships are more likely to be developed among similar people. Extensive experiments conducted on two real-world datasets show the superior performance of our approach compared with the representative baseline methods.
Research on Webcast Supervision Based on Convolutional Neural Network and Wireless Communication

Action recognition is the technology of understanding people’s behavior and classification from video or image sequences. This thesis uses the deep learning approach for action recognition to realize webcast supervision. This paper uses the convolutional neural network (CNN) and the Gaussian Mixture Model (GMM) to establish the webcast supervision system. At the same time, streaming-based wireless communication network technology is adopted to ensure video transmission speed and quality. Results show that the average detection speed of the system can reach 11.86 frame/s, and the average recognition accuracy is 92.16%, and the missed detection rate is lower than 5%. The design of this system can fully meet the requirements of webcast supervision.
The Implementation of Supply Chain Management in the N95 Respirator Product by using Blockchain Technology

As it starts at the end of the year 2019, the Covid-19 pandemic has already disturbed the condition of human life, socio-culture, and economics globally. As the virus is spreading from human to human, the need for a respirator mask is critical. One very well-known respirator mask is N95. The huge demand and high price of the N95 respirator effect create a bogus mask. This study proposes using blockchain in a global implementation of supply chain management of N95 respirator supply chain activity. Blockchain systems may help to reduce people's worldwide anxiety about the spread of N95 respirator counterfeits. The method used in this research is a systematic literature review and proposing a blockchain framework base on blockchain components. The implementation can use RFID at the level of manufacturers, distributors, and sellers. Using QR code or RFID stickers are possible in the level of customers or buyers. The result of this research is a proposal of blockchain technology for N95 respirator supply chain management systems, N95 respirator supply chain activities workflow, and a use case diagram.
BNPrune: A Channel Level Pruning Method for Deep Neural Network used of Batch Normalization

Deep Neural Networks have become surprisingly popular in target detection due to its fast detection speed and high accuracy in recent years. However, it is difficult to apply deep neural network in edge equipment because of the problems of large amount of computation and complex model. In this paper, a channel level pruning method of neural network is improved, named as BNPrune, where BN indicates the batch normalization layer of the neural network. The input of the method is the original neural network model, and the insignificant channels are identified through sparse training, which will be pruned. And a precise and simple neural network model is achieved. YOLOv4 model and WiderFace dataset are used to verify the pruning method and its performance. The results show that BNPrune can effectively reduce the neural network size by pruning the neural network model at the channel level.
Quantum Color Image Scaling on QIRHSI Model

Scaling operations are widely used in traditional image processing. Therefore, in this paper, an improved quantum image representation based on HSI color space (IQIRHSI) is proposed, which extends the original \(2^{n} \times 2^{n}\) size to general \(2^{{n_{1} }} \times 2^{{n_{2} }}\) size. Then, the quantum algorithms and circuits were designed to implement quantum image scaling. Interpolation was introduced to recover the lost information in the scaled image. The nearest neighbor interpolation method was researched on scaled IQIRHSI to make the interpolation method easy to implement. Finally, the complexity of the quantum circuit for image scaling was analyzed and the process of quantum image scaling was described in detail by examples.
Deep neural network for dynamic symbol

In this paper, EVSF-Network, a deep neural network for dynamic symbols, is presented. EVSF-Network is an extension of VSF-Network, which was developed for the same purpose. The symbols that can be processed by VSF-Network are static symbols, while those that can be processed by EVSF-Network are dynamic symbols. Unlike static symbols, dynamic symbols have the property of changing their meaning according to the situation. It is a hybrid neural network that combines a hierarchical neural network and a chaotic neural network. The hierarchical neural networks perform feature extraction. The chaotic neural network extracts co-occurrence relations between perceptual features depending on situations from the internal state of the hierarchical neural network. The co-occurrence relations between features found by the chaotic neural network are reflected in the output of the hierarchical neural network by attention. First, the binding problem is discussed as a problem related to basic properties about dynamic symbols. A simple example of the binding problem is introduced, followed by an explanation of the synchronization group of neurons, a neuroscience model of the binding problem. EVSF-Network expresses situation dependency in dynamic symbols by means of synchronization groups. Next, the structure of EVSF -Network and its learning procedure are presented. Following that, an experiment using an image recognition task dealing with the binding problem by Rosenblatt et al. and their results are presented. Finally, a discussion about future extensions to the EVSF-Network is shown.
Path Modeling based on Entity-Connectivity for Knowledge Base Completion

