Reviewerly/exHarmony huggingface dataset:
Comparison of Colorectal Cancer Classification between K-Nearest Neighbors (K-NN) and Neural Network

Abstract Machine learning is one of the technologies used in medicine. Machine learning can help detect various kinds of problems in the medical field and enables a process to be faster and more efficient. Cancer is one of the most dangerous diseases in the world. Machine learning is widely used in bioinformatics and particularly in cancer diagnosis. One of the most popular methods is K-nearest neighbors (K-NN) and Neural Network. There are supervised learning methods. Using K-NN, the quality of the results depends largely on the distance and the value of the parameter “k” which represents the number of the nearest neighbors. This research is explains the classification of colorectal cancer by using K-NN with different k values and Neural Network Classification. Our work will be performed on the Colorectal Cancer dataset obtained by the Al-Islam Hospital, Bandung, Indonesia and it consists of benign cases 163 and malignant cases 47 samples. Thus, the final result indicates better performance for K-nearest neighbors’ accuracy is 0.786 in K-parameter equal to 7, 9, 11 has the same accuracy with 60% data training and Neural Network reached 0.904 with 90% of data training.
Self attended stack pointer networks for learning long term dependencies

© 2020 The Authors. Published by ACL. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://aclanthology.org/2020.icon-main.12
Aplicación móvil para el seguimiento de los cursos de la plataforma de Ambiente Virtual de Aprendizaje

Currently, mobile applications have increased their use in all areas of daily life. Among other things, the above is due to the ease in the exchange of information. The Institute Technologic de Oaxaca proposes a mobile application that complements the Virtual Environment Platform for Professor Training. This application allows the connection with the platform, enter the course where the user is registered; and in this way, the user receives notifications of important dates such as dates of delivery of tasks and activities. The above is possible, since the application links directly to the Google calendar. With these functions, as an extension of the virtual platform, it facilitates the course participant to develop the activities in a timely manner. The application is developed based on incremental model.
An Approach to Securely Store Electronic Health Record(EHR) Using Blockchain with Proxy Re-Encryption and Behavioral Analysis

With the developmentKiran Dash, Kajal of the Internet of Things (IoT) and information communication technology (ICT),Nayak, Biswojit the traditional healthcare system changes to real-time monitoring of patients using smart devices.Kumar Mohanta, Bhabendu These smart devices are capable of collecting vital information and communicating with other devices. The smart devices are more vulnerable to the attackers because of resource constraint devices and connected through wired or wireless. The security and privacy issues like data integrity, availability, and storage, etc., are very important to make the smart healthcare system secure. In this paper, we proposed architecture using blockchain technology to secure data sharing In the smart healthcare system, proposed proxy re-encryption and behavioral analysis techniques make data communication and storage securely. The designed architecture makes vital information tamper-proof and preserves privacy for the data of the patient. Behavioral analysis is about the detailed security analysis which gives security for privacy and stops from manipulating the data.
Big Data Stream Discretization Using ChiMerge Algorithm

In this paper, we propose a new approach of the ChiMerge algorithm. We have added a new layer to remediate the threshold limitation issue and process the incoming data in real-time with a minimal latency. Our empirical results show that ChiMerge stream discretization helps to improve the subsequent pre-processing and algorithm execution time by 3.5 $$\times $$ compared to batch processing, with a slightly different accuracy rate.
An investigation on the current situation of information-based learning ability of primary and secondary school students based on ISTE student standard

This paper aims at six aspects of ISTE's standards for students'IT competence, questionnaire survey was conducted among students from 9 urban schools and 4 Township schools. Researcher studies the current situation of the environment, information-based learning ability and difference analysis, and draws a conclusion. The comprehensive indicators of information-based learning ability are constantly improving, the ability to diagnose and solve problems needs to be improved, and the education of network legal norms needs to be further strengthened; the investment of Township Education Funds should be increased, and the construction of township information infrastructure and teacher training should be strengthened; the improvement of students'information-based learning ability also needs the company to develop better learning products, as well as the understanding of schools and parents, inclusion and support.
Clustering of Writers and Cluster Dependent Classifier Selection for Offline Signature Verification

