Reviewerly/exHarmony huggingface dataset:
An efficient privacy-preserving data publishing in health care records with multiple sensitive attributes

Emerging advanced technologies in the e-health sector have improved the industry, helping to invent new medicine, reduced cost, better-quality consultation, and treatment. The improvements have originated from the patient record analysis that is shared between the health care provider and researchers. Due to this collaboration, there is an emerging security and privacy requirement in the health care sector. Privacy-preserved data publishing plays a significant role in preventing the disclosure of individual identity. Many privacy-preserving models have been proposed earlier for data publishing. However, they still suffer from few drawbacks i) cannot be applied on 1: M dataset ii) no trade-off between privacy and utility. To satisfy the multi-record privacy and trade-off between classification utility and privacy, we proposed Quasi-identifier Bucket-Individual Multi-Sensitive Attribute Bucket (QIAB-IMSB) algorithm. The main focus of our work is to anonymize the multi-valued record of an individual. The initial phase of our work is to merge the multi-record of an individual. Next, we implement vertical partitioning and apply k-anonymity for quasi-identifier bucket (QIAB) and (k, l) - diversity for individual multi-sensitive attribute bucket (IMSB). Our proposed approach mainly focuses on preventing the sensitive linking attack by implementing hierarchical generalization taxonomy. Our experimental results proved that there is an improved privacy and classification utility in privacy-preserving data publishing of health care records.
Hybrid Data Privacy and Anonymization Algorithms for Smart Health Applications

In line with the concept of Smart cities and Smart World, there have been bewildering developments in the field of Internet of Things (IoT) and Big Data Analytics where one most noteworthy territory is that of Smart Health. Private Health data are required to be transmitted over wireless communication systems where the main problem arising is that of data security and privacy. Various algorithms have been implemented to achieve data privacy. In this work, k-anonymity, differential privacy, k-map, $$\ell$$ -diversity and a hybrid of the three algorithms combined with ℓ-diversity have been analyzed using algorithms running on a raspberry pi (edge) for a breast cancer dataset. The results demonstrated a difference in using stand-alone algorithms in terms of output anonymized dataset, execution time, information loss, risk analysis, and statistical analysis. If solely based on its execution time, the hybrids took less than 20 s in comparison to the stand-alone taking nearly 90 s, leading to conclusion that hybrid algorithms are much more suitable in terms of efficiency. Having the option to share anonymized information quicker will reduce financial and admin costs indicating that hybrids should be the main focus even if all algorithms fulfilled their objective of achieving data privacy.
TSW-FD: A Novel Temporal and Spatial Domain Weight Analysis of Feature Difference for Micro-Expression Spotting

Abstract The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME II and SMIC-E-HS.
Implementing innovative teaching methods in the digital environment

The article examines the issues of adopting the innovative teaching methods to effectively organise the educational process within blended and distance learning and teaching. The examples of curricula design using modern information technologies and elements (resources and activities) of the digital learning environment of Penza State University are given. The teaching methods and features of their employment are described. The authors highlight the need for further elaborating the issues of ongoing methodological support for teachers in the field of information and pedagogical technologies.
Study of Artificial Intelligence Flight Co-Pilot Speech Recognition Technology

Speech recognition technology, as part of human-computer interaction, is essential for machine intelligence. The utilization of robots as the co-pilot in civil aircraft is the major breakthrough and innovation direction in the civil aviation industry. The application of speech recognition technology to the the robot co-pilot can make the command of the captain directly to the co-pilot program, making it possible to cooperate between the captain and the robot co-pilot. In view of the above background, according a standard callouts speech cropus, using the end-to-end speech recognition methods, three CTC speech recognition models were built. One of the speech recognition models named the Bi-LSTM recurrent neural network speech recognition based on CTC, the training error rate is reduced to 1.2%, and the test error rate is reduced to 3.2%. Therefore, the Bi-LSTM speech recognition model is used as the speech recognition system of the artificial intelligence co-pilot.
A Survey on Filtering Techniques for Recommendation System

The advancement of technology in recent years have caused a tremendous development in the ecommerce industry. With the growth in ecommerce, recommendation systems have gained importance in research. The recommendation system is based on different filtering techniques to provide appropriate personalized suggestions to the user. In this paper we focus on the analysis of the commonly used filtering techniques of recommendation system and provide a comparison of these techniques.
Developing online interactive learning media by using easyclass with geogebra to help students representation mathematic on linear programming