Knowledge reasoning aims to infer missing facts based on existing facts in the knowledge base(KB), which is also known as knowledge completion. The representation learning method uses certain formula as a constraint to embed the triples of facts (head, tail, relations) into the low-dimensional continuous vector space. Since there are not only direct inference pattern but also indirect inference pattern in fact relation inference. PTransE introduce graph feature model into the embedded and make the indirect relation path also participate in translation training. However, indirect relationships has widened the differences on the number of high-connectivity entities (multi-connectivity entities) and low-connectivity entities in the training. The more closer related entities in the graph will be more closely related to each other, and the weaker ones will be weaker, which leads to the unfitting of the low-connectivity entities. In this paper, we propose PTransE based on entity-connectivity (C-PTransE) model to optimize the evaluation function by adjusting the proportion of distance score and path score according to the high and low connectivity of entities. ln this way, the isolation of the high-connectivity entity from the low-connectivity entity can be alleviated, and the overfitting of the high-connectivity entity can be alleviated. A large number of experiments on real data sets show that this method can make link prediciton more effectively.
FreeStyleGAN

Current Generative Adversarial Networks (GANs) produce photorealistic renderings of portrait images. Embedding real images into the latent space of such models enables high-level image editing. While recent methods provide considerable semantic control over the (re-)generated images, they can only generate a limited set of viewpoints and cannot explicitly control the camera. Such 3D camera control is required for 3D virtual and mixed reality applications. In our solution, we use a few images of a face to perform 3D reconstruction, and we introduce the notion of the GAN camera manifold, the key element allowing us to precisely define the range of images that the GAN can reproduce in a stable manner. We train a small face-specific neural implicit representation network to map a captured face to this manifold and complement it with a warping scheme to obtain free-viewpoint novel-view synthesis. We show how our approach - due to its precise camera control - enables the integration of a pre-trained StyleGAN into standard 3D rendering pipelines, allowing e.g., stereo rendering or consistent insertion of faces in synthetic 3D environments. Our solution proposes the first truly free-viewpoint rendering of realistic faces at interactive rates, using only a small number of casual photos as input, while simultaneously allowing semantic editing capabilities, such as facial expression or lighting changes.
EEG Model

Social sharing of emotions (SSE) occurs when one communicates their feelings and reactions to a certain event in the course of a social interaction. The phenomenon is part of our social fabric and plays an important role in creating empathetic responses and establishing rapport. Intelligent social agents capable of SSE will have a mechanism to create and build long-term interaction with humans. In this paper, we present the Emotional Episode Generation (EEG) model, a fine-tuned GPT-2 model capable of generating emotional social talk regarding multiple event tuples in a human-like manner. Human evaluation results show that the model successfully translates one or more event-tuples into emotional episodes, reaching quality levels close to human performance. Furthermore, the model clearly expresses one emotion in each episode as well as humans. To train this model we used a public dataset and built upon it using event extraction techniques1.
Implementing the Comparative Analysis of AES and DES Crypt Algorithm in Cloud Computing

Cloud computing helps users to access information over the internet. Efficiency, scalability and resource consumption are all optimized in cloud computing. Data security is an important concern in cloud computing that can be addressed with cryptography. Cryptography can be defined as a measure to protect confidential information that must be protected from others who are not intended to be viewed in the same way. The use of cryptography is widespread from small schools and colleges to the social media platform that we addicted to. AES and DES cryptographic algorithms are the main concern of this paper and also implementing which algorithm is best in the basis of time and also comparing the differences between these two algorithms.
Electronic Medical Record Sharing Solution for Editable Blockchain

