Reviewerly/exHarmony huggingface dataset:
Trilateral GAN with Channel Attention Residual for Semantic Image Synthesis

In this paper, a novel method for synthesizing photo-realistic images from semantic label maps using GANs is proposed, which is an ideal and challenging task in computer vision and image synthesis. Due to the sparsity of the information contained in semantic label maps, it is difficult for some existing methods to achieve satisfactory synthesis effect. This paper proposes a trilateral generative adversarial network to support multi-directional transmission between images of different resolutions, called TrilateralGAN. Compared with the traditional single-directional transmission, the design of our TrilateralGAN network can better retain the information in the original image to avoid loss of details. In addition, we further propose a new channel attention residual as the main part of the TrilateralGAN network. This part can enhance the retained information to varying degrees, which can make the image synthesized by TrilateralGAN have clearer edges and richer details. The experimental results on Cityscapes and ADE20K datasets demonstrate the advantage of TrilateralGAN over the state-of-the-art approaches, regarding both visual quality and the representative evaluating criteria.
Interactive eBook Applications in the Context of the Modern Media System: Release and Promotion Strategies

The article treats digital technologies impact on modern ways of content producing, promotion and consumption. The focus is on interactive book apps as innovative publishing projects that use interactive multimedia content. Interactive book apps are designed for tablets and smartphones and combine different elements (texts, images, video, audio, animation and so on). The author traces back in short e-books prehistory from Kindle gadgets to the time of innovative iPad tablet that resulted in a new generation of e-books. Different types of interactive book apps are highlighted in the article on the base of their purposes and genres (for examp.: fiction, non-fiction, for kids and so on); some of these apps have elements of critical edition. From theoretical point of view interactive book apps can be treated within the frame of the media convergence approach that tends to blur boundaries between contents of different types. Exactly from these stem theoretical and practical difficulties as follows: in the first case – with notions that could be applied to them, in the second one – with tools for their search, identification, systematization and preservation in archives. The article analyzes a number of limitations (technological, software, legal, socio-cultural) that hinder their dynamic development in the modern media system. Main strategies of apps’ output and promotion are generalized and a number of restrictions that hinders their actual development in the media system is analyzed.
Classification of Extreme Reviews from Online Products Using RNN Model

Online reviews on a particular product play an important role in the sales of a product. There exist genuine reviews where the customers review their opinion about the products out of joy or frustration. But, from the study, we came to know that some people get hired by certain brands to provide reviews which are not actually genuine. This reviewing is also done as group. Although the previous methods exist for detecting fake reviews, but they are just limited to identify the individual reviews. In this paper, the group reviews which are concentrating on a particular brand will be detected as extremist reviews. The characterizing is done using the algorithm RNN algorithm by which is also used to train data by itself. RNN is recurrent neural networks from TensorFlow which is same as the regular artificial neural networks, but the only difference is RNN consists of an additional memory for computations. Here, we used different layers of the network like LSTM, dense and dropout layers, and a softmax layer for classification of the reviews as extremist or moderate.
Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning

Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more interlocutors, exchanging information with each other and having an inherent hierarchical structure based on the sequence of utterances by different speakers. This paper proposes a simple but effective hybrid approach that consists of two modules and uses transfer learning by leveraging pretrained language models (PLMs) to generate an abstractive summary. The first module highlights important utterances, capturing the utterance level relationship by adapting an auto-encoding model like BERT based on the unsupervised or supervised method. And then, the second module generates a concise abstractive summary by adapting encoder-decoder models like T5, BART, and PEGASUS. Experiment results on benchmark datasets show that our approach achieves a state-of-the-art performance by adapting to dialogue scenarios and can also be helpful in low-resource settings for domain adaptation.
Energy efficient workflow scheduling in Dynamic Voltage Frequency scaling enabled cloud using Metaheuristic approach

Dynamic voltage Frequency Scaling is widely used in data centers for reducing the power consumption. When DVFS is integrated into cloud workflow scheduling, the work is allotted to the virtual machines that has minimal operating frequency. Instead, it will be better if it is optimally allocated based on the overall list of VMs present and the entire requirement. To achieve this, Memetic Dynamic Voltage/Frequency scaling (MDVFS) algorithm is proposed where the task assigned is redistributed to less utilized processors and free processors are released. The experimental results show reduction in power consumption by 30–50 % for different parallel applications.
CheckMate: A Bucket List App Facilitating Collaborative Social Interactions

