Reviewerly/exHarmony huggingface dataset:
A Survey on Application of Machine Learning Algorithms in Cancer Prediction and Prognosis

Most of the developed countries are facing the threat of rapid growth in cases of cancer patients. Cancer is one of the diseases that lead to a high number of deaths every year. Cancer treatment efficacy in medical practice majorly depends upon prediction of cancer susceptibility, cancer prognosis, and cancer recurrence. In recent years, statistical approach of machine learning techniques is proven to be a boon for diagnosis, classification, prediction, and prognosis purposes in health care. Various researchers are implementing machine learning concepts to improve cancer prediction and prognosis. Decision trees, KNN, SVM, and Neural networks are applied to predict the survivability of cancer patients with high accuracy. Recently, researchers are more focused on Deep Learning for better results. However, features for cancer prognosis vary with the type, site, and stage of cancer. For oncological statistical studies, histopathological, biomarker, and genes expression profile data are analyzed, and relationships are inferred for cancer prediction and prognosis. A majority of the studies have been carried out in breast, prostate, and lung cancer as these are most prevailing cancer types. With the emerging technological hazards, environmental factors, and ill habits of people, other types of cancer are also becoming more and more common. The present study provides a review of various features for cancer prognosis and accuracy in prediction of cancer prognosis using different machine learning techniques.
Design of face recognition system based on CNN

Abstract This paper studies and implements a face recognition system based on convolutional neural network. The system firstly uses AdaBoost algorithm to detect the face, that is to get the position and size of the face accurately in the image, then uses the deep convolution neural network to extract the face features and classify them. Finally, the hardware application Altera DE1_SOC development board, designs and completes a face recognition system with high recognition rate.
A Novel Image Encryption Method Based on LSB Technique and AES Algorithm

Digital image transfer security is presently vital in image communications, as pictures are more and more utilized in industrial processes. It’s necessary to safeguard confidential image information from unauthorized access. Image security has become a vital downside, and privacy challenges are steadily increasing. Many strategies of knowledge protection and privacy were mentioned and developed. Coding is maybe the foremost obvious. Several methods of data protection and privacy were discussed and developed. Encryption is probably the most obvious. To protect valuable information from unwanted drives, image encryption is necessary. In this paper, we introduce information masking and digital image encryption that enhance the security of confidential data and digital images using LSB technology and the Advanced Encryption Standard (AES) algorithm. To hide data in the image, we use LSB technology, and to encrypt the embedded image, we use the AES algorithm.
Modelling and Experimental Validation of Articulated Mobile Robots with Hybrid Locomotion System

In recent years, autonomous vehicles and mobile robots are starting to become a trend also in several new fields of application. In some cases, they are articulated vehicles with an active front module and a rear one that is pulled passively or that can contribute to the vehicle traction when required. Although modelling of mobile robots is not a novelty, most of the available studies are limited to kinematic or very basic dynamic models. In order to correctly simulate mobile robots with fast dynamics or operating off-road, this study proposes a dynamic model of such systems derived taking into account lateral and longitudinal slip in the wheels. The model is then validated experimentally using a small articulated robot.
Transpension: The Right Adjoint to the Pi-type.

Presheaf models of dependent type theory have been successfully applied to model HoTT, parametricity, and directed, guarded and nominal type theory. There has been considerable interest in internalizing aspects of these presheaf models, either to make the resulting language more expressive, or in order to carry out further reasoning internally, allowing greater abstraction and sometimes automated verification. While the constructions of presheaf models largely follow a common pattern, approaches towards internalization do not. Throughout the literature, various internal presheaf operators ($\surd$, $\Phi/\mathsf{extent}$, $\Psi/\mathsf{Gel}$, $\mathsf{Glue}$, $\mathsf{Weld}$, $\mathsf{mill}$, the strictness axiom and locally fresh names) can be found and little is known about their relative expressivenes. Moreover, some of these require that variables whose type is a shape (representable presheaf, e.g. an interval) be used affinely. We propose a novel type former, the transpension type, which is right adjoint to universal quantification over a shape. Its structure resembles a dependent version of the suspension type in HoTT. We give general typing rules and a presheaf semantics in terms of base category functors dubbed multipliers. Structural rules for shape variables and certain aspects of the transpension type depend on characteristics of the multiplier. We demonstrate how the transpension type and the strictness axiom can be combined to implement all and improve some of the aforementioned internalization operators (without formal claim in the case of locally fresh names).
Experimental Approaches to Measure Displacements in Mechanical Systems Through Vision Devices