The traditional medical record management system combines cloud storage and encryption technology, which has the advantages of high efficiency, environmental protection, and easy storage. However, the high economic value of central cluster storage and electronic medical records makes it vulnerable to hacker attacks. In order to solve the above problems, this article combines the editable blockchain and proxy re-encryption technology, and proposes an electronic medical record sharing scheme with on-chain data editing function. It realizes the sharing of medical records between medical institutions, the call of medical records during patient referral, and the secondary update and redundancy elimination of electronic medical records. In terms of security, this article is based on the decision-making bilinear Diffie-Hellman hypothesis, which has the indistinguishability of ciphertext under the selected plaintext attack, and realizes the safe sharing and storage of electronic medical records. Finally, we combined the PBC library for efficiency analysis and simulated the data sharing protocol. The results show that this scheme has higher efficiency and strong practicability during the shared medical records.
On Illumination Vector Quantities Due to Area Light Sources: Comparison of Two Calculation Approaches

The concept of illumination vector quantities at a given point in three-dimensional space has been proposed since long time ago, but it is mostly described for scenes with point light sources. In real scenes with area light sources, numerical approximations are required to estimate the illumination vector quantities, which can be done by discretising the area into an array of point sources. Two different approaches are proposed in this study; Approach 1 is applicable in the design phase, where the positions of the source and receiver point are exactly known. Approach 2 is applicable during field measurement, based on the obtained cubic illuminances. This study aims to determine the required number of point source elements in the computation and to investigate the difference between both calculation approaches. The proposed concept is demonstrated using a scenario with horizontal, square source and a scenario with vertical, rectangular source, with various luminous intensity distribution patterns, by observing various illumination vector quantities. Grid sensitivity analyses suggest that the largest grid size satisfying the five-to-one approximation rule shall yield quantities that converge within no more than 5% to the final value. For a given size of area source, at a given distance, the differences of illumination vector quantities obtained from both calculation approaches are expected to be approximately the same, regardless the luminous intensity distribution. The estimation of cylindrical illuminance is generally more accurate than that of scalar illuminance.
A Method for Identifying Potential Internal Violators Based on the Analysis of the Tone of Messages from Users of Social Networks

The purpose of the work is to study social network users as potential internal violators through the analysis of the tonality of text messages. To achieve the goal, the following tasks have been set and solved: 1) Determine the basics of sentiment analysis for text messages; 2) Highlight the lack of existing methods for identifying internal violators; 3) Propose and develop a preventive method for identifying potential internal violators based on the analysis of the sentiment of text messages. An analysis of the subject area of the study was carried out, which included a review of existing methods for identifying insiders. The paper analyzes internal violators with the identification of potential ones, and also considers the means of processing natural languages and identifies approaches to the classification of the tonality of the text.. The article solves the problem of developing a method for identifying potential internal offenders, based on the analysis of the sentiment of text messages. An analysis was made of the funds allocated and the methods that are appropriate to achieve the research objective. The developed method is described. As a result of the study, a method was proposed that allows one to identify potential internal offenders by analyzing the sentiment of their text messages on social networks.
Predicting Number of Bugs before Launch: An Investigation based on Machine Learning

Identifying and minimizing the number of bugs before release is a high priority of any team working on software development. This can be achieved by Machine Learning (ML) models. By using particular aspects of the code, ML Models can predict the number of bugs that are possible post launch. We use a public dataset consisting of 15 Java projects from GitHub as our training and test dataset. We use five ML models for our investigation: Multilayer Perceptron, K-Nearest Neighbors, Linear Regression, Logistic Regression, and Decision Trees. We conduct a preliminary investigation to evaluate how these ML models perform in predicting bugs. The results show that Linear Regression outperforms the other four ML models in finding the number of bugs post release.