CheckMate, a social media platform, enables users to complete their bucket lists efficiently and effectively organise them with editorials, peer learnings, and validations. By adopting a user-centred design process, we explored how CheckMate would enable people to interact, collaborate and achieve goals and desires with people sharing the same interests. It aims at organising the user’s bucket list with active tracking of one’s achievements, gamified elements for added incentives, and intelligent suggestions based on one’s interests and past accomplishments to create memories and human-human interaction through collaboration. CheckMate’s goal is to facilitate support and motivation by providing a platform to interact with people with similar interests and gain insights from one another. This paper will discuss user research and design processes and their importance in our application development.
A Hidden Markov Model-Based Tagging Approach for Arabic Isnads of Hadiths

Hadith judgment implies checking the validity of Hadith to decide whether it is correct (trustworthy) or false (bogus). “Matn” and “Isnad” are the main constituents of Hadith; “Matn” is the sayings of the prophet, whereas “Isnad” represents the narrators’ series. The first step of Hadith judgment is the extraction of narrators’ names, after that, the rules of judgment, which were set out by Hadith’s scientists, could be implemented, three of these rules are particularly related to the narrators’ series, and these rules are continuity of the transmission chain, the trustworthiness of the narrators, and the preciseness of the narrators. Therefore, to check the authenticity of Hadiths, the three conditions must be satisfied, and to do so, the narrators’ names must be extracted first. Isnad contains many words and phrases called “Isnad-Phrases”; these phrases have many types or categories called part of Isnads (POIs) like Narrator-Name, Prophet-Name, and Received-Method. A lot of computational research studies suggest serving Hadith sciences by extracting the narrators’ names and other POIs using various approaches. This study presents a new hybrid approach founded on the hidden Markov model (HMM) and gazetteer lists to process “Isnad.” The approach objective is to expect all POIs in the Isnad including narrators’ names. The experiments carried on 1,000 Hadiths from “Sahih Muslim”: 900 Hadiths as training dataset and 100 Hadiths as testing dataset, and the results show a noteworthy accuracy for the proposed hybrid approach.
A fast manhattan frame estimation method based on normal vectors

Abstract In most human made scenes, such as high‐rise urban city or indoor environment, the surface normal vectors or direction vectors are concentrated in three orthogonal principal directions. The scene of such a pattern is called Manhattan World (MW), and the coordinate frame formed by the three principal directions is called Manhattan Frame (MF). MF estimation methods have been applied to many different fields, such as scene reconstruction, Visual based Simultaneous Localization And Mapping (V‐SLAM) and camera calibration. In this paper, we propose a novel MF estimation method based on a set of normal vectors. A cost function of normal vectors and MF axes is introduced based on the trigonometric function. For computational purpose, the cost function is significantly simplified by making use of vector dot and cross products, and the reduced cost function only involves 14 scalar parameters that need to be computed with O(n) complexity. The experimental results show that the proposed MF estimation method has excellent real‐time performance and gives high accuracy on both the virtual and real‐world benchmark datasets of different sizes.
Biometric authentication utilizing fuzzy extractor with PSO based security ensuring the data security mechanism as Trio in Cloud

Cloud computing is widely preferred by various organizations due to its numerous advantages such as lower costs, better security, and faster processing times. The most critical data stored in the remote servers are protected by firewalls and encryption, at the same time, the user’s essential data may be accessed from the remote cloud server without their control. This kind of data storage facility introduces various security complications. In order to prevent these issues, we have proposed a method that uses a Biometric authentication method with PSO to provide high-level security. The method combines a pseudo-integer authentication scheme with a secret OTP that can be used to authenticate an individual. A Tri-level security mechanism model is proposed to store three types of data classification namely, No Privacy, Privacy with Trusted Provider and Privacy with Non-PNR. The proposed method provides a uniform security scheme for all the classifications using Adaptive ElGamal with Menezes-Qu-Verstone, that prevents data loss and corruption caused by insiders in the cloud. Further Seed Block is utilized to recover and extract data without loss. It does so by effectively extracting the data from the formatted disks. It is then followed with the decryption process, as if the data are requested by the user. Our framework aims to provide a secure and authenticated environment for accessing data. It features an improved authentication process and reduced complexity.
Review of existing variants of Grey Wolf Optimization algorithm handling Load Balancing in Clouds

