Reviewerly/exHarmony huggingface dataset:
Survival Time Prediction of Breast Cancer Patients Using Feature Selection Algorithm Crystall

Breast cancer is one of main causes of death for women. Most of the existing survival analyses focus on the features' associations with whether the patients may survive five years or not. The personalized question remains largely unresolved about how long a breast cancer patient will live. This study aims to predict the patient-specific survival time of breast cancer patients. It formulates the personalized question into two machine learning problems. The first problem is the binary classification of whether a patient will live longer than five years or not. The second one is to build a regression model to predict the patient's survival time within five years. The methylome of a breast cancer patient is used for the prediction. A new algorithm Crystall is presented to find the methylomic features for this regression model. Our models perform well in the above two problems, and achieve the mean absolute error (MAE) of about 1 month for predicting how long a breast cancer patient will live within five years. The detected biomarker genes demonstrate close connections with breast cancers.
DESIGN AND ANALYSIS OF COLLABORATIVE FILTERING BASED RECOMMENDATION SYSTEM

Recommender Engine is a specific type of smart system that uses old user feedback on products and/or additional information to make useful product recommendations.This assumes a key job in a wide scope of utilization, including web-based shopping, e-business administrations, and social ecommerce networking.Collaborative sifting (CF) is the most well-known methodologies utilized for suggestion frameworks; however, CF experiences full cold start (CCS) issue where no appraising record is accessible and with Incomplete Cold Start (ICS) issues where there are just few rating records accessible for some new things or application clients.Therefore, the recommendation algorithms for collaborative filtering are useful and play a vital role in businesses to reach out to new users and promote their services and products.This paper introduces a new cooperative filtering recommendation algorithm based on dimensionality reduction called Singular Value Decomposition (SVD) used to cluster related users and reduce dimensionality.These method and concept are continuously being used and referred in order to attain an increased and enhanced accuracy over the present Netflix system.This paper is working with Netflix's prize dataset, we use the incremental SVD approach to predict movie ratings based on previous user preferences.Different experiments are conducted to see the effect of various parameters on the algorithm's performance.
COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR PURCHASING FORECAST

With the development of computer technologies and invention of internet, many concepts have entered our lives. With the starting of wide usage of globalized internet network, concept of machine learning has emerged in time for smarter management of data flow in big dimensions. In line with technological developments, all activities began to be carried to digital environment and as a result of this, concept of e-commerce has entered our lives. E-commerce is one of the areas where machine learning is used most widely. By examining product purchasing situations in accordance with data available at the enterprises, various researches have been made for selection of most appropriate model in order to predict future data. In the study it was mentioned about concepts of e-commerce and machine learning and by applying Logistic Regression, Naïve Bayes and Support Vector Machines being machine learning classification algorithms, it has been aimed to determine the model having best accuracy ratio. JEL codes: L81, C11, C38, C39, C53
Klasifikasi Penyakit Tanaman Padi Menggunakan Model Deep Learning Efficientnet B3 dengan Transfer Learning

The level of rice productivity is influenced by several inhibiting factors, for example disease attack in rice plants. The slow and inappropriate treatment of rice plant can make the crop failure so that rice production and farmers' income decrease. The symptoms of rice disease are difficult to distinguish, especially in severe symptoms. Collaboration with other fields, especially computer science, is needed to classify diseases automatically so that the farmers can take action for plant treatment and the spread of disease can be controlled quickly. The classification of diseases based on images requires the best features/characteristics so that the disease can be classified. In this research, Deep Learning method, especially Convolutional Neural Network with EfficientNet B3 architecture, can extract features very well. In this research, the classification of brown spot and bacterial leaf disease by applying EfficientNet B3 with transfer learning reached 79.53% accuracy and 0.012 loss/error.
A differentially private distributed data mining scheme with high efficiency for edge computing

Abstract A wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.
COTS: A Multipurpose RGB-D Dataset for Saliency and Image Manipulation Applications

Many modern computer vision systems include several modules that perform different processing operations packaged as a single pipeline architecture. This generally introduces a challenge in the evaluation process since most datasets provide evaluation data for just one of the operations. In this paper, we present an RGB-D dataset that was designed from first principles to cater for applications that involve salient object detection, segmentation, inpainting and blending techniques. This addresses a gap in the evaluation of image inpainting and blending applications that generally rely on subjective evaluation due to the lack of availability of comparative data. A set of experiments were carried out to demonstrate how the COTS dataset can be used to evaluate these different applications. This dataset includes a variety of scenes, where each scene is captured multiple times, each time adding a new object to the previous scene. This allows for a comparative analysis at pixel level in image inpainting and blending applications. Moreover, all objects were manually labeled in order to offer the possibility of salient object detection even in scenes that contain multiple objects. An online test with 1267 participants was also carried out, and this dataset also includes the click coordinates of users' selection for every image, introducing a user interaction dimension in the same RGB-D dataset. This dataset was also validated using state of the art techniques, obtaining an $F_\beta $ of 0.957 in salient object detection and a mean (Intersection over Union) IoU of 0.942 in Segmentation. Results demonstrate that the COTS dataset introduces novel possibilities for the evaluation of computer vision applications.
Overview of Image Semantic Segmentation Technology

With the continuous development of artificial intelligence technology, image semantic segmentation has increasingly become the focus of computer vision and artificial intelligence algorithms. Image semantic segmentation technology has been applied in remote sensing satellite images, agricultural products production, medical diagnosis and treatment and other fields, and these fields need very accurate and effective semantic segmentation technology. This paper summarizes the common semantic segmentation techniques, introduces the main algorithms of image semantic segmentation techniques, mainly explains the image semantic segmentation techniques based on traditional machine learning and image semantic segmentation techniques based on deep learning, expounds the structure of each method, summarizes the performance and effect of each method, and finally expounds our views on the future development direction and challenges of semantic segmentation.
Modelling for Ethical Concerns for Traceability in Time of Pandemic “Do no Harm” or “Better Safe than Sorry!”

We propose a service design for ethics framework that applies the four diamonds-of-context model for complex service design (4DocMod) framework to analyze, decompose, and interpret the main edicts of ethics (credibility, transferability, and validity) in data collection and use in public health complex service systems.We illustrate how different contexts of different actors can be accommodated ethically at the service design level.The paper explains the main artefacts of the 4DocMod framework (diamonds See, Recognize, Organize, Do) against community and individual ethics in several case studies related to the current COvID-19 pandemics facing the use of traceability technologies.The main contribution of the paper highlights how actions and goals in healthcare as a service ecosystem (H-SES) may have contexts, while contextual interpretation of activities constitutes the basis for ethical evaluation.
A New Topological Sorting Algorithm with Reduced Time Complexity

In the case of finding the topological ordering of a directed acyclic graph (DAG), kahn’s and Depth First Search (DFS) topological sorting algorithms are used. Both of these algorithms time complexity is O(|V| + |E|). Here a topological sorting algorithm is proposed that is completely new and it reduces the time complexity of the previous algorithms. By separating the vertices having outgoing edges and the vertices having no outgoing edges then removing outgoing edges step by step, we can find a topological ordering of any DAG. The time complexity after using the proposed algorithm reduces to $$ {\text{O}}\left( {\sum\nolimits_{i = 1}^{\left| v \right|} {\left| {\left( {NE} \right)_{i} } \right|} } \right) $$ for both average case and worst case but for best case it reduce to O(|V|), here |V| is the number of vertex and |(NE)| is the number of vertex contains at least one outgoing edge. This algorithm also can detect cycle in a graph. This algorithm cab be used for resolving dependencies, scheduling system, planning and in many graph algorithms.
Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers

In deep neural networks, the spectral norm of the Jacobian of a layer bounds the factor by which the norm of a signal changes during forward/backward propagation. Spectral norm regularizations have been shown to improve generalization, robustness and optimization of deep learning methods. Existing methods to compute the spectral norm of convolution layers either rely on heuristics that are efficient in computation but lack guarantees or are theoretically-sound but computationally expensive. In this work, we obtain the best of both worlds by deriving {\it four} provable upper bounds on the spectral norm of a standard 2D multi-channel convolution layer. These bounds are differentiable and can be computed efficiently during training with negligible overhead. One of these bounds is in fact the popular heuristic method of Miyato et al. (multiplied by a constant factor depending on filter sizes). Each of these four bounds can achieve the tightest gap depending on convolution filters. Thus, we propose to use the minimum of these four bounds as a tight, differentiable and efficient upper bound on the spectral norm of convolution layers. Moreover, our spectral bound is an effective regularizer and can be used to bound either the lipschitz constant or curvature values (eigenvalues of the Hessian) of neural networks. Through experiments on MNIST and CIFAR-10, we demonstrate the effectiveness of our spectral bound in improving generalization and robustness of deep networks.
COVID-19: Privacy and Confidentiality Issues with Contact Tracing Apps