Abstract The power of mathematical representation by applying concepts needs to be improved so that students can remember mathematical concepts sustainably. The purpose of this study to development and describe the process of interactive online media learning using a geogebra aid easyclass to help students represent mathematically on linear program materials. Research model is research dan development 4-D Thiagarajan. Twenty-four sophomores of science 4 man 2 jember. Media is tested based on three criteria that are valid, practical and effective. Validity of learning media when reaching high categories with correlation coefficient values ( α ) &gt; 0,6. The practicality of learning media when achieving good categories with a percentage &gt; 60%. The effectiveness of learning media when the average score of a student’s study results tests meets completedness, 80% of all students get a score greater or equal to the Minimum Completed Criteria = 75. Studies show 0.917 correlation value and therefore valid to a very high category. Practical analysis based on student responses indicates a percentage of 99 percent so it is stated practical by excellent criteria. The effectiveness is analyzed based on a student’s mathematical representation on a matter and quiz exercise when after using the learning media. Based on data analysis, interactive online learning media use a geogebra aid linier program material meets valiant criteria, practicality and effectiveness so that media can help students reflect mathematically on the linear program material and can be used for learning activities.
Fish Image Instance Segmentation: An Enhanced Hybrid Task Cascade Approach

The fish instance segmentation task plays an important role in fish image analysis. Traditional fish analysis methods (e.g., segmenting the fish curve by hand to obtain the size of fish) cost a mass of manual labor and thus are not efficient. Convolutional Neural Networks (CNNs) become an effective scheme to replace manual labor to decrease costs and improve efficiency. The Hybrid Task Cascade (HTC) is a novel CNN model which applies cascade architecture to achieve boosted performance in the instance segmentation task. However, instance segmentation models cannot handle fish images very well due to small image size and low image quality. Furthermore, HTC still suffers from the incomplete confidence score only consisting of the classification information without the mask information, so the instance segmentation performance would be degraded. To this end, we propose an Enhanced Hybrid Task Cascade (EHTC) model to overcome these limitations. (1) The EHTC conducts data pre-processing before the instance segmentation network through an image super-resolution technology to resize images and optimize features that can be more easily understood by the later instance segmentation network. (2) Our EHTC addresses the incomplete confidence score problem in the HTC by adding one mask scoring block, named MaskIoU, to generate mask confidence scores providing the mask that improves the instance segmentation accuracy. Finally, the experimental results show that our EHTC achieves better performance than the state-of-the-art models on the Fish4knowledge dataset.
Data Security Implementation using Data Encryption Standard Method for Student Values at the Faculty of Medicine, University of North Sumatra

Abstract It is undeniable that advances in technology and the rapid spread of information are also accompanied by an increase of crime in the IT field, very valuable information is sought by criminals in the IT field in order to be misused so that they gain enormous profits. One of the information is student value data, so a system is needed that applies a cryptographic algorithm that can secure the information in this case the method used is securing student value data using the Data Encryption Standard (DES) algorithm. DES is asymmetrical cryptographic algorithm and is also classified as a cipher block with a 64-bit key size. DES converts plaintext into a ciphertext of the same size, 64 bits using a 56-bit internal key. By building a system to implement the DES algorithm in securing data values, it is hoped that it can help the Faculty of Medicine, University of North Sumatra in protecting the confidentiality of data values from irresponsible parties
Reliable Identity Management System Using Raspberry Pi

Biometric authentication, the process of verification using unique biological characteristics, is considered one of the most accurate technologies for authentication. It compares two or more biometric data to identify the differences among them properly. Based on the comparison between individual's biometric features and stored authorized features, it makes the decision of matched or mismatched. If the given sample of the biometric data matches with the stored one, authorization is confirmed. Nowadays, many real-time applications use biometric authentication to meet security purposes fully. However, it is difficult to implement fingerprint recognition system specially on Linux based computer. A well-known, less costly embedded computer called Raspberry Pi is used to build the entire system. Fingerprint enrollment, fingerprint recognition, photo capture, QR code generation as well as QR code reading were entirely done using python and python associated libraries. For storage management, we have used MySQL as it is convenient and open-source. This paper discusses about a quality authentication model which is highly capable and cost- effective. The authentication process consists of extracting the biometric features with ORB algorithm, matching those features using FLANN matcher, generating and validating QR code based on encrypted personal information accurately. Here, AES algorithm is used for encrypting personal data which is then converted and stored in form of QR code. Final authentication process involves both fingerprint and QR code validation. Performing two factor authentication affixes more reliability to the system.
Deep learning for ovarian follicle (OF) classification and counting: displaced rectifier linear unit (DReLU) and network stabilization through batch normalization (BN)

Background and aim: Diagnosis and treatment of female infertility conditions would help future reproductive planning. Although current deep learning frameworks are able to classify and separately count all types at high accuracy, these solutions suffer from a misclassification error and a high computation complexity due to a positive bias effect and an internal covariate shift. The objective of this paper is to increase the classification accuracy of OFs and to reduce the computational costs of classification via deep learning (DL). Methodology: our framework for follicle classification and counting (FCaC) uses filter-based segmentation. A new method is also proposed to accelerate learning and to normalize the input layer by adjusting and scaling the activations. Our method uses a modified activation function (MAF)- displaced rectifier linear unit (DReLU) and batch normalization (BN) in Feature Extraction and Classification. Therefore, faster and more stable training is achieved by modifying input distribution of an activation function (AF). Results: The proposed system was able to obtain a mean classification accuracy of 97.614%, which is 2.264% more accurate classification than the state-of-the-art. Furthermore, the model processes a single WSI 30% faster (in 10.23 seconds compared to 14.646 seconds processing time of the existing solutions). Conclusion: The proposed system focuses on processing histology images with an accurate classification. It is also faster, has an accelerated convergence and enhanced learning thanks to BN and the EAF. We considered a positive bias effect and internal covariate shift as the main aspects to improve the classification performance.
CLIONE: Clone Modification Support for Pull Request Based Development

A code clone (clone) is known as one of the factors that makes software maintenance difficult. Thus, in software maintenance, clone modification is essential. An existing study proposed a tool that notifies developers of information about clone changes so that the developers can modify clones efficiently. However, the existing tool is premised on regular execution and not designed to be triggered by external factors except for time. Hence, the existing tool is difficult to be executed triggered by development workflow, such as modifying source code or merging branches, and we think this causes some issues. Consequently, in this study, we propose a new clone modification support technique aimed to integrate into pull request (PR) based development for solving those issues. The proposed technique detects code fragments that need modifications by tracking clones at the time of creating PRs. Moreover, we made three improvements for more accurate clone change tracking. Additionally, we implemented the proposed technique as a software tool, CLIONE. To evaluate CLIONE, we investigated the proportion of PRs in which clones have been modified non-simultaneously, and also we compared the results of clone change tracking with the existing tool. As a result, 11.9%~30.4% of PRs included non-simultaneously modified clones, and we confirmed that CLIONE was able to track clone changes more accurately than the existing tool. CLIONE is available at https://github.com/T45K/CLIONE.
Robust Web Data Extraction Based on Weighted Path-layer Similarity

Web data extraction techniques often focus on accurate and efficient information acquisition from webpages. However, webpage variants cause frequent extraction to fail and result in high maintenance costs. Significant effort is attracted to robust extraction, but most either require complex pre-processing or supplementary files. In this paper, a novel method is proposed to enhance extraction robustness by using datatype and weight information of path-layers. The similarities between paths of the target node in the original webpage and candidate nodes in page variants are calculated to determine the node with the highest possibility. Experiments on a large set of real data show that this method yields better robustness than the existing approaches.
De-noising method of aero-engine gas path electrostatic signal based on sparse representation

Aero-engine gas path electrostatic signal often have various types of noise interference, and valuable signals are usually weak and obliterated among the many strong noises. In order to solve this problem, the noise reduction method based on the sparse representation of the aero-engine gas path electrostatic signal was studied. The electrostatic signal of a civil aviation turbofan RB211 aero-engine test record was selected as the experimental data, which effectively removed the noise interference in the original signal, verifies the effectiveness of the proposed method. The processing of aero-engine gas path electrostatic signal can effectively provide early warning of engine operating status and improve safety margins.
Face verification algorithm based on Angular Margin triplet

Abstract In recent years, the face recognition algorithm based on CNN mainly focuses on the improvement of loss function. This paper improves the softmax loss function from two aspects: one is to normalize the weight and features of the loss function, the other is to introduce in class cosine similarity judgment on the basis of the original loss function. These improvements eventually make the distribution of features within the class closer and the distance between classes as far as possible. The recognition accuracy and generalization ability of the network are enhanced.
DCFD-Net for Segmentation on MR Images of Right Ventricle