Cloud computing has earned lot of awarenes in the Information Technology and now appeared as the next level in the evolution of Internet. But now it has been observed that the Load balancing (LB) is one among various challenges in cloud computing that needs to be resolved to perform the accurate operations on cloud and also to obtain the rapid development in the the sphere of cloud computing. The demand of various customers all over the world for the services used to increase the load rapidly. Therefore load balancing required to equally distribute the workload over each and every virtual machine in the cloud system hence as a result the throughput increases and the response time minimizes. The aim of load balancing is to build the client satisfaction, resource utilization maximisation and improvement in the cloud system performance leads to reduction in energy consumption and heat dissipation. In the present paper, the standard Grey Wolf Optimisation algorithm for load balancing is demonstrated for the cloud environment. Also the other versions of Grey wolf optimisation has been studied to know the issues related to them and additional functionality required by them to achieve the higher system performance. Furthermore, according to the surveyed research papers it has been seen that now the performance of the proposed hybrid Grey Wolf Optimisation algorithms is simulated by using Cloudsim simulator on the basis of different parameters such as throughput and response time etc.
Object Detection System for Self-Checkout Cashier System Based on Faster Region-Based Convolution Neural Network and YOLO9000

This paper will discuss an alternative to replace barcodes in the form of object detection based on deep learning which reads the overall feature of an object, so defects and irregularly shaped products do not hinder the reading of the product in processing purchases. Two deep learning-based object detection models, Faster Regional Convolutional Neural Network (Faster R-CNN) and You Only Look Once 9000 (YOLO9000), were tested for their performance in the training phase and real-time implementation. The result of training phase testing shows that the Faster R-CNN model is more accurate and efficient with an mAP of 88.2%, a training time of 1175.6 seconds/epoch, and memory usage of 1.611 GB. The result of the real-time testing of the model shows that Faster RCNN has a high accuracy of 67.1%, but YOLO9000 has a very fast prediction speed of 0.023 seconds/frame. The result of simulation testing shows that YOLO9000 can read products at a speed of 67.40 seconds which is comparable to the speed of a barcode scanner-based cash register that can read products at a speed of 65.77 seconds.
Sentiment Analysis on Tweets Using Emojis to Help the Distressed

In the recent years, many people use social media such as twitter to express distressed emotions. Proofs establish that depressed people utilize Twitter to express their feelings. To provide help to the distressed, we believe sentiment analysis serves this purpose because it is a field of study that analyses sentiments and emotions from the text. However, there is a need for an additional factor to be considered while performing sentiment analysis to make the process more effective and accurate. Emojis can fulfil this purpose since it can express one’s emotions effectively irrespective of their diversities. Sentiment analysis on tweets through emojis would classify the tweets into different category of emotions, thereby making the process of extraction of emojis indicating negative feelings trouble-free. From obtained results, we intend to help the affected through an in-platform reply to the tweet with the details of appropriate authorities who will do the needful.
Design of Interior Design Database Based on Ant Colony Algorithm

Interior design can be said to be the personal pursuit of contemporary people after the improvement of living standards. People's requirements for information technology processing are getting higher and higher. In the interior design work, the design database plays an important role in it. The purpose of this article based on ant colony algorithm research and design of interior design database is to organize interior design resources and provide people with some interior design methods and opinions, in order to achieve beautiful housing and meet users’ expectations. This article mainly applies the experimental method and the comparative method to compare and analyze the unit test and integration test results, and get the corresponding data results. The data shows that in the database system designed in this paper, its performance indicators can basically reach 80%, and its security performance has a high level, which can protect user privacy.
Design Multi-Level Security Communication System Based on Encryption and Steganography

Today, the amount of data generated is enormous. Despite the creation and transmission of this data on the internet, the presence of many threats to this data is not at all surprising, but on the contrary, the security of this data is a very important issue. There are always different methods that can be used to somehow secure data against security threats. One such rapidly developing technique is the science of steganography. There are several ways to hide information. One of the most powerful methods against data theft is steganography based on encryption using chaotic systems. Chaos systems are very sensitive to initial conditions. This makes it difficult to understand the password key easily. In this research, a system is designed to protect confidential data, which is represented by (image, sound), where the image is encrypted based on (Duffing chaotic map), and after encrypting it, it is hidden in the cover image and the sound is used as confidential information to be protected. This is done by encrypting the sound using (Modify Arnold Cat map), after that it will be hidden in the cover image. After that, the hidden data (stego image) is sent through a public channel. These messages are then retrieved in the receiver. The proposed method, creates a multi-level secure system. The simulation results also showed the possibility of using the proposed method in practical systems.
Information System of Constructive English Teaching Platform Based on RBF Algorithm

