Reviewerly/exHarmony huggingface dataset:
A cross encryption scheme for data security storage in cloud computing environment

Cloud computing is one of the popular technologies in the development of information technology. Cloud computing not only provides users with high-performance computing, but also meets the needs of large-scale data storage. However, because the storage service provided by cloud computing is completely transparent to users, users cannot understand whether their data is safe in the cloud computing environment. The resulting distrust has brought great obstacles to the development of cloud computing. Therefore, this paper first describes the basic knowledge and system architecture of cloud storage, and analyses the development status of cloud storage. Secondly, in order to ensure the storage security of user data in the cloud computing environment, this paper studies the data encryption algorithm, and proposes a cross encryption scheme of data security storage in the cloud computing environment. Finally, the scheme is compared with the traditional hybrid encryption method. The experimental results show that the scheme has the advantages of good encryption and decryption effect, fast execution speed and high security. It is an ideal scheme for data security storage in cloud computing environment.
AI, the Overall Picture

Nowadays, artificial intelligence (AI) algorithms are being designed, exploited and integrated into a wide variety of software or systems for different and heterogenous application domains. AI is definitively and progressively emerging as transversal and powerful technological paradigm, due to its ability not only to deal with big data and information, but especially because it produces, manages and exploits knowledge. Researchers and scientists are starting to explore, from several perspectives, the different and synergetic ways AI will transform heterogenous business models and every segment of all industries.
CRACLE: Customer Resource Allocation in CLoud Environment

As well known, cloud computing follows dual pricing approach. It follows free-to-use as well as pay-per-use model approach, in which a user pays for the quantum of the resources they consume over a period of time. Depending on the demand, a customer requests for the resources and the Cloud Service Provider (CSP) allocates the resources matching to the customer’s requirements. In the past, in the simulation conducted using CloudSim software, researchers have usually worked with the pre-loaded and default values of variables for the resource allocation without considering the end-user requirement. In the current research work, a CRACLE: Customer Resource Allocation in CLoud Environment has been proposed that prompt customers for resource request and subsequent framework allocation of the same is done by the CSP.
Colorizing Black and White Images Using Deep ConvNets and GANs

In this paper, a technique to colorize black-and-white images combining both localized and global features has been presented. The technique is based on Deep Convolutional Neural Networks and Generative Adversarial Networks (GANs), which merge localized scene information derived from small area with the global scene information of sample image. The whole framework, including the colorization model, is trained in an E2E manner, i.e., end-to-end, because of its ability to perform well irrespective of the knowledge of the problem. Unlike other convolution-based approaches, the model is able to process images of varied resolutions. To train the model, an existing large-scale scene classification dataset has been used. The class labels about the types and scenes represented in various images have been utilized for efficient training. In this work, the adversarial networks on top of deep convolutional nets have been used as a generalized approach toward I2I translation. What makes these networks generic is the way their algorithm learns loss function mapping along with mapping from input to output layers, which otherwise would have required separate approaches. At last, we have compared the proposed model with some existing models used for grayscale image colorization. The proposed model demonstrated promising result on many different types of black-and-white images and delivered realistic colorization, even on random images taken from the Internet.
A Collaborative Destination Recommender Model in Dravidian Language by Social Media Analysis

Data generation in social media has seamlessly gone beyond imagination nowadays. It could be in varieties of types like images, videos, audio, multimedia formats, and text. The reports or text can also be in different languages. Language processing has been emerged as one of the hottest research area with advancements of artificial intelligence. Social media is considered as the largest data repository and growing time to time. This paper focuses on information retrieval methods from Facebook.com in travel and tourism domain which are written in Malayalam language, one of the prominent Dravidian languages used in the southern part of Indian state Kerala. The second topic discusses on developing an algorithm to recommend the suitable locations for individuals by fetching their travel histories, personal choices, and preferences using unsupervised machine learning techniques. A customized dataset has been generated from the largest Malayalam Facebook group in travel domain named “Sanchari” whose URL is www.facebook.com/groups/teamsanchari . Algorithm can suggest a set of suitable destinations for each user with an accuracy of 90% with help of collaborative filtering.
Performance Evaluation of Contextualized Arabic Embeddings: The Arabic Sentiment Analysis Task

