Reviewerly/exHarmony huggingface dataset:
GDPR-Compliant Data Management Protocol: A Scalable Solution

Personal data protection has become a key concern in the European Union (EU) as well as around the world. The General Data Protection Regulation (GDPR) restores data owners' authority over their personal information, which had previously been controlled and processed by service providers utilising new technology. However, the service provider's action cannot be guaranteed to be devoid of malicious intent, and hence it is not fully compliant with GDPR. Furthermore, the Data Owner (DO) has no way of knowing whether or not a service provider is GDPR compliant. In the existing work, DO has to store and manage individual keys for each piece of data. As a result, DO would undoubtedly suffer from a scalability problem. It would be extremely difficult for a small device to manage a high number of keys for a similar quantity of data if the data owner deployed a smaller device to act as DO. Our work aims to develop a unidirectional proxy re-encryption protocol in which multiple private keys for distinct encrypted data are re-encrypted using only one public key. As a result, the DO doesn't have to deal with a big number of keys.
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Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 58, Issue 4 p. I-I Full Issue Full Issue First published: 12 June 2021 https://doi.org/10.1111/j.1467-6346.2021.09997.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume58, Issue4June 2021Pages I-I RelatedInformation
COVID-19 pandemic: Virtual technology applied to higher education at CU UAEM Valle de México and Ecatepec

One of the most important challenges in the education system are the technological advances in the educational models at a higher education, achieving a transformation of the student's thinking, in an approach of analysis and innovative construction based on interdisciplinary processes and creativity. The Autonomous University of the State of Mexico is aware that its teachers must have a comprehensive training in communication and creativity with a social and humanistic commitment with transcendence of technological research. Due to COVID-19, pandemic caused by an infectious virus that produced a total closure of educational facilities, in which the only teaching alternative is online, this technology has been integrated by necessity to the educational system with a different pedagogical approach, which has been questioned by the type of knowledge acquired by students. Finally, this research awareness among the teachers of Valle de México and Ecatepec that they must face the challenges in the use of virtual technology.
Understanding Facial Expression Expressing Hate from Online Short-form Videos

The impact of hate speech is not only detrimental to an individual's human rights; but also, a grave threat to social stability and democracy. Through social media, the spread of hate speech has alarmingly increased across the globe. Various social media platform's goal is to eliminate hateful content and this challenge poses the need for automatic and accurate hate speech detection. Presently, known techniques in this research primarily made use of either text or audio features. However, the use of the facial expression in hate speech detection is not that explored. Thus, for this study, the use of facial expressions to understand hate speech has been thoroughly investigated. The dataset used is image data generated from Filipino Tiktok videos with a frame size of 1080 x 1920 pixels and divided into 5 frames per second. Two approaches namely conventional and deep learning-based frameworks have been implemented in building the Facial Expression Recognition (FER) model to understand hate speech. Based on the experimentation, the conventional approach using the Random Forest approach has achieved the best performance with 86.9% training accuracy and 84.8% validation accuracy, outperforming the other conventional classifiers and the DL-based approach significantly. For future direction, facial expression features combined with text or audio input type will be implemented to examine whether the use of facial expression can complement or improve hate speech detection models.
Authenticate IoT Data for Health Care Applications Using ATSHA204 and Raspberry Pi

Internet of Things (IoT) has made life easier by connecting devices on one platform and communicates with other devices. However, the threat of malicious activities has also increased. The attackers intrude over the cloud malign the data or hack the device that proves a huge loss for the organization. Therefore, reliable systems are required to authenticate and secure the data in IoT. The paper presents a robust method to authenticate the data send from medical monitoring device. The Microchip ATSHA204 crypto IC is used to authenticate the data from its digital signature which locks the.csv data file generated by Keysight InfiniiVision MSO4024A Mixed Signal Oscilloscope. Raspberry pi 3B+ used as a controller in the technique. The coding is done in Python language.
Specified Ontologies in User Tags to Graphical User Interface Hedonistic Type Based on Sentiment Analysis

In this research, some promising initial results are described from induced ontologies labels related to a Hedonistic Graphical User Interface with vocabularies. These results were selected using Cultural Algorithms based on desire intentions using some model assumptions. The usefulness of ontology aspects as a supplement to a labeling system that brand and model results and also demonstrates clearly its usability specification. Proposing a revised probabilistic model using ontologies to induce seed ontology aspects and describe how the model can be integrated into the community’s logistics labeling. Most hedonistic interfaces do not have an appropriate model that can correctly measure the assessment of having made a suitable match. Therefore, there is no model of user satisfaction associated with the search for a virtual dating center, which is why we propose a way to analyze the labels associated with an experience in a hedonistic interface and its associated satisfaction considering ontologies and an innovative metaheuristic. This metaheuristic allows to group-specific characteristics considering attributes associated with archetypes of male escorts.
Study of bio-inspired neural networks for the prediction of liquid flow in a process control system

This research investigates the use of a bio-inspired algorithm (BIA) in preparing an artificial neural network in the field of a nonlinear liquid flow model. To improve the expanding multifaceted nature and operational productivity of fluid stream measures, plans that can improve model recognizable proof of exceptionally nonlinear frameworks are required. As a contextual investigation, the WFT-20-I measure control arrangement for flow rate measurement and control issue is discussed. The key issue is to locate a specific design for an artificial neural network (ANN) that best models the fluid stream measure. The authors propose the utilization of BIAs with particle swarm improvement using particle swarm optimization and firefly calculation using a firefly algorithm as systems to upgrade the synaptic weights of ANN. All these algorithms were used to improvise the weight of an ANN so that it poduces local optimum of objective function of a liquid flow rate. The proposed model was formulated with broad tests & factual investingation to show the appropriateness of the whole demonstration. The outcomes were investigated utilizing the root mean square error exactness, and other measures to evaluate the level of identification performance of the liquid flow contextual analysis model. The trustworthiness of the present models was compared with earlier models for similar subsystems utilizing competitive intelligent methodologies. The outcomes of the present model show that the proposed approach gives satisfactory output and outperforms its rivals.
Code Comprehensibility Evaluation for Java Class based on LDA Topic Model

The rapid development of the current software industry has produced a large amount of software, to evaluate the quality of a software code is difficult, which is also not conducive to software maintenance and iteration. Among them, software comprehensibility is one of the most important factors for software maintainability, and it is one of the indispensable elements for software quality evaluation. However, most of the existing software quality evaluation only stay in the concept level, lack of corresponding evaluation methods. From the perspective of java class code, this paper constructs a comprehensibility evaluation method for java class code based on LDA topic modeling, implements the software class code intelligibility evaluation, and quantifies the experimental results using clustering algorithm and WordNet similarity calculation method. Experiments show that the proposed method can clearly distinguish the comprehensibility of class code and obtain reliable evaluation results of software class code intelligibility.
An Improved Model for Clarification of Geospatial Information

The article formulates an improved mathematical model for clarification of imagery for an area of the earth’s surface. An improved mathematical model for clarification of geospatial information has been formulated in general form, which can be presented as a result of the action of an operator that transforms coordinates, operators that conduct image clustering, and operators that carry out zoning according to some criterion. The co-ordinates transformation operator, clustering operators, zoning operators, and their explicit form are presented. This model, based on the model of forming imagery, performs the inverse transformation of the imagery coordinates into spatial coordinates and clustering imagery into particular classes according to their texture and color. This takes into account geographic zoning. A model for clarification of geospatial information in operator form is obtained.
Impact of Privacy Issues on Successful Implementation of Personalized Medicare System

Personalized medicare systems is an emerging field of research, which bears the potential to significantly reduce healthcare expenditures and treatment errors and thereby to revolutionize the entire treatment procedure. In this novel approach, genomic variation in different individuals is duly taken into consideration. However, there exist several serious issues (e.g. privacy concerns) that provide hindrance to large-scale adoption of this medicare system. The main objective of this study has been to identify the privacy issues and to evaluate their impact on successful implementation of this novel medical treatment. The methodology used is empirical and is based on a survey-based post facto procedure. The data collected from the survey are analyzed by using the method of structural modelling analysis. This is an original study in the realm of healthcare management, which reveals that the technology related factors and privacy concerns have considerable impact on the successful implementation of personalized medicare system on a large scale. But the privacy concerns have no significant moderating effect on the impact of technology related factors, so far, the success of implementation of personalized medicine is concerned.
A Chinese-Thai Cross-language Word Embedding Method Based on Unequal Corpus of Small Dictionaries