With the successful applications in image processing, convolutional neural networks (CNNs) have been transferred into medical image processing and a great success has been achieved over the past few years. The U-net architecture is one of the most famous CNN architectures for semantic segmentation in the field of medical image segmentation. However, high-level semantic feature information learned by convolution layers can't make the pixels on target image's edge easily distinguished in semantic segmentation. In this paper, we propose a novel neural network called dense connection of fully dilated-convolution network to deal with this challenging problem. The proposed DCFD-Net fuses different sizes of receptive fields through dense connection to reuse all feature maps and replace the basic architecture of down-sampling and up-sampling with dilated convolution. Two-level losses method and post processing are proposed in this paper to make model training more adequate. Our proposed neural network architecture outperforms U-Net in segmentation on MR images of right ventricle.
The effect of the use of smartphone as an e-learning tool to students’ performance

This study aims to determine the influence of the use of smartphone as an e-learning tool to students’ performance. This experimental research was conducted online. The research sample was students in SMP Negeri 1 Kota Langsa which consist of two classes, VIII A as the experimental class and the VIII B as the control class. Data collection was carried out by distributing question test, and the data were analyzed by using t-test data analysis. The results of the research reveal that the experimental class t-table is greater than its t-count, where t-count = 9.345 ≥(ttab) = 2.001, which means Ho is rejected and Ha is accepted. The hypothesis testing concludedthat there is the influence of the use of smartphones on students’ performancein SMP Negeri 1 Langsa.
Continual Learning of Visual Concepts for Robots through Limited Supervision

For many real-world robotics applications, robots need to continually adapt and learn new concepts. Further, robots need to learn through limited data because of scarcity of labeled data in the real-world environments. To this end, my research focuses on developing robots that continually learn in dynamic unseen environments/scenarios, learn from limited human supervision, remember previously learned knowledge and use that knowledge to learn new concepts. I develop machine learning models that not only produce State-of-the-results on benchmark datasets but also allow robots to learn new objects and scenes in unconstrained environments which lead to a variety of novel robotics applications.
Generative Adversarial and Self-Attention Based Fine-Grained Cross-Media Retrieval

Deep convolutional neural networks have recently demonstrated an impressive ability to conduct the task of fine-grained cross-media retrieval. However, existing fine-grained cross-media retrieval algorithms offer comparatively low retrieval accuracy and are difficult to apply in practice because of three challenging difficulties. Firstly, videos contain many noise frames which may affect the extraction of features. Secondly, existing algorithms deal with different modalities in an indiscriminative way, which ignore the characteristic of each modality, for example, the sequence characteristic of the text. Thirdly, the lack of joint semantic space learning limits retrieval accuracy. To overcome the drawbacks, we propose a novel fine-grained cross-media algorithm, which is based on the generative adversarial network and self-attention mechanism. Our approach firstly removes noise frames in the videos by a spatial cluster filtering algorithm to obtain more pure video data. Then we extract features of each modality. It should be noted that text features are extracted by a self-attention based LSTM structure. Finally, a generative adversarial network is used to learn the common semantic space for features of all modalities. Experimental evaluations on a new benchmark FGCorssNet demonstrate the improving results compared to other counterpart methods. The source codes, models, and data have been made anonymously available at https://github.com/gasanet/GASA.
Facilitating Cryptojacking Through Internet Middle Boxes

The usage of anonymous proxies and virtual private network has increased due to the privacy and Internet censorship issues. The traffic passing through proxies (Middle boxes) can be easily intercepted and modified by the controller to perform man-in-the-middle attacks like data injection, data tampering, and data deletion. A stealthy attack called cryptojacking started infecting the popular Web sites to mine cryptocurrency without the Web site visitor’s consent. This paper proposes an effective and stealthy approach to perform cryptojacking attack by injecting cryptomining script on anonymous proxy’s Web site traffic. To increase the efficiency of the attack on larger scale, a testbed environment for private The onion router (Tor) network is deployed to implement the same attack on tor exit node. Our study shows that covertness of the attack can be improvised by varying the central processing unit usage of the victim during mining to avoid detection. The existing defensive mechanism to prevent this attack is also reviewed.
Enhanced Advanced Encryption Standard with Randomised S Box

Networks and communications today is an inherent part of any military and defence force organisation. Internet of things (IoT) is also making inroads into various defence networks and applications. Such networks and applications have sensitive data riding on them which is vital national security. Hence, the requirement of protecting and securing this data. The Advanced Encryption Standard (AES), is a Federal Information Processing Standard (FIPS) for symmetric cryptography since 2001, and is the most secure symmetric in the public domain. However, the AES being a public algorithm and also known to be vulnerable to various crypt analytical attacks, cannot be used in its original form for military networks and applications. This paper does an extensive review of the various proposed modifications to the AES algorithm, and proposes an enhanced AES algorithm with a randomised S box, which provides better security than plain AES with negligible overheads and hence can be employed for military networks and IoT applications.