Sentiment analysis is a growing topic of study that straddles several disciplines, consisting of machine learning, natural language processing, and data mining. Its goal is to automate the extraction of conveyed concepts from a text. Much research has been undertaken in this field, particularly on English texts, because of their wide uses, whereas other languages, like Arabic, have gotten far less attention. The objective is to implement a multi-classification sentiment analyzer for Arabic text while using four recently published contextualized embedding models. The proposed work is an intrinsic evaluation metric of the four models. The results of the experiments revealed that MARBERT and ARABERT models are found to be the best.
Analysis on Exposition of Speech Type Video Using SSD and CNN Techniques for Face Detection

The Internet and multimedia have been advanced vision tools, which are sources of videos and images. Storing, organising and indexing are a process, in which huge number of memories has been operated. As a consequence, a CNN method is suggested for extracting useful data from the original video with a mixed number of emotions. The primary goal of video exposition is to use deep learning methods to provide a substantial abstract vision with emotions from a full film in a short period of time. This technology is very helpful in all platforms including google applications. The earlier models are cannot handle this emotion-tracking feature from videos. In this investigation, convolutional neural network (CNN)-based deep learning mechanism has been proposed for emotion detection on running video extraction. The performance measures like accuracy 98.34%, sensitivity 97.23%, recall 98.23% and throughput 97.23% have attained these are outperformance methodology and compete with current technologies.
CAPTCHA-Based Image Steganography to Achieve User Authentication

In recent days, steganography is the major promising data hiding technique. Data can be hidden by different techniques. Similarly, attackers also develop many tools to break the technique and retrieve the data. In recent trends, CAPTCHA is introduced to ensure that the receiver is a human being and not a robot. To authenticate the user and to increase the confidentiality, a randomized CAPTCHA code is created. Steganography is the method and to hide the secret data from an unauthorized access. The steganography method tends to mute the visibility of the secret data into a cover file of different multimedia type. Passing of a secret message over a medium in modern days, the newly invented steganography techniques are used to embed the data into the original cover image. To achieve secret and reliable data communication, the encryption and decryption mechanism is used. A predefined procedure cannot be detectable by the system. After establishing a secure connection between an authenticate sender and receiver, the original secret message is transmitted using image steganography algorithm. Our proposed algorithm gives the better PSNR ratio (~1.04%) compared with the existing method and also minimizes the MSE.
Privacy-Preserving Truth Discovery with Truth Transparency

This chapter will introduce how to identify the data submitted by sensing users in the data processing phase and calculate the truth with privacy-preserving. For the same task, the data submitted by sensing users often varies widely, and user concerns about the privacy of their data may affect the use of truth discovery. Existing truth discovery schemes for privacy-preserving either have large computational and communication costs or cannot provide comprehensive protection for user data and weight. Therefore, based on a homomorphic encryption algorithm and two-server model, this chapter proposes a truth discovery scheme RPTD for privacy protection. Specifically, the chapter first proposes a privacy-preserving truth discovery scheme RPTD-I with user participation in the truth discovery iteration process, which can effectively preserve the privacy of user’s data and weights and achieve efficient and secure truth updates and weight updates. Subsequently, this chapter proposes a privacy-preserving truth discovery scheme RPTD-II without user participation in the truth discovery iteration process, which transfers the user’s computation in the truth discovery iteration process to a cloud server, greatly reducing the user’s computational and communication overhead.
Estimating Human Running Indoor Based on the Speed of Human Detection by Using OpenPose

The recognition of human activities had received much attention because of its broad range of applications. Academics have recently studied it in pattern recognition, for example, in the intelligent elderly care area, intelligent monitoring systems to detect probable aberrant occurrences, tracking pedestrian mobility. This article proposes a new approach to classifying human activities in a video by capturing skeleton behavior in video or camera. In this respect, three activities are monitored: running, walking, and stopping. This method to recognize the type of walking depends on determining the human speed. The human speed is determined by calculating the distance moved over time based on a mathematical model. It will aid in determining if a human in a video or camera is walking or running. The main idea of estimating speed of human is to determine the distance between the human detected in video or camera, using the skeleton data from the OpenPose technique. Experiments were conducted on different experimental video sequences to prove the efficiency in detection and classification. The results showed walking 97%, running 98%, and stopping 95%.
Lessons Learned from Developing an Industry 4.0 Mobile Process Management System Supported by Artificial Intelligence

Research, development and innovation (RDI) projects are undertaken in order to improve existing, or develop new, more efficient products and services.Moreover, the goal of innovation is to produce new knowledge through research, and disseminating it through education and training.In this line of thinking, this paper reports and discusses the lessons learned from the undertaken project, regarding three areas: machine learning (artificial intelligence), computational intelligence, and database management systems (DBMS).As nowadays, a numerous of the RDI projects are oriented towards the development of dataintensive solutions, the authors are confident that these lessons will be valuable not only for data engineers, but also for those researchers and practitioners who are dealing with the issues related to building and validating machine learning models, applications of moving averages to high-frequency data streams, and the implementation and deployment of DBMS.
Comparison and Assessment of Intelligent and Geostatistical Models for Analysis of Spatial Variations of Groundwater Quality (Komijan Plain)