Using vision devices to get information from mechanical systems is a huge advantage: indeed, having a contactless is a massive benefit since no other sensor affects the system and somehow system keeps its integrity.This paper deals with the use of vision techniques to measure motion of mechanical systems. We have proposed three different methodologies that employ marker and no-marker approaches in combination with a low cost RGB-depth device.
Security architectures for sensitive Data in Cloud Computing

Security of sensitive data in cloud computing is a very challenging. In computing platform, data is distributed among different systems in parallel and this makes the high of data leakage because it is present in various places around the world and shares in distributed systems. In this era of digitization security of data in cloud computing are the key factors in a user's point of view in cloud technology. Although many technologies have been researched and applied in academics and industries to solve cloud computing data security problems. Protection of data and privacy in cloud computing for sensitive data is more challenging for the next generation technologies of cloud computing technology in government organizations, banking, finance, private industries and the e commerce. Normally in cloud computing these issues in data which related to hardware and software can be avoided by applying data storing and retrieving architecture.
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning

Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-optimizing inter-dependent factors both at the per-job level and at the cluster-wide level. Most existing schedulers will assign each job a number of resources requested by the user, which can allow jobs to use those resources inefficiently. Some recent schedulers choose job resources for users, but do so without awareness of how DL training can be re-optimized to better utilize those resources. Pollux simultaneously considers both aspects. By observing each job during training, Pollux models how their goodput (system throughput combined with statistical efficiency) would change by adding or removing resources. Leveraging these models, Pollux dynamically (re-)assigns resources to maximize cluster-wide goodput, while continually optimizing each DL job to better utilize those resources. In experiments with real DL training jobs and with trace-driven simulations, Pollux reduces average job completion time by 25%-50% relative to state-of-the-art DL schedulers, even when all jobs are submitted with ideal resource and training configurations. Based on the observation that the statistical efficiency of DL training can change over time, we also show that Pollux can reduce the cost of training large models in cloud environments by 25%.
AUTOMATIC REGISTRATION OF VECTOR DATA WITH OPTICAL IMAGES

Abstract. This paper presents a method to perform automatic vector-to-image registration. The proposed method performs well on different kinds of optical satellite images from Very High Resolution (VHR, sub-meter resolution) to images in the 10/20 m resolution range. It allows to automatically register vector dataset such as urban maps (by using building layers). In contrast with existing methods, our method needs few prior-knowledge on the features to match and can therefore adapt to different landscapes.This paper demonstrates the method robustness in several use-cases and presents the implementation which will soon be available as open-source software.
Personal Qualities Of Teachers' As Factors Of Their Psychological Well-Being

The article is dedicated to the problem of ensuring the psychological safety of primary schoolchildren in the educational environment, which is relevant for primary education. An important condition for ensuring the children’s safety is the psychological well-being of the teacher’s personality, which helps to create psychological comfort in the classroom, remove psycho-logical barriers, and create psychologically safe conditions for the children’s development. This problem is of particular importance for inclusive education. The article analyzes the results of a study of the personal qualities of primary school teachers as factors in their psychological well-being. The correlation between the indicators of the scales according to the six-factor model of psychological well-being by K. Ryff, a technique based on the eudaimonistic approach, the main idea of which is to understand that personal growth is a prerequisite for experiencing psychological well-being, and the personal characteristics of teachers, measured using 16 factor personality questionnaire by R. Cattell. The personal qualities of elementary school teachers associated with their psychological well-being were determined: geniality, openness; the power of "I", emotional maturity, emotional stability; self-confidence; tendency to take the lead/be a leader; courage; calmness; self-sufficiency; equanimity, relaxedness. Qualities that impede psychological well-being: insularity, detachment; weakness of "I"; compliance; timidity; diffidence; tendency to feeling guilty; group dependence; constraint. The results of the study can be taken into account when selecting teachers for inclusive classes, as well as for the development of psychological support programs for personal and professional self-development of teachers and also in the practice of psychological counselling.
CapsNet vs CNN: Analysis of the Effects of Varying Feature Spatial Arrangement

Despite the success over the recent years, convolutional neural network (CNN) has a major limitation of the inability to retain spatial relationship between learned features in deeper layers. Capsule network with dynamic routing (CapsNet) was introduced in 2017 with a speculation that CapsNet can overcome this limitation. In our research, we created a suitable collection of datasets and implemented a simple CNN model and a CapsNet model with similar complexity to test this speculation. Experimental results show that both the implemented CNN and CapsNet models have the ability to capture the spatial relationship between learned features. Counterintuitively, our experiments show that our CNN model outperforms our CapsNet model using our datasets. This implies that the speculation does not seem to be entirely correct. This might be due to the fact that our datasets are too simple, hence requiring a simple CNN model. We further recommend future research to be conducted using deeper models and more complex datasets to test the speculation.
Concept Drift Detection on Data Stream for Revising DBSCAN Cluster

Data stream mining of IoT data can help operators immediately isolate causes of equipment alarms. The challenge, however, is how to keep the classifiers high-purity (i.e., keep data of the same class in the right cluster) while dealing with the concept drifting ascribed to differences between alarm models and entities. We propose continuously revising the classification model in accordance with the data distribution and trend changes. Evaluations showed there was no purity deterioration for oscillation condition data with a drifting rate of 1%. This result demonstrates that our approach can help operators improve their decision making.
SafeEat: Extraction of Information About the Presence of Food Allergens in Recipes

The application of artificial intelligence is becoming increasingly complex and sophisticated to shorten the gap between users and digital systems. IA techniques can bring a lot of advantage in facing the issue of allergic reaction related to food allergies. Recognising and avoiding the foods on which it is based is an effective way to avoid an allergic reaction. To identify possible allergens, it is important to read the food labels carefully or to know what are the ingredients from which it is made. In this paper we present a system that exploits the techniques and tools of Artificial Intelligence to extract and analyse the ingredients of a recipe, and alert the user to the presence of possible allergens. The performed experimentation shows that the system can alert the user of allergens in Wikipedia Cookbok dataset recipes.
Incremental Weighted Linear Discriminant Analysis for Face Recognition

Face recognition has been a popular research area with several real-world applications. Linear discriminant analysis (LDA) is a well-known method for face recognition in literature. However, one of the requirements of LDA is the availability of all data samples available before training. In this paper, we have proposed a novel variant of LDA method based on incremental learning and is called incremental weighted linear discriminant analysis (IWLDA). In IWLDA, a weighted pairwise Fischer criterion is suggested to efficiently separate all class data homogeneously in the transformed subspace. IWLDA is developed in such a manner that the distance between nearby classes is increased and simultaneously the distance between farther classes is reduced and the overall distance is preserved. This results in improved classification accuracy. Experimental results on 5 publicly available datasets, viz. AR, CACD, YaleB, FERET, and ORL show that the proposed method outperforms the popular methods, i.e., principal component analysis (PCA), LDA, incremental principal component analysis (IPCA), and incremental linear discriminant analysis (ILDA) on all the datasets in terms of K-fold (\(K = 2\), 3, 4, 5) cross-validation. Further, it is also found that the training time of IWLDA is better than the batch methods, i.e., PCA and LDA.
Classification of Static Signature Based on Distance Measure Using Feature Selection

In this paper, eigenvector-based moments are proposed for offline signature validation. Here, principal component analysis (PCA) and linear discriminant analysis (LDA) techniques are used for dimension reduction and generated eigenvector which is calculated using Euclidean distance. It measured the distance between two vectors having an equal size in 2-D space. A newly suggested approach to generate the eigenvector from training and testing samples of signatures, which is calculated through Euclidean distance as a classifier. In which, it has shown high verification accuracy of 91.07% on the MCYT-75 corpus and GPDS synthetic signature database.
PageRank for Billion-Scale Networks in RDBMS

Data processing for Big Data plays a vital role for decision-makers in organizations and government, enhances the user experience, and provides quality results in prediction analysis. However, many modern data processing solutions make a significant investment in hardware and maintenance costs, such as Hadoop and Spark, often neglecting the well established and widely used relational database management systems (RDBMS’s). PageRank is vital in Google Search and social networks to determine how to sort search results and how influential a person is in a social group. PageRank is an iterative algorithm which imposes challenges when implementing it over large graphs which are becoming the norm with the current volume of data processed everyday from social networks, IOT, and web content. In this paper we study computing PageRank using RDBMS for very large graphs using a consumer-grade server and compare the results to a dedicated graph database .
LWT-DCT based Image Watermarking Scheme using Normalized SVD

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.
Two-stage encoding Extractive Summarization

Pre-trained language model can express the semantics of word or text span, is widely applied in many NLP tasks, and text summarization is no exception. It is created using fine-tuning or feature-based method on pre-training model. Since Bidirectional Encoder Representations from Transformers (BERT; Devlin et al. 2019), many works model text summarization based on BERT, and fine tune all the parameters end-to-end. Notably, multiple research proposed different strategies to create enhanced versions of BERT further, which achieve the state-of-the-art performance in many NLP tasks. In this paper, we explore the potential of multiple versions of BERT to handle text summarization. We present a two-stage encoder model (TSEM) for extractive summarization. The first stage applies A Lite BERT (ALBERT; Lan et al. 2019) to secure sentence-level embedding, identify valuable content based on A Lite BERT (ALBERT; Lan et al. 2019). The second stage proposes a new strategy to fine-tune BERT deriving meaningful document embedding, then select the best-matched combination of important sentences with source document to compose summarization. Experimental result on the CNN/Daily Mail dataset demonstrates that our model is competitive with the state-of-the-art result.
Modern Technologies For Implementation Of Individual Educational Trajectories In Higher Education

In the article the problem of introduction and implementation of individual educational trajectories in modern Russian higher educational institutions which is directed to differentiation of an educational product is considered. The authors notice that individualization of the educational process is one of the leading principles of higher education renewal, it is related to the problem of increasing competitiveness of Russian institutions of higher education. However, there is a sustainable lack of scientific knowledge about methods, forms and practices which would allow universities and institutes to construct an adapted education individualization model. Taking Yaroslav-the-Wise Novgorod State University as an example of implementing a project-oriented model, the authors consider a number of modern technologies which allow implementing individual educational trajectories comprehensively. Based on a complementary approach, and guided by a systemic method, the authors analyze three groups of technologies: a) technologies of project activities, b) technologies of educational process support, c) technologies of education digital transformation. One thesis of the work is related to the authors’ statement that the introduction of individual educational trajectories must not be limited by the opportunity for the student to select academic disciplines. As a result of introducing the technologies of individual educational trajectory in the university, a personality capable to solve non-standard problems, think creatively, and adapt to a quickly changing environment efficiently and flexibly is supposed to be formed.
Deep and Shallow Feature Fusion and Recognition of Recording Devices Based on Attention Mechanism

In the device source identification task, there is a lot of redundant information in the traditional MFCC, GSV and i-vector features, which affects the accuracy of device source identification. In this paper, in order to extract the key information from the device source, we propose a multi-feature fusion attention mechanism network to achieve improved accuracy. First, the multi-feature fusion attention mechanism network we proposed directly compresses the MFCC, GSV and i-vector features that characterize the source of the device using convolutional neural networks, reducing the dimension and computation of the feature vector and improving the feature vector Representativeness Second, we use a feature attention mechanism to adaptively rescale feature characteristics by considering the interdependence between features. Finally, we also use a combination of deep learning and machine learning. The back-end uses SVM models for classification. Extensive experiments show that our proposed method achieves better accuracy in device source recognition tasks.
Analysis of Predicting Trust in Complex Online Social Networks

Abstract Humans are utilizing one of the biggest networks of social communication by the web, the development of internet gives a way to the rise of the Online social network that assists users to communicate and accommodate between the users, thus sharing and conveying their emotions and information in an online social network. As users in the online social network are growing and performing several activities, they want trusted users or sources. Trusted users or source of information is one of the primary requirements due to security and privacy of the information. Finding future trusted user is called trust prediction. Several tools are available for predicting trust between users in an online social network. The main aim of trust prediction is to find the trust association between the users in the online social network. In the following study, researchers have at reviewed the diverse trusted-link prediction techniques and evaluated them on the basis of the provided standard dataset provided to us