With the rapid development of information technology, network technology has become an indispensable part of human survival and progress. In teaching, the use of computer-aided teaching system can well solve the problems of large amount of information and limited knowledge for teachers and students under the traditional education mode. The information construction of English curriculum based on RBF algorithm is an important and innovative work. Firstly, this paper first introduces the objectives of constructive English teaching and the connotation of learning view of constructivism theory, then introduces the characteristics of English teaching informatization, and designs and develops the information system of constructive English teaching platform according to RBF algorithm. Finally, the running performance of the system is tested. The test results show that the maximum response time and minimum response time of the system are about 1 ms. The maximum response time and minimum response time when processing sessions are maintained at about 1–2 ms. From the overall point of view of the system, this platform realizes the management of student information, course courseware and teacher users, and can effectively interact data in the network environment.
Enhancing the security of data in cloud computing environments using Remote Data Auditing

The main aim of this report is to find how data security can be improved in a cloud environment using the remote data auditing technique. The research analysis of the existing journal articles that are peer-reviewed Q1 level of articles is selected to perform the analysis.The main taxonomy that is proposed in this project is being data, auditing, monitoring, and output i.e., DAMO taxonomy that is used and includes these components. The data component would include the type of data; the auditing would ensure the algorithm that would be used at the backend and the storage would include the type of database as single or the distributed server in which the data would be stored.As a result of this research, it would help understand how the data can be ensured to have the required level of privacy and security when the third-party database vendors would be used by the organizations to maintain their data. Since most of the organizations are looking to reduce their burden of the local level of data storage and to reduce the maintenance by the outsourcing of the cloud there are still many issues that occur when there comes the time to check if the data is accurate or not and to see if the data is stored with resilience. In such a case, there is a need to use the Remote Data Auditing techniques that are quite helpful to ensure that the data which is outsourced is reliable and maintained with integrity when the information is stored in the single or the distributed servers.
Embedding Lexicon and direction information in Chinese NER

At present, the field of Chinese named entity recognition mostly introduces external lexicon information to improve the effect of the model. In previous articles, authors mostly use general-purpose lexicon, and their method cannot distinguish the importance of different lexicon information according to the training corpus. In this paper, we propose the approach of dynamic weighting according to the frequency of related words in the corpus when quoting external lexicon. According to recent researches, the long short term memory(LSTM) usually performs better than Transformer in NER task, but it is not suitable for parallel computing. An improved Transformer encoder with a relative position embedding that can better perceive the direction of input sequence is employed in this paper. Experiments demonstrated that the proposed model has better FI measurements than LSTM based models in most instances and significantly saves the training time.
Performance Prediction Model Based on K-Means Clustering Algorithm

With the development of the times, more and more fields will gradually realize the objectivity and convenience of using data to describe the characteristics of characters. The essence of the key user profile of accurate services is data mining and natural language processing, of which the more widely used Including short text classification processing method, keyword extraction method, fusion pruning algorithm, TF-IDF algorithm and other methods. How to solve the problem of different data standards, complete ability and job matching, maximize employee value, achieve objective evaluation of employee performance, comprehensively record employee work behavior and abilities, scientifically improve company A’s employee profile model, and use big data technology to manage human resources Empowerment is the core and key of A company's talent training and value drive. Through a large number of literature readings and experimental investigations, this paper gives a detailed overview of the performance model research under the flexible organizational structure of A company, the task-driven OKR of A company, and the establishment of the AI-based company A employee in-depth portrait platform. And use the K-Means clustering algorithm to establish a performance prediction model, and carry out experimental simulation and performance analysis. The K-means clustering algorithm is applied to the flow chart and experimental simulation and system performance test of the group connection point aggregation formation control. The experimental results show that the K-means clustering algorithm in the model process is applied to the flow chart of the cluster connection point aggregation formation control with accurate parameter data, which greatly improves the efficiency of human resource management.