Contact tracing has been a main topic of conversation in the COVID-19 pandemic. While implementation of app-based contact tracing can be beneficial, it raises concerns of privacy and confidentiality. To better understand how these issues were addressed, a qualitative study was conducted which analyzes the current status of contact tracing apps from Iceland, Italy, Germany, India, Singapore, Japan, and 4 states within the United States. The comparisons made amongst the contact tracing apps will be surveyed across numerous criteria. The results show contact tracing apps are able to assist in the COVID-19 caseloads by determining self-isolation periods. Future developments can change these apps into a tool for returning to normalcy that may require more user information disclosure, but limited protections of privacy and confidentiality issues have not been addressed at a worldwide level
Framework for e-Library Services to Support the e-Learning Environment in a Secondary School

This paper proposes an ICT-based framework that incorporates e-library services into the e-learning environment. There has been wide recognition that the integration of elibrary services within e-learning environments is lacking. The literature suggests that there is no formal collaboration between teachers, students and librarians in the course of the proposing, acquiring and utilising of e-library resources. The proposed framework defines different scenarios, roles and learning cycles that support different interactions within the e-learning process. The system is expected to provide greater opportunities to fulfil students’ needs with less time and effort.
A survey on group modeling strategies for recommender systems

E-commerce websites generate huge volumes of customer review data. In this era of bigdata, recommender systems have played a vital role in suggesting customized recommendations to the users. Recommender systems generally deal with user preferences. Increase in online group activities has fueled the need for group recommender system extremely. A group recommender system recommends one item (or more) to a group of users considered together (with some matching similarity). Group recommender systems are used in various domains like music, movies, travel, etc. Group recommendation uses the preference of their group members in order to recommend the items to a group of users. This chapter explores various group modeling strategies, their implementation methods, and modified variations. Some of the group modeling techniques are additive, least misery, Borda count, Copeland, most pleasure, most respected, mean after threshold, fairness, etc. All the modeling techniques are explained with suitable examples and solved problems.
Casa, Escuela y Ciudad: el cultivo del lenguaje en un mundo digital

This paper aims to show the importance of language and the challenges it faces in the digital revolution by considering the possibilities for cultivating it during adolescence in three fundamental spaces: Homes, Schools, and Cities. It starts by analysing the dual function of language as a means of both communication and representation, as how one receives one’s heritage and imagines and projects one’s future. It then considers the false dichotomy between digital competency and reading skills in light of the latest results from the Programme for International Student Assessment (PISA). Reading can no longer be understood just as the ability to enjoy dusty volumes of old classics languishing on library bookshelves. Rather, it can be defined as a set of strategies that enable personal development and participation in society. This extension of the concept inexorably involves the competence of acting creatively in digital environments. Unfortunately, these environments go largely unheeded at School, where they are relegated to the mundane world of play. At Home, however, digital discourse can be experienced as unfathomable, where the digital divide makes it a world apart. Therefore, collaboration between School and Home becomes vital for ensuring that adolescents’ immersion in the digital world leads to critical, responsible participation in citizenship.
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training

Recent advances by practitioners in the deep learning community have breathed new life into Locality Sensitive Hashing (LSH), using it to reduce memory and time bottlenecks in neural network (NN) training. However, while LSH has sub-linear guarantees for approximate near-neighbor search in theory, it is known to have inefficient query time in practice due to its use of random hash functions. Moreover, when model parameters are changing, LSH suffers from update overhead. This work is motivated by an observation that model parameters evolve slowly, such that the changes do not always require an LSH update to maintain performance. This phenomenon points to the potential for a reduction in update time and allows for a modified learnable version of data-dependent LSH to improve query time at a low cost. We use the above insights to build MONGOOSE, an end-to-end LSH framework for efficient NN training. In particular, MONGOOSE is equipped with a scheduling algorithm to adaptively perform LSH updates with provable guarantees and learnable hash functions to improve query efficiency. Empirically, we validate MONGOOSE on large-scale deep learning models for recommendation systems and language modeling. We find that it achieves up to 8% better accuracy compared to previous LSH approaches, with 6.5× speed-up and 6× reduction in memory usage.
Convolutional Auto-encoder Based Sky Image Prediction Model for Minutely Solar PV Power Forecasting