Comparison and Assessment of Intelligent and Geostatistical Models for Analysis of Spatial Variations of Groundwater Quality (Komijan Plain)
<i>CompCube:</i> A Space-Time-Request Resource Trading Framework for Edge-Cloud Service Market

As the footing stone of artificial intelligence (AI), ubiquitous computing resource is beginning to receive interest. With this trend, a new form of edge-cloud service market dedicated to collecting, trading, and scheduling computing resources is rising. The computing participants in the service market, as providers and demanders of computing resources, are becoming more diversified and open. As such, the intriguing economic phenomenon and the circulation mechanism have emerged. These bring inherent challenges, such as a volatile market, the ossification of pricing, isolation, and inefficiency. In this article, we propose a novel space-time-request trading framework for the edge-cloud service market, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CompCube</i> . To ensure stability, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CompCube</i> adopts the dual-circulation futures-spot trading method, including space-time dynamic pricing in the macro-cycle, request intention conversion, and resource matching in the micro-cycle. Among this, an incomplete information game model is designed to determine the long-term trading pricing in the macro-cycle. Then, to tackle the indicator isolation problem due to the inconsistency between the user's requests and the computing-power provider's (CPP’s) resources, we focus on minimizing the rental cost of computing resources while satisfying diverse service level agreements (SLA) of users. To address this problem, a spatiotemporal scale Lyapunov optimization and an alternating actor-critic algorithm, A2SC, are developed. Besides, in the micro-cycle, a discriminatory double auction helps to determine the computing resource matching results efficiently and impersonally. We evaluate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CompCube</i> of the A2SC algorithm with realistic datasets. Compared to other baselines, i.e., DYRECEIVE, Price Preferred, and Random, A2SC reduces the average rental cost by 30.45%, 5.74%, and 17.57%, respectively. Furthermore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CompCube</i> can improve SLA satisfaction, as well as promote resource efficiency and social welfare compared with the traditional methods.
Deep Robust Neural Networks Inspired by Human Cognitive Bias Against Transfer-based Attacks

In recent years, with the proliferation of cloud services, the threat of Transfer-based attacks, a type of Adversarial attacks, has increased. Adversarial Training is known as an effective defense against this attack, but it has been pointed out that it degrades accuracy against normal data and robustness against random noise(Gaussian noise). To solve these problems, we focus on the human visual function, which has robustness while maintaining high accuracy. The contribution of top-down processing, in which the feedforward signal is overwritten by some bias factor, has been pointed out as the reason for this. From this perspective, we propose a new model based on Neural Networks using human cognitive bias. This is an algorithm that overwrites signals according to human cognitive bias and is expected to reproduce human visual functions. Evaluation experiments on two different datasets suggest that the proposed model is robust against Transfer-based attacks. Furthermore, the proposed model can mitigate the accuracy degradation of the normal data to a limited extent, suggesting that it is robust against random noise.
Proposal and Evaluation of a Course-Classification-Support System Emphasizing Communication with the Sub-committees Within the Committee of Validation and Examination for Degrees

The National Institution for Academic Degrees and Quality Enhancement of Higher Education (NIAD-QE) awards academic degrees based on the accumulation of credits. These credits must be classified according to pre-determined criteria for the chosen disciplinary field. This work has been carried out by the sub-committees within the Committee of Validation and Examination for Degrees (CVED), whose members should be well-versed in the syllabus of each course to ensure appropriate classification. The number of applicants is increasing every year, and thus, a course classification system supported by information technology is strongly desired. We have proposed the Course Classification Support system (CCS) and the Active Course Classification Support system (ACCS) for the awarding of degrees in NIAD-QE. On the other hand, in this paper, from the standpoint of emphasizing communication with the sub-committees, we construct a course classification support system using deep learning, which has been developing remarkably in recent years. We also confirm the effectiveness of the proposed method using actual syllabi from two universities.
A Study on Effectiveness of BERT Models and Task-Conditioned Reasoning Strategy for Medical Visual Question Answering

Medical visual question answering task requires a framework to understand a medical question in natural language and examine the corresponding image to produce the answer to the question. The common framework consists of a language understanding module, a visual understanding module, a signal fusion module, and an answer prediction module. Most existing works employed recurrent neural network-based models for the language understanding module. However, these approaches may not produce robust text presentations and are hard to interpret. On the other hand, BERT models are more robust for text representation and can provide a clue for interpretability via the attention weights between the words. Besides, as the questions consist of closed-answer questions and open-answer questions, the task-conditioned reasoning strategy was proposed to handle each type of question separately while maintaining several modules in the framework to be shared. In this paper, we investigate the effectiveness of pre-trained BERT models and the task-conditioned reasoning strategy for the task of medical visual question answering on the VQA-RAD dataset. Experimental results demonstrate improvements when pre-trained BERT models are combined with the task-conditioned reasoning strategy.
Enhance the Hidden Structure of Deep Neural Networks by Double Laplacian Regularization

The Laplacian regularization has been widely used in neural networks due to its ability to improve generalization performance, which enforces adjacent samples with the same labels to share similar features. However, most existing methods only consider the global structure of the data with the same labels, but neglect samples in boundary areas with different labels. To address this limitation and improve performance, this brief proposes a novel regularization method that enhances the hidden structure of deep neural networks. Our proposed method imposes a double Laplacian regularization on the objective function and leverages full data information to capture its hidden structure in the manifold space. The double Laplacian regularization applies both attraction and repulsion effects on the hidden layer, which encourages the hidden features of instances with the same label to be closer, and forces those of different categories to be further away. Extensive experiments demonstrate the proposed method leads to significant improvements in accuracy on different types of deep neural networks.
Automatic Extraction of Design Rationale from Voice Sounds

The Japanese manufacturing industry faces various problems, one of which is the need to solve the problem of collaboration regarding information sharing with other departments such as production engineering, manufacturing, and procurement. In this study, the target of information extraction is the idea stage in the design process. All conversations including the design rationale, such as the reasons for the decision on the structure and function of each part and the process of consideration leading up to it, are recorded in the design process, and the data is automatically transcribed using the Google Speech API. The document data is analyzed in terms of TFIDF, which is text mining technique to detect important words from a text. We proposed an automatic method for extracting design rationale and important information by extracting sentences containing the extracted words from the document data in the order of their TFIDF values, and then extracting the logical and important information sentences. The proposed method was applied to the design of scissors. The case shows the potential for extraction of rationale while there are several points to be improved.
Edge Preserved Low-Rank SAR Image Despeckling via Hierarchical Prior Knowledge Regulation

Synthetic aperture radar (SAR) image despeckling is a challenging task as speckle noise is spatially correlated and signal-dependent, and appears as a grainy texture superimposed on images. Although traditional low-rank SAR image despeckling methods have shown promising performance, they have the problem of producing over-smoothed images with blurred edges due to their low-rank characteristics. In this paper, we propose a novel edge preserved SAR despeckling method named EP-LRSID, which can keep rich edge details while reducing speckle noise. Specifically, EP-LRSID takes a fresh look at the low-rank model, i.e., we can obtain structural edge information from residuals which is viewed as noise and simply disregarded by the traditional low-rank methods. To obtain discriminative edge information from residuals, the edge subspace is obtained in a manifold framework by using a dynamic affinity graph regularization. Moreover, a new hierarchical prior knowledge regulation is designed to make different kinds of pixels processed hierarchically, especially the strong scattering points in SAR images. By introducing this prior knowledge, our low-rank model can obtain more confidential low-rank parts and edge parts, thus structural information including edges can be better preserved in this way. Extensive experiments on several real and synthetic datasets demonstrate that EP-LRSID can achieve the highest despeckling performance with edge preservation than other state-of-the-art despeckling algorithms.
A comparative study of lexical similarity approaches for Malay short text

The short text retrieval is gaining tremendous attention due to rapid exposure and widespread usage of social networks. This type of text is widely used in various applications such as spam filtering, tweet classification, question-answer systems, web search and so forth. Thus, the effective analysis of these texts is of vital need. But, short text retrieval often becomes complex because they are usually contains repeated words, less features and overloaded with noise. Different lexical similarity approaches have been developed to tackle the problem of lexical analysis. However, most of them are in English, and the solutions for problems involving Malay are of limited availability. This paper examined the reliability of lexical similarity approaches focusing on term-based approaches for Malay short text analysis. The Cosine, Overlap coefficient and Jaccard similarity methods were used to quantify the similarities of some news title written in Malay news. Results show that the selected approaches term-based has potential to be used to analyze Malay short texts.