Identity establishment on handwritten signature is a promising research area in biometrics. This work is based on an approach for verification of offline signatures through the recommendation of classifiers for each group of likelihood writers. Initially, a single representative sample is created for each writer which exhibits maximum similarity with all other samples of the same writer. Once the representative signatures are created, we cluster them through k-means clustering to find groups of likelihood writers. Further, features most relevant for a writer are selected through computationally simple feature selection criteria which results in selection of suitable features for each writer. A suitable classifier is selected from a pool of classifiers based on the EER estimated with each of the classifier through k-fold cross-validation. We recommend same classifier for all writers of a particular cluster which yields lowest EER for majority of writers in that cluster. The genuineness of the test signature of a writer is established using the set of features and the classifier selected for the cluster for which the claimed writer is a member. The applicability of the proposed approach is validated through extensive experiments on two benchmarking datasets namely MCYT-75 and CEDAR. Comparison of the obtained results is also brought out against several other existing contemporary methods.
Fast Road Sign Detection and Recognition Using Colour-Based Thresholding

Automation of road sign detection and recognition is an important task in the context of applications like self-driving cars. Current popular, state of the art detectors, employing Deep Neural Networks (DNN) are very accurate but at the same time, very complex and have a very high processing time which might not be desirable for real-time applications in autonomous vehicles. In this paper, we present a road sign detection cum recognition pipeline, which exhibits the potential to achieve a considerable speed-up over the DNN based detection algorithms with a relatively small reduction in accuracy. The purpose of the detector is to capture as many road signs as possible in the least possible time. We also propose several techniques at the recognition stage to improve the performance of the pipeline. Comparison has been made to various state-of-the-art DNN based detector pipelines. The proposed pipeline is the fastest amongst all the detection-cum-recognition pipelines with an average processing time of 0.103 secs. per frame. The best F-score achieved by the pipeline is 0.87377. In comparison to this Faster R-CNN achieved the best F-Score of 0.9474 but with an average processing time of 17.664 secs. per frame.
Universal approximation on the hypersphere

The approximation properties of finite mixtures of location-scale distributions on Euclidean space have been well studied. It has been shown that mixtures of location-scale distributions can approximate arbitrary probability density function up to any desired level of accuracy provided the number of mixture components is sufficiently large. However, analogous results are not available for probability density functions defined on the unit hypersphere. The von-Mises-Fisher distribution, defined on the unit hypersphere Sm in Rm+1, plays the central role in directional statistics. We prove that any continuous probability density function on Sm can be approximated to arbitrary degrees of accuracy in sup norm by a finite mixture of von-Mises-Fisher distributions. Our proof strategy and result are also useful in studying the approximation properties of other finite mixtures of directional distributions.
Hybrid Movie Recommender System - A Proposed Model

Recommendation systems are intelligent search systems that predict the preferred information and provide suggestions to users. Traditional ways mainly are of content filtering and collaborative approach. However, these approaches show some demerits like the data sparsity, cold start, scalability, etc. Thus to tackle these demerits, the paper focuses on a system based on a hybrid approach that integrates the approaches of content-based and collaborative, employed with a singular value prediction algorithm. In addition to this, a systematic study and a proposed model is presented along with the machine learning algorithm. The study shows that the cosine similarity indexing, ranking, and the SVD algorithm can achieve better research objectives. The main idea is to investigate the present trends and responses of users. Promising validation results are produced by the experiments done over the public database.
Too Close for Comfort: Morasses of (Anti-) Censorship in the Era of CDNs

Abstract Recent research claims that “powerful” nation-states may be hegemonic over significant web traffic of “underserved” nations ( e.g., Brazil and India). Such traffic may be surveilled when transiting (or ending in) these powerful nations. On the other hand, content distribution networks (CDNs) are designed to bring web content closer to end-users. Thus it is natural to ask whether CDNs have led to the localization of Internet traffic within the country’s boundary, challenging the notion of nation-state hegemony. Further, such traffic localization may inadvertently enhance a country’s ability to coerce content providers to censor (or monitor) access within its boundary. On top of that, the obvious solution, i.e., anti-censorship approaches, may sadly face a new dilemma. Traditional ones, relying on proxies, are easily discoverable. Whereas newer ones ( e.g., Decoy Routing, Cache-Browser, Domain Fronting and CovertCast etc. ) might not work as they require accessing web content hosted outside the censors’ boundary. We thus quantitatively analyzed the impact of web content localization on various anti-censorship systems. Such analysis requires geolocating the websites. Thus we adapted a multilateration method, Constraint Based Geolocation (CBG), with additional heuristics. We call it as Region Specific CBG (R-CBG) . In more than 89% cases, R-CBG correctly classifies hosts as inside (or outside) w.r.t. a nation. Our empirical study, involving five countries, shows that the majority (61%−92%) of popular country-specific websites are hosted within a client’s own country. Further, additional heuristics classify the majority of them to be on CDNs.
Image Segmentation based on Geodesic Distance Combined with Region and Edge Gradient

In this paper, data points are uesd to represent vertices of a graph connected by weighted edges signifying similarity based on distance. The goal of this work is to accurately achieve image segmentation using both edge and region information on the basis of superpixels. In order to combine these clues in an optimal way, we use geodesic distance and formulate features that respond to characteristic changes in brightness, color, texture and gradient associated with adjacent superpixels. To determine whether a superpixel boundary is a segmentation, the edge gradient and the local density are used to overcome some shortcomings of the traditional DP algorithm. Experiments are conducted on Berkeley Segmentation Database and the result of segmented images verify the efficiency of our approach by comparison with other methods.
PERSONAL KNOWLEDGE VERSUS DISTANCE LEARNING

The modern educational process is focused on the training of a specialist who is able to think critically and creatively solve problematic situations, constantly raising the level of his education. Education as the most important social institution reacts sharply to the processes taking place in society. Actively developing distance learning is quite popular today, but there are different kinds of questions related to its implementation and the significance of a personal example, personal contacts in the learning process is one of the questions that is analyzed in this article. Would the distance learning be complete if it was deprived of subjectivity by definition? The educational system is understood to be a tradition in the article. The authors stress two types of traditions: verbalized and non-verbalized. The specificity of pedagogical activity does not allow many of its significant components to be explicated. The attention is focused on non-verbalized traditions existing in the form of implicit knowledge. Specific examples (V.I. Vernadsky, V.V. Dokuchaev, P.V. Ototsky) reveal the necessity of personal knowledge in the educational process. The article concludes that personal communication between the lecturer and the student is necessary as a conscious movement against deindividuation in modern society.
The design of electronic book for batik learning

Abstract This study aims to design an electronic book (e-book) used in batik learning in a Vocational High School. The design of the e-book is made on the web based application platform. The design of the e-book applies the Software Development Life Cycle (SDLC) method. The interaction model developed in the application is a user interface in the form of a frontend. The e-book User Interface in the form of a Codeigniter framework is based on the Hypertext Pre-processor (PHP). All content and databases are stored in the server. The results of the design include digital application of borrow-read-return procedures. In content, material about West Java Batik is accessible in three features, namely the pages of digital book room, audio book room, and video book room. The consequences of designing this e-book indicate the need for adequate internet network availability and the need for special administrator to constantly update the contents.
Predicting User Engagement Status for Online Evaluation of Intelligent Assistants

Evaluation of intelligent assistants in large-scale and online settings remains an open challenge. User behavior based online evaluation metrics have demonstrated great effectiveness for monitoring large-scale web search and recommender systems. Therefore, we consider predicting user engagement status as the very first and critical step to online evaluation for intelligent assistants. In this work, we first propose a novel framework for classifying user engagement status into four categories – fulfillment, continuation, reformulation and abandonment. We then demonstrate how to design simple but indicative metrics based on the framework to quantify user engagement. We also aim for automating user engagement prediction with machine learning methods. We compare various models and features for predicting engagement status using four real-world datasets. We conduct detailed analyses on features and failure cases to discuss the performance of current models as well as potential challenges.( $$^1$$ Resources used in this study can be found at https://github.com/memray/dialog-engagement-prediction. )
The Use of Stochastic Models in the Analysis of Vast English Literary Data Corpora

In the stylometric analysis of literary works, there are primarily specific aims, for example, at resolving the authorship, the period of the work, style, motif and purpose. These tasks, in fact, can be placed in a machine learning framework, where the quantitative features of known passages are initially learned and analyzed, and then compared with the corresponding metrics in disputable or doubtful passages. While in the past, relative laborious manual approaches relying on the human judgment of selected literary experts are mainly employed, they have shown to error-prone since human experts are unable to systematically process and methodically integrate the varied features of vast volumes of literary data. By combining a big data approach with the application of sophisticated stochastic models, more efficient, dependable and defensible decisions can be made. In this paper, we introduce two models for the quantitative analysis of the characteristics of literary work. The first is a Bernoulli model, which assumes the underlying quantitative features that are independent of each other, and the second is a Markov model, which is particularly appropriate for the analysis of poetic styles. We derive probabilistic measures of passage characteristics, and provide their illustrations in literary works in the English language. Experiments are performed the results of which corroborate the approach.
Single-mode multiphoton polarization states under random Pauli noises

