Reviewerly/exHarmony huggingface dataset:
Protection of Data in Cloud based on Seed based Algorithm and Encrypted Access Control

Data Storage security is one of the highest concerns for any organisation. Even if the organisation maintains its own infrastructure or hires cloud storage to store its data, privacy of the data and its protection in any mode is always the highest priority for any organisation. The cloud provides with a huge amount of storage space and also with various resources to the different organisations as per their requirements. In this paper, four different techniques that are proposed for data protection and recovery of the data in case of loss due to any eventuality or failure of the functioning of cloud are reviewed. The first technique to be studied is the Seed based algorithm that is used for recovery of data which is remotely stored at any geographical location in a cloud. The technique can restore the data which even if unfortunately is lost can be still recovered. The second technique reviewed is the attribute based access control algorithm which is used to secure the data thereby maintaining the safety and sanctity of the stored data in the cloud. Another technique which is quite novel is a hybrid technique of using MRADO data obfuscation method along with LSB. This provides with data masking wherein the original data remains totally camouflaged under the image that get obfuscated without the attacker coming to know about its existence. Another data protection model mentioned in the paper is the model that is based on an encryption attribute access control. This model proposes a data detection algorithm which detects the access record of the data. Post analysis of the four different algorithms, the use of two of the techniques is proposed for the protection of the data in cloud.
CAC: A Learning Context Recognition Model Based on AI for Handwritten Mathematical Symbols in e-Learning Systems

The e-learning environment should support the handwriting of mathematical expressions and accurately recognize inputted handwritten mathematical expressions. To this end, expression-related information should be fully utilized in e-learning environments. However, pre-existing handwritten mathematical expression recognition models mainly utilize the shape of handwritten mathematical symbols, thus limiting the models from improving the recognition accuracy of a vaguely represented symbol. Therefore, in this paper, a context-aided correction (CAC) model is proposed that adjusts an output of handwritten mathematical symbol (HMS) recognition by additionally utilizing information related to the HMS in an e-learning system. The CAC model collects learning contextual data associated with the HMS and converts them into learning contextual information. Next, contextual information is recognized through artificial intelligence to adjust the recognition output of the HMS. Finally, the CAC model is trained and tested using a dataset similar to that of a real learning situation. The experiment results show that the recognition accuracy of handwritten mathematical symbols is improved when using the CAC model.
A Gradient-Descent Calibration Method to Mitigate Process Variations in Analog Synapse Arrays

A technique to compensate the effects of process variation in analog-based neural computation circuit arrays is presented. The proposed technique considers the process variation that degrades the accuracy of a neural network as a variable for optimization and adjusts the circuit parameters via a gradient descent method. In the TensorFlow framework, the effects of process variation on the neural network are modeled and the improvement in the inference accuracy achieved by the proposed technique is analyzed. For a 4-layer CNN MNIST example driven for 1000 different variations sets, the technique restores the accuracy to the original level, which can degrade to 17.3~95.2% due to process variations.
Distributed Cross‐Domain Link Creation for Flexible Data Integration and Manageable Data Interoperability Standards

ABSTRACT A distributed link creation strategy represents a shift from the traditional centralized link creation strategy used for data integration. A distributed link creation strategy provides a more flexible, user‐friendly approach for engineers to specify cross‐domain links between different domains of engineering data, which is key to achieving traceability across different product lifecycle phases. A new type of API that allows API clients to find link targets easily and create links makes this possible. Features of this new link‐enabling API include support for the open‐world assumption, embeddable search dialogs, and hypermedia for self‐discovery. Furthermore, a distributed link creation strategy also supports more efficient standardization efforts to achieve data interoperability by only requiring small, thus more manageable, domain‐specific standards in combination with standardized cross‐domain links, in contrast to the typically large, therefore unmanageable, multi‐domain standards.
Textual variations affect human judgements of sentiment values

• Text variations like letter repetition, letter capitalization, emoticon, different punctuations are found to have additional sentiment value compare to the plain text. • People tend to use text variations to express their emotions within text. • Text variations can play a significant role in affecting the interpretation of text reviews. • Current sentiment tools should take into consideration different text variations. Electronic word-of-mouth communication in the form of online reviews influences people’s product or service choices. People use text features to add or emphasise feelings and emotions in their text. The text emphasis can come in as capital letters, letter repetition, exclamation marks and emoticons. The existing literature has not paid sufficient attention to the effects of such textual variations on human text interpretation. This paper presents an analysis of text variations that can affect the interpretation of a text. A total of 1,041 online comments were collected, in which seven types of the most used textual variations were identified and simulated for hypothesis testing. Sentiment scores from 500 participants were collected to rate the value expressed for each of the textual variations. Statistical analysis showed that collected ratings are significant for the accurate calculation of sentiment values for short comments. Furthermore, the performance of ten existing sentiment tools was analysed based on seven textual variations. Results indicate that those tools should consider these textual variations to fully reflect a human interpretation on the text variations.
A note on factor normalization for deep neural network models

Deep neural network (DNN) models often involve high-dimensional features. In most cases, these high-dimensional features can be decomposed into two parts: a low-dimensional factor and residual features with much-reduced variability and inter-feature correlation. This decomposition has several interesting theoretical implications for DNN training. Based on these implications, we develop a novel factor normalization method for better performance. The proposed method leads to a new deep learning model with two important characteristics. First, it allows factor-related feature extraction, and second, it allows for adaptive learning rates for factors and residuals. These model features improve the convergence speed on both training and testing datasets. Multiple empirical experiments are presented to demonstrate the model's superior performance.
An Argentine ant system algorithm for partial set covering problem

Purpose The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and well-known non-deterministic polynomial (NP)-hard problem whose goal is to cover at least k elements with as few subsets as possible. Design/methodology/approach In this work, the authors present a novel variant of the ant colony optimization (ACO) algorithm, called Argentine ant system (AAS), to deal with the PSCP. The developed AAS is an integrated system of different populations that use the same pheromone to communicate. Moreover, an effective local search framework with the relaxed configuration checking (RCC) and the volatilization-fixed weight mechanism is proposed to improve the exploitation of the algorithm. Findings A detailed experimental evaluation of 75 instances reveals that the proposed algorithm outperforms the competitors in terms of the quality of the optimal solutions. Also, the performance of AAS gradually improves with the growing instance size, which shows the potential in handling complex practical scenarios. Finally, the designed components of AAS are experimentally proved to be beneficial to the whole framework. Finally, the key components in AAS have been demonstrated. Originality/value At present, there is no heuristic method to solve this problem. The authors present the first implementation of heuristic algorithm for solving PSCP and provide competitive solutions.
A Novel Framework For Multi Source Based Cloud Forensic

With the advent of Cloud Computing, not only ethical users but also the malicious users get opportunities to use various technological advancements. This creates more challenges to LEA in tracking security breach. Also, the traditional forensic approach which is being followed in cloud forensic investigation lacks various well-developed tools and framework for effective investigations. This paper analyses various commonly used cloud forensic frameworks and traditional techniques and observes the limitation of having single evidence collection method . In this research a new Cloud Forensic Framework which emphasizes on multiple evidence collection methods and multiple forensic analysis distribution methods depending on various sources and techniques is presented. This provides scope for multi tenancy and checks data integrity also.
Correlation Between Attention Heads of BERT

Recently, as deep learning achieves tremendous success in a variety of application domains, natural language processing adopting deep learning also has become very widespread in research. The performance of typical such models like Transformer, BERT and GPT models is quite excellent and near human performance. However, due to its complicate structure of operations such as self-attention, the role of internal outputs between layers or the relationship between latent vectors has been seldomly studied compared to CNNs. In this work, we calculate the correlation between the output of multiple self-attention heads in each layer of a pre-trained BERT model and investigate if there exist redundantly trained ones, that is, we test if the output latent vectors of an attention head can be linearly transformed to those of the other head. By experiments, we show that there are heads with high correlation and the result implies that such examination on the correlation between heads may help us to optimize the structure of BERT.
Database Patterns for the Cloud and Docker Integrated Environment using Open Source Machine Learning

Computer vision, natural language processing, sentiment analysis, and fraud detection are just a few of the sectors, where significant area of research are Machine and predictive analysis. Machine learning can be used to train or set up software and application of H/W for mining specially and the acquisition data. In order to extract accuracy and performance among datasets utilizes unsupervised and supervised data, MLDB for high-performance data analytics for a variety of applications is presented in the work as usage patterns of an open source machine learning platform.
DCT Based Texture Region Classification for Image Denoising

Image denoising has long been studied in literature. However, so many conventional techniques may excessively smooth texture regions during the denoising process and the original texture information is lost. This could be resolved by adjusting the strength of denoising if texture regions of a noise image were effectively classified. In this paper, we propose a new texture classification method to exploit frequency characteristics on DCT domain where texture can be easily separated from non-texture.
Fine-Grained Image Classification based on Self-attention Feature Fusion and Graph-Propagation

Abstract The goal of FGVC is to distinguish different subclasses under the same common class. Because data sets often have large intra-class differences and large inter-class similarity, FGVC is more challenging than traditional image classification. Previous work focused on mining salient regions in images and directly utilizing their features, but ignored that the features of regions are semantically related and the regional groups have stronger differences. In order to solve the above problem, we propose a FGVC method based on self-attention feature fusion and graph-propagation, consists of two branches, one of the branch based on the characteristics of map mining granularity characteristics of image, another branch study the inner semantic relevance between the regional characteristic vecto named CFSM, by iteration to enhance information elements and suppress useless element to improve the ability to recognize. A large number of experiments have proved the effectiveness of the proposed network, which has good accuracy and efficiency on the three benchmark data sets of Stanford Cars, FGVC-Aircraft and CUB200.
An abnormal user login behavior detection method of industrial control system based on multi-dimensional probability analysis

Abstract Aiming at the poor detection performance of user abnormal login behavior in the complex environment of industrial control system, a new method based on multi-dimensional probability analysis is proposed. First of all, login time, source, destination and other login parameters are collected. Secondly, the abnormal probability of login parameters is calculated. Thirdly, the login transfer probability among multiple destination hosts is calculated. Finally, through the comprehensive analysis of the above three probabilities, an abnormal login behavior detection model based on multi-dimensional probability analysis is constructed. Experiments show that this method can effectively identify the abnormal login behavior of users caused by various types of network attacks.
Coded Pattern Optimization: Image Model-based Theoretical Analysis

Coded exposure photography (CEP) modulates a point spread function (PSF) causing motion blur to enhance the motion deblurring performance. The performance of motion deblurring using CEP depends on the coded pattern which modulates the PSF, thus, the optimality of the coded pattern is the key to successful deblurring. Since the intensity of the captured image has a non-linear relation with the irradiance of the scene, the CEP without considering the non-linear relationship in deblurring is very likely to have poor deblurring results. In this paper, we investigate how to optimize the pattern by considering the nonlinear characteristics of image capturing.
Location-Visiting Characteristics Based Privacy Protection of Sensitive Relationships

In the era of Internet of Things (IoT), the problem of the privacy leakage of sensitive relationships is critical. This problem is caused by the spatial–temporal correlation between users in location-based social networks (LBSNs). To solve this problem, a sensitive relationship-protection algorithm based on location-visiting characteristics is proposed in this paper. Firstly, a new model based on location-visiting characteristics is proposed for calculating the similarity between users, which evaluates check-in features of users and locations. In order to avoid an adversary inferring sensitive relationship privacy and to ensure the utility of data, our proposed algorithm adopts a heuristic rule to evaluate the impact of deduction contributions and information loss caused by data modifications. In addition, location-search technology is proposed to improve the algorithm’s execution efficiency. The experimental results show that our proposed algorithm can effectively protect the privacy of sensitive data.
Detecting Trivariate Associations in High-Dimensional Datasets