The precise minute time scale forecasting of an individual Photovoltaic power station output relies on accurate sky image prediction. To avoid the two deficiencies of traditional digital image processing technology (DIPT) in predicting sky images: relatively limited input spatiotemporal information and linear extrapolation of images, convolutional auto-encoder (CAE) based sky image prediction models are proposed according to the spatiotemporal feature extraction ability of 2D and 3D convolutional layers. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry (PIV) and Fourier phase correlation theory (FPCT) are introduced to build the benchmark models. The results show that the proposed models outperform the benchmark models under different scenarios.
1. State-of-the-art speaker recognition methods applied to speakers with dysarthria

Speech-based biometrics is one of the most effective ways for identity management and one of the preferred methods by users and companies given its flexibility, speed and reduced cost. Current state-of-the-art speaker recognition systems are known to be strongly dependent on the condition of the speech material provided as input and can be affected by unexpected variability presented during testing, such as environmental noise, changes in vocal effort or pathological speech due to speech and/or voice disorders. In this chapter, we are particularly interested in understanding the effects of dysarthric speech on automatic speaker identification performance. We explore several state-of-theart feature representations, including i-vectors, bottleneck neural-networkbased features, as well as a covariance-based feature representation. High-level features, such as i-vectors and covariance-based features, are built on top of four different low-level presentations of dysarthric/controlled speech signal. When evaluated on TORGO and NEMOURS databases, our best single system accuracy was 98.7%, thus outperforming results previously reported for these databases.
Bibliologos and library and information education

In the Decree of the President V. V. Putin of July 21, 2020 "On the national development goals of the Russian Federation for the period up to 2030", there are two national goals that are directly related to the book and library business: the education of a harmoniously developed and socially responsible personality and the implementation of digital transformation of key sectors of the economy and social spheres. The Russian Bibliologos (book mind) has been creatively and collectively created over millennia. Cognition of the essence of the Bibliologos is a priority task of book and library science and library and information education today. The article examines the biological and social prerequisites of book communication, the dynamics of the development of the classical Bibliologos and the scientific and pedagogical foundations of the formation of the non-classical Bibliologos of the XXI century. The Bibliologos is understood as a biologically and socially determined intelligent productive force mastered by people in the process of hominization (humanization). The following functional definition is proposed: Bibliologos is the collective mind of a historically stable community of people who own the book culture, write and read books and are directly involved in the production of the bibliosphere. The Classical Bibliosphere is defined as a supersystem of book-communication systems that ensures the reproduction, preservation and further development of the national book culture. The structure of the bibliosphere is formed by socio-cultural institutions (systems, or branches of the book business), namely: publishing, printing industry, book trade, librarianship, bibliography. Each institute includes practice, education, science, special communication, management bodies. The branch problems of the bibliosphere are studied by related, but relatively independent, scientific and practical studies (bibliography, library science, records management, book history, bibliopolistics, editsiology, etc.), and bibliology is engaged in general problems – a complex science (or a complex of sciences) about books and books, as well as general document theory, or documentology. In addition to branch institutes, the bodies of the bibliosphere are: bibliophile socio-cultural movement; non-profit and commercial founders and voluntary associations; government authorities and censorship. The participation of the Russian Bibliologos and the library school in the implementation of the human-creative project as a national target is envisaged. Particular attention is paid to the European experience of modeling a new type of person, the historical project "Memory for the Future", cyber-socialization of the society of the future, topical issues of non-classical library and information education and the synthesis of book culture and digital culture of the XXI century. It is concluded that national security and the well-being of future generations depend on the non-classical Bibliosphere, in particular on national libraries and school librarians.