This paper proposes a Chinese-Thailand cross-language word embedding method based on the unequal corpus of a small dictionary. The method first normalizes the word vectors of Chinese and the small dictionary, and obtains the gradient descent for the orthogonal optimal linear transformation of the small dictionary words. The initial value is then clustered on a large Chinese corpus, with the help of a small dictionary to find the Chinese word vector corresponding to each cluster cluster, and take the mean value of each cluster word vector obtained by the clustering and the mean value of the word vector corresponding to Chinese and Thai, Establish a new bilingual word vector correspondence, and extend the newly established bilingual word vector to the small dictionary, so that the Chinese-Thai small dictionary can be generalized and expanded. Finally, use the Chinese-Thai dictionary after generalization to perform gradient descent on the cross-language word embedding mapping model to obtain the optimal value.
Applying Artificial Intelligence to the Data Association Problem

Data Association decisions are more optimal as the size of the global scoring matrix increases, however creating and solving the global assignment matrix becomes computationally intractable as the size of the matrix grows. This effort attempts to replace traditional steps of first creating and then solving the global assignment matrix with a single neural network. While the accuracy results for the 3x3 and 4x4 square assignment problems show that using the neural network may be a viable substitute for the JAM and solver algorithms, the 5x5 and 7x7 results are less than desired. The number of neurons required in the hidden layer in order to achieve reasonable results also call to question the computation value.
Abstractive Sentences Summarization Model Combining Entity Information and Bidirectional Decoding

Text summarization is a valuable research task in the field of Natural Language Processing (NLP). The Sequence-to-Sequence model is currently the mainstream model of abstractive summarization. However, most abstractive summarization models do not consider named entity information separately. Named entity incompleteness often occurs when long text generates a summary. Based on the characteristics of generating summaries, error accumulation is also the main problem causing summary discontinuity. In this paper, we proposed an abstractive summarization model NER_Bideocder, which combines entity information with bidirectional decoding based on a sequence-to-sequence model. Named entity recognition tags are used for text, and the contents of the tags are used as entity information when encoding; meanwhile, decoding uses a bidirectional LSTM structure, which combines the decoding results in both directions with attention mechanism to generate summarization. Experiments on NLPCC2017 task3: Single Document Summarization dataset show that the performance of this model is better than other models.
A Comparative Study of PnP and Learning Approaches to Super-Resolution in a Real-World Setting

Single-Image Super-Resolution has seen dramatic improvements due to the application of deep learning and commonly achieved results show impressive performance. Nevertheless, the applicability to real-world images is limited and expectations are often disappointed when comparing to the performance achieved on synthetic data. For improving on this aspect, we investigate and compare two extensions of orthogonal popular techniques, namely plug-and-play optimization with learned priors, and a single end-to-end deep neural network trained on a larger variation of realistic synthesized training data, and compare their performance with special emphasis on model violations. We observe that the end-to-end network achieves a higher robustness and flexibility than the optimization based technique. The key to this is a wider variability and higher realism in the training data than is commonly employed in training these networks.
Completing bases in four dimensions

Criteria and constructive methods for the completion of an incomplete basis of, or context in, a four-dimensional Hilbert space by (in)decomposable vectors are given.
A Secure ECG based Smart Authentication Scheme for IoT Devices

In this super-evolving world, there exist numerous hidden vulnerabilities and any kind of security breach in recent times would have implications in ways which are even difficult to imagine. To counter such probable scenarios the demand evolved to design security protocols which are sound enough to counter most of such situations in near future. In this scheme, an ECG based user authentication protocol for smart IoT devices is proposed. ECG is one of the unique characteristic traits of any individual which is experimented to remain similar throughout, provided any biological changes have not occured. The scheme uses the captured ECG signal from a smart IoT device taken as raw input by the computation model. The signal is then used to extract the user characteristics such as the QRS complex, P peak, T peak, etc. A feature vector is derived after pre-processing of the signal, which is then used by the computation model to check for the authenticity of the individual. The final authentication of the user depends upon the similarity index of the feature vector generated by the existing user’s record captured during registration. The data used here is from the ECG ID database and the obtained accuracy is measured close to 94.2%. A comprehensive study of the performance of the protocol is presented taking communication cost, computation cost, storage cost, and even the security features as the parameters for comparison.
Research on Intelligent Transformer Differential Protection System Based on "Plug and Play"