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.
Retracted: English-Chinese Corpus Collection and Machine Intelligent Translation Methods based on Big Data and Pattern Analysis Model

First, it analyzes the development trend of artificial intelligence in the machine translation industry, and explains the specific application methods; second, based on the national conditions, explore the development advantages of China in the artificial intelligence and machine translation industry, and automatically align the results with the artificial Comparison of manual alignment results, thinking about the problems encountered in the process of building the database, including the selection of sentence identification, the alignment and matching of many-to-many types of sentences, and on this basis, this paper proposes a design framework for machine translation systems. Including the design method of the electronic dictionary, the storage structure of the electronic dictionary.
Cultural Algorithms as a Framework for the Design of Trustable Evolutionary Algorithms

One of the major challenges facing Artificial Intelligence in the future is the design of trustworthy algorithms. The development of trustworthy algorithms will be a key challenge in Artificial Intelligence for years to come. Cultural Algorithms (CAs) are viewed as one framework that can be employed to produce a trustable evolutionary algorithm. They contain features to support both sustainable and explainable computation that satisfy requirements for trustworthy algorithms proposed by Cox [Nine experts on the single biggest obstacle facing AI and algorithms in the next five years, Emerging Tech Brew, January 22, 2021]. Here, two different configurations of CAs are described and compared in terms of their ability to support sustainable solutions over the complete range of dynamic environments, from static to linear to nonlinear and finally chaotic. The Wisdom of the Crowds method was selected for the one configuration since it has been observed to work in both simple and complex environments and requires little long-term memory. The Common Value Auction (CVA) configuration was selected to represent those mechanisms that were more data centric and required more long-term memory content. Both approaches were found to provide sustainable performance across all the dynamic environments tested from static to chaotic. Based upon the information collected in the Belief Space, they produced this behavior in different ways. First, the topologies that they employed differed in terms of the “in degree” for different complexities. The CVA approach tended to favor reduced “indegree/outdegree”, while the WM exhibited a higher indegree/outdegree in the best topology for a given environment. These differences reflected the fact the CVA had more information available for the agents about the network in the Belief Space, whereas the agents in the WM had access to less available knowledge. It therefore needed to spread the knowledge that it currently had more widely throughout the population.
Investigating the optimisation of real-world and synthetic object detection training datasets through the consideration of environmental and simulation factors

Computer vision is used for many industrial applications involving automation, especially those related to efficiency and safety. Computer vision techniques which use machine learning, such as object detectors, need a dataset of images for training and testing. Publicly available datasets or new datasets can be used. However, these datasets rarely consider whether the dataset is leading to optimal performance. Environmental factors, such as lighting and occlusion, will alter the appearance of the images and so images taken under certain condition may have different effects on training. A knowledge gap has formed as to how the test performance of deep neural networks can be improved by considering the effect and interactions of factors where either real or synthetic images are used. The following research illustrates that the different factors can have a significant impact on the test performance and demonstrates a process that can be used on real-world and synthetic images to identify the effect of each factor and discusses how this information may be used to create an optimal training dataset.
A Hierarchical Visualization Fusion Method for Multi-information of Unconventional Mutation Medical Big Data

Abstract In the process of information fusion for medical data, there are some problems, such as low precision of data cleaning and more omission of duplicate data, which lead to the unsatisfactory effect of information fusion. This paper proposes a data collection strategy of a distributed network based on linear regression analysis, constructs a perceptual unconventional mutation data model by applying linear regression analysis method, and maintains the characteristics of perceptual data to realize data collection. The ID tags taken as data Transducer Identification of Strain gage are compared. Assuming that the TIDs of two data points are the same, that is, duplicate data are detected, the detected data set flows according to the method flow. To enhance the accuracy of data cleaning and avoid omission, the hierarchical visualization fusion method of radar maps is used to achieve multi-information fusion. It was found that the proposed method can clean the redundant data accurately and realize its efficient fusion.