We investigate behaviors of single-mode multiphoton polarization states under lossless random Pauli noises, where ``single-mode'' means that all the photons are in the same spatiotemporal mode and cannot be distinguished by any degree of freedom. We characterize the different decoherence effects of multiphoton and single-photon polarization states via the normalized linear entropy. Our results show that, contrary to single-photon states, multiphoton states cannot always evolve to the maximally mixed states. In particular, decoherence-free states and subspaces are found for the random Pauli noises. We reveal that the distinct behaviors result from the bosonic bunching effect and permutation symmetry, and hence our study may be generalized to other photon degrees of freedom and even some other bosonic systems.
Determination of Angstrom Coefficients for Masvingo and Makoholi Stations, Zimbabwe

Coefficients of the first order and second order Angstrom model were obtained for Masvingo and Makoholi stations in Zimbabwe. Long term average ground measurements of sunshine hours and global horizontal solar radiation from the meteorological services were used as input data. Monthly average values of the clearness index, and the fraction of sunshine hours, were determined. Regression analysis was used to obtain the first and second order coefficients of the Angstrom model for the two locations. The estimates from the developed models were tested against measured values and results obtained were in agreement to within +/- 5%. The results also showed that the models performed better with these coefficients compared with coefficients suggested in previous researches. Also the first order model was found to perform better than the second order model for Makoholi station while the second order model works well for Masvingo.
User Affective Experience into a Scope of Conversational Artificial Intelligence

The goal of the following research is to know the User experience during the interaction into a scope of Conversational Artificial Intelligence, having “Max” the virtual assistant from Universidad Técnica Particular de Loja (UTPL) as the evaluation interface. The assessment is done through virtual form with the presence of 45 members from the university community (15 students, 15 parents and 15 external). Previously to the interaction a kind of formative initial test is applied next to the Recognition of perceived emotions, the mobile application EmoApp-Pro (EMODIANA) and the FaceApi from Microsoft Azure. The research has a mixed approach, with a concurrent and quasi-experimental design of transactional type and a descriptive scope. The obtained results show that user’s affective experience is negative. Therefore, it is concluded that the must be constant feedback to the data base managed by the UTPL, precision in the addressing of answers and a mayor campaign to the society has to be done. Due to the sanitary emergency we face nowadays because of COVID-19 it will be very helpful doing academic processes that cannot be done in personal manner.
Discriminative semantic transitive consistency for cross-modal learning

Cross-modal retrieval is generally performed by projecting and aligning the data from two different modalities onto a shared representation space. This shared space often also acts as a bridge for translating the modalities. We address the problem of learning such representation space by proposing and exploiting the property of Discriminative Semantic Transitive Consistency—ensuring that the data points are correctly classified even after being transferred to the other modality. Along with semantic transitive consistency, we also enforce the traditional distance minimizing constraint which makes the projections of the corresponding data points from both the modalities to come closer in the representation space. We analyze and compare the contribution of both the loss terms and their interaction, for the task. In addition, we incorporate semantic cycle-consistency for each of the modality. We empirically demonstrate better performance owing to the different components with clear ablation studies. We also provide qualitative results to support the proposals.
RICA-MD: A Refined ICA Algorithm for Motion Detection

With the rapid development of various computing technologies, the constraints of data processing capabilities gradually disappeared, and more data can be simultaneously processed to obtain better performance compared to conventional methods. As a standard statistical analysis method that has been widely used in many fields, Independent Component Analysis (ICA) provides a new way for motion detection by extracting the foreground without precisely modeling the background. However, most existing ICA-based motion detection algorithms use only two-channel data for source separation and simply generate the observation vectors by decomposing and reconstructing the images by row, hence they cannot obtain an integrated and accurate shape of the moving objects in complex scenes. In this article, we propose a refined ICA algorithm for motion detection (RICA-MD), which fuses a larger number of channels than conventional ICA-based motion detection algorithms to provide more effective information for foreground extraction. Meanwhile, we propose four novel methods for generating observation vectors to further cover the diverse motion styles of the moving objects. These improvements enable RICA-MD to effectively deal with slowly moving objects, which are difficult to detect using conventional methods. Our quantitative evaluation in multiple scenes shows that our proposed method is able to achieve a better performance at an acceptable cost of false alarms.