In order to realize the differential protection of transformer better, the research of intelligent transformer differential protection system based on "plug and play" is put forward. In the aspect of hardware, "plug and play" is used to realize the connection of differential protection. According to IEC 60870-5-104 transmission protocol, the communication between transformer and differential protection device is established to realize the mutual transfer of information after verification. In the aspect of software, the information automatic checking service from main station to substation and from substation to differential device is accomplished by information automatic checking. On the basis of the basic principle of current differential protection, the brake quantity is introduced and the criterion of ratio differential protection is analyzed. The differential protection device is used to collect the checked information, and the secondary harmonic of the inrush current is calculated after A/D conversion to complete the differential protection. The test results show that the system has higher information automatic check efficiency and ratio differential protection operation efficiency, and can better complete the differential protection of intelligent transformer.
Hierarchical Language Modeling for Dense Video Captioning

The objective of video description or dense video captioning task is to generate a description of the video content. The task consists of identifying and describing distinct temporal segments called events. Existing methods utilize relative context to obtain better sentences. In this paper, we propose a hierarchical captioning model which follows encoder-decoder scheme and consists of two LSTMs for sentence generation. The visual and language information are encoded as context using bi-directional alteration of single-stream temporal action proposal network and is utilized in the next stage to produce coherent and contextually aware sentences. The proposed system is tested on ActivityNet captioning dataset and performed relatively better when compared with other existing approaches.
UPLIFT: Usage of Poverty Level Indicators to Facilitate Transformation

UPLIFT: Usage of Poverty Level Indicators to Facilitate Transformation, as the name suggests, is a study that aims at analysing data consisting of various socio-economic factors that affect households in order to alleviate poverty by understanding and identifying various aspects that may have an impact on poverty levels. As a use case, we have studied data pertaining to the Latin American nation of Costa Rica. Initially, we perform exploratory data analysis to discover patterns in the data and gain an insight to the relationship between attributes. We then perform feature selection and normalization to render the data suitable for modeling with machine learning techniques. We use these models to predict the economic health of each household, mapping them into four categories corresponding to the level of poverty. Finally, the data is reinterpreted in the light of this classification to elicit the factors that strongly affect each category, towards recommending methods for improvement and an appropriate allocation of social welfare benefits.
Pattern Matching Algorithms: A Survey

In an enormous amount of factual data, it is necessary to find necessary information that can lead us to meaningful work. One such domain that does this work in pattern matching. The work of pattern matching is to provide us with information whether a particular pattern exists or not in given data. The algorithms of pattern matching fall in two categories, single and multiple, according to the number of patterns they can find, out of which the latter has wider applicability as compared to former. Such algorithms not only signify all the occurrences of particular patterns but are also useful in their analysis, which may lead to significant information. These algorithms have wide applicability due to the presence of abundance of data over the Internet. The paper emphasizes detailed study on the algorithms that can find more than one pattern, followed by a comparative analysis.
Conditional Generative Adversarial Network for Devanagari Handwritten Character Generation

In the deep learning era, the model requires more variations in the large dataset for better understanding. Generative Adversarial Networks (GANs) are often used to learn to generate various images. The design of GAN consists of a collection of neural networks used in an unsupervised way. In GAN, many kinds of arrangements of neural networks are possible for various applications in many areas of computer vision and machine learning. Conditional Generative Adversarial Network (CGAN) is one of them helpful in the generation of the definite type of images. This work illustrates the use of CGAN for generating specific Devanagari handwritten characters.
High resolution imaging of fatigue damage evolution in fiber reinforced composites using digital volume correlation for model validation

Detailed experimental results in support of the probabilistic progressive damage model, SD2M are presented. The SD2M model is able to replicate damage modes that are common between quasi-static and fatigue loading of laminated composites. Validations of the model have been done in the past using limited experimental data, however, due to the complexity of fatigue mechanisms, a more detailed validation is necessary . Digital Image Correlation (DIC) was used to obtain the strain response and characterize the crack density evolution in cross-ply specimens loaded quasi-statically in tension. A Fourier analysis was done to co-relate the initial crack density observed in the DIC analysis to variations in fiber volume fraction measured from microscopic images of the composite. Based on these results, it was established that fiber volume fraction variations can be used to guide the determination of stiffness and strength distributions in a SD2M virtual rendition. A proof-of-concept test was done to verify that Digital Volume Correlation (DVC) can be used to non-destructively observe internal damage modes such as delamination and matrix splitting.