Reviewerly/exHarmony huggingface dataset:
Prediction of Breast Cancer Using Ensemble Learning

Among all types of cancers, breast cancer is the most crucial and fatal cancer particularly for women as it is considered the second leading cause for cancer death among women. When it's a question of survival, correct treatment is a vital requirement, and for proper treatment, the first requirement is to identify cancer accurately. Here, we are motivated to provide a highly reliant and consistent system for the prediction of breast cancer. In the proposed method, we have used ensemble learning for the desired accuracy. The ensemble voting system comprises a total of five machine learning (ML) classifiers which include Random Forest, Naive Bayes, SVM with two different kernels (rbf, polynomial), K-Nearest Neighbors and Decision Tree. We experimented on the Wisconsin Breast Cancer Dataset from UCI machine learning repository. We achieved a maximum testing accuracy of 99.28% and a maximum precision of 97.22% while using 5-fold cross-validation. The proposed system exhibited satisfying accuracy on the dataset and outperformed many of the prominent existing methods.
Dynamic News Recommendation with Hierarchical Attention Network

News recommendation is an effective information dissemination solution in modern society. In general, news articles can be modeled from multiple granularities: sentence-, element-and news-level. However, the first two levels have been largely ignored in existing methods and it is also unclear how such multi-granularity modeling can enhance news recommendation. In this paper, we propose a novel dynamic model for news recommendation. A unique perspective of our model is to discriminate the contributions of previously interacted contents for triggering the next news-reading, in sentence-, element-and news-level simultaneously. To this end, we design a hierarchical attention network of which the lower layer learns the impacts of sentences and elements, while the upper layer captures disparity of news. Moreover, we incorporate a time-decaying factor to reflect the dynamism, as well as convolution neural networks for learning sequential influence. Using three real-world datasets, we conduct extensive experiments to verify the superiority of our model, compared with several state-of-the-art approaches.
Exploring the Benefits and Barriers of Using Computational Notebooks for Collaborative Programming Assignments

Programming assignments in computer science courses are often processed in pairs or groups of students. While working together, students face several shortcomings in today's software: The lack of real-time collaboration capabilities, the setup time of the development environment, and the use of different devices or operating systems can hamper students when working together on assignments. Text processing platforms like Google Docs solve these problems for the writing process of prose text, and computational notebooks like Google Colaboratory for data analysis tasks. However, none of these platforms allows users to implement interactive applications. We deployed a web-based literate programming system for three months during an introductory course on application development to explore how collaborative programming practices unfold and how the structure of computational notebooks affect the development. During the course, pairs of students solved weekly programming assignments. We analyzed data from weekly questionnaires, three focus groups with students and teaching assistants, and keystroke-level log data to facilitate the understanding of the subtleties of collaborative programming with computational notebooks. Findings reveal that there are distinct collaboration patterns; the preferred collaboration pattern varied between pairs and even varied within pairs over the course of three months. Recognizing these distinct collaboration patterns can help to design future computational notebooks for collaborative programming assignments.
Attention After Attention: Reading Text in the Wild with Cross Attention

Recent methods mostly regarded scene text recognition as a sequence-to-sequence problem. These methods roughly transform the image into a feature sequence and use the algorithms for sequence-to-sequence problem like CTC or attention to decode the characters. However, text in images is distributed in a two-dimensional (2D) space and roughly converting the features of text into a feature sequence may introduce extra noise, especially if the text is irregular. In this paper, we propose a novel framework named cross attention network, which learns to attend to local features of a 2D feature map corresponding to individual characters. The network contains two 1D attention networks, which operates harmoniously in two directions. Thus, one of the attention modules vertically attends to the features corresponding to the whole text of 2D features and the other horizontal module selects the local features to decode individual characters. Extensive experiments are performed on various regular benchmarks, including SVT, ICDAR2003, ICDAR2013, and IIIT5K-Words, which demonstrate that the proposed model either outperforms or is comparable to all previous methods. Moreover, the model is evaluated on irregular benchmarks including SVT-Perspective, CUTE80 and ICDAR 2015. The performance on irregular benchmarks shows the robustness of our model.
A class of narrow-sense BCH codes over $$\mathbb {F}_q$$ of length $$\frac{q^m-1}{2}$$

BCH codes with efficient encoding and decoding algorithms have many applications in communications, cryptography and combinatorial design. This paper studies a class of linear codes of length $$ \frac{q^m-1}{2}$$ over $$\mathbb {F}_q$$ with special trace representation, where q is an odd prime power. With the help of the inner distributions of some subsets of association schemes of quadratic forms, we determine the weight enumerators of these codes. Determining some cyclotomic coset leaders $$\delta _i$$ of cyclotomic cosets modulo $$ \frac{q^m-1}{2}$$, we prove that narrow-sense BCH codes of length $$ \frac{q^m-1}{2}$$ with designed distance $$\delta _i=\frac{q^m-q^{m-1}}{2}-1-\frac{q^{ \lfloor \frac{m-3}{2} \rfloor +i}-1}{2}$$ have the corresponding trace representation, and have the minimal distance $$d=\delta _i$$ and the Bose distance $$d_B=\delta _i$$, where $$1\le i\le \lfloor \frac{m+11}{6} \rfloor $$.
YouTube Video Classification based on Title and Description Text

YouTube has a library of millions if not billions of videos and keeping a track of the types of videos for effective retrieval and use can be quite difficult. YouTube videos can be classified into different classes based on the title and descriptions of the videos. To classify so many videos, an effective scalable algorithm is required. This can be achieved by using a Random Forest Classifier along with Natural Language Processing techniques like Bag of Words, Word Stemming etc. This paper also discusses method to scrape YouTube videos using packages like selenium, requests and Beautiful Soup for videos and their metadata. At the end we discuss various evaluation metrics for Random Forest Classifiers.
A Novel Algorithm for Optimizing Selection of Cloud Instance Types in Multi-Cloud Environment

With the development of cloud computing, the cloud market is becoming more and more complicated. There are many cloud providers and different cloud instance types, which brings users some confusion when they select cloud instance types. In order to solve the cloud instance type selection problem in multi-cloud environment, a Cloud Instance Type Selection Algorithm based on Genetic Algorithm (CITSA-GA) is proposed. CITSA-GA mainly includes two-dimensional encoding with the constraint between adjacent genes, selection operation adopting the elite retention strategy and the roulette strategy, crossover operation using the first fit strategy, and mutation operation with mutation bounds. We perform some experiments to prove the effectiveness of the proposed CITSA-GA.
Path planning with Incremental Roadmap Update for Visibility-based Target Tracking

In this paper, we address the visibility-based target tracking problem in which a mobile observer moving along a p-route, which we define as a fixed path for target tracking, tries to keep a mobile target in its field-of-view. By drawing a connection to the watchman's route problem, we find a set of conditions that must be satisfied by the p-route. Then we propose a metric for tracking to estimate a sufficient speed for the observer given the geometry of the environment. We show that the problem of finding the p-route on which the observer requires minimum speed is computationally intractable. We present a technique to find a p-route on which the observer needs at most twice the minimum speed to track the intruder and a reactive motion strategy for the observer.
An Active Data Representation of Videos for Automatic Scoring of Oral Presentation Delivery Skills and Feedback Generation

Public speaking is an important skill, the acquisition of which requires dedicated and time consuming training. In recent years, researchers have started to investigate automatic methods to support public speaking skills training. These methods include assessment of the trainee's oral presentation delivery skills which may be accomplished through automatic understanding and processing of social and behavioural cues displayed by the presenter. In this study, we propose an automatic scoring system for presentation delivery skills using a novel active data representation method to automatically rate segments of a full video presentation. While most approaches have employed a two step strategy consisting of detecting multiple events followed by classification, which involve the annotation of data for building the different event detectors and generating a data representation based on their output for classification, our method does not require event detectors. The proposed data representation is generated unsupervised using low-level audiovisual descriptors and self-organizing mapping, and used for video classification. This representation is also used to analyse video segments within a full video presentation in terms of several characteristics of the presenter's performance. The audio representation provides the best prediction results for self-confidence and enthusiasm, posture and body language, structure and connection of ideas, and overall presentation delivery. The video data representation provides the best results for presentation of relevant information with good pronunciation, usage of language according to audience , and maintenance of adequate voice volume for the audience. The fusion of audio and video data provides the best results for eye contact. Applications of the method to provision of feedback to teachers and trainees are discussed.
HC-PSOGWO: Hybrid Crossover Oriented PSO and GWO based Co-Evolution for Global optimization

In order to introduce and maintain an optimum balance between exploration and exploitation in the search space, a hybrid crossover oriented Particle Swarm optimization(PSO) and Grey Wolf optimization(GWO) based co-evolution structure, abbreviated as HC-PSOGWO is proposed in this paper. The proposed HC-PSOGWO mainly focuses on better generalization, search procedure and diversification. Here both particles and wolves, as search agents, concurrently and independently explore the entire search space for optimal results. The learning procedure of GWO is also modified. Once, the exploration process is over, the evolved search agents from both the optimization techniques are crossed, they communicate among themselves, better agents are identified in the process and they are again allowed to modify the swarms targeted by the individual technique. The performance of the proposed technique is tested on 23 well known classical benchmark functions, one real world optimization problem and compared with several established optimization techniques. The experimental results and related analysis show that the proposed HC-PSOGWO is significantly better than other existing techniques.
Big Data with Decision Tree Induction

Big data mining is one of the major challenging research issues in the field of machine learning for data mining applications in this present digital era. Big data consists of 3V's: (1) volume - massive amount of data/too many bytes, (2) velocity - high speed streaming data/too high a rate, and (3) variety - data are coming from different sources/too many sources. Collecting and managing real-life big data is a difficult task, as big data is so big that we cannot keep all the data together in a single machine. Therefore, we need advanced relational database management systems with parallel computing to deal with big data. Knowledge mining from big data employing traditional machine learning and data mining techniques is a big issue and attract computational intelligent researcher in this area. In this paper, we have used the decision tree (DT) induction method for mining big data. Decision tree induction is one of the most preferable and well-known supervised learning technique, which is a top-down recursive divide and conquer algorithm and require little prior knowledge for constructing a classifier. The traditional DT algorithms like Iterative Dichotomiser 3 (ID3), C4.5 (a successor of ID3 algorithm), Classification and Regression Trees (CART) are generally built for mining relatively small datasets. So, we need a more scalable decision tree learning approach for mining big data. In this paper, we have engendered several trees employing two scalable decision tree algorithms: RainForest Tree and Bootstrapped Optimistic Algorithm for Tree construction (BOAT) using seven benchmark datasets from Keel Repository and UCI Machine Learning repository. We have compared the performance of RainForest and BOAT algorithms. Also, we have proposed a decision tree merging approach, as decision tree merging is a very complex and challenging task.
Multiple instance deep learning for weakly-supervised visual object tracking

Intelligently tracking objects with varied shapes, color, lighting conditions, and backgrounds is an extremely useful application in many HCI applications, such as human body motion capture, hand gesture recognition, and virtual reality (VR) games. However, accurately tracking different objects under uncontrolled environments is a tough challenge due to the possibly dynamic object parts, varied lighting conditions, and sophisticated backgrounds. In this work, we propose a novel semantically-aware object tracking framework, wherein the key is weakly-supervised learning paradigm that optimally transfers the video-level semantic tags into various regions. More specifically, give a set of training video clips, each of which is associated with multiple video-level semantic tags, we first propose a weakly-supervised learning algorithm to transfer the semantic tags into various video regions. The key is a MIL (Zhong et al., 2020) [1]-based manifold embedding algorithm that maps the entire video regions into a semantic space, wherein the video-level semantic tags are well encoded. Afterward, for each video region, we use the semantic feature combined with the appearance feature as its representation. We designed a multi-view learning algorithm to optimally fuse the above two types of features. Based on the fused feature, we learn a probabilistic Gaussian mixture model to predict the target probability of each candidate window, where the window with the maximal probability is output as the tracking result. Comprehensive comparative results on a challenging pedestrian tracking task as well as the human hand gesture recognition have demonstrated the effectiveness of our method. Moreover, visualized tracking results have shown that non-rigid objects with moderate occlusions can be well localized by our method.
An Improvement on QR Code Limit Angle Detection using Convolution Neural Network

The QR Code implementation has been used in various fields such as manufacturing, mining, and logistics, retail, health, life sciences, transportation, and office automation. The advantage is useful for sharing information and integrated with a browser to get easier detail information. QR Code detection requires the position of the distance and the right reading angle. Moreover, for robots and the visually impaired person will not be able to do easily without guidance. The low-resolution camera cannot detect QR Code accuracy and need closer distance to be able to scan the QR Code. This is the challenge for improving the performance QR Code detection on low-resolution camera. This work proposes an algorithm that increases the reading of the angle of QR codes and maximizes detection on low-resolution cameras. This detection involves the image processing and Convolutional Neural Network (CNN) algorithm. This algorithm is applied to recognize the different QR Code version. The proposed approach can detect QR Code from a wider Euler Angle. The results demonstrated that the proposed detection method can increase 40o angle detection from the low- resolution and 15o angle detection from the higher-resolution. The proposed approach reached the accuracy rate of over 90%. These results revealed that the proposed method has high potential to to detect QR Code from the wider perspective angle.
ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection

In this paper, we propose a novel data augmentation technique (ANDA) applied to the Salient Object Detection (SOD) context. Standard data augmentation techniques proposed in the literature, such as image cropping, rotation, flipping, and resizing, only generate variations of the existing examples, providing a limited generalization. Our method has the novelty of creating new images, by combining an object with a new background while retaining part of its salience in this new context; To do so, the ANDA technique relies on the linear combination between labeled salient objects and new backgrounds, generated by removing the original salient object in a process known as image inpainting. Our proposed technique allows for more precise control of the object's position and size while preserving background information. Aiming to evaluate our proposed method, we trained multiple deep neural networks and compared the effect that our technique has in each one. We also compared our method with other data augmentation techniques. Our findings show that depending on the network improvement can be up to 14.1% in the F-measure and decay of up to 2.6% in the Mean Absolute Error.
Towards projectional editing for model-based SPLs

Model-based software product lines (MBSPLs) are implemented using various variability mechanisms. These are commonly categorized whether they separate features virtually (e.g., using annotations) or physically (e.g., using modules). Each of these mechanisms comprises advantages and disadvantages regarding MBSPL development, maintenance and analysis. To date, MBSPL developers have to choose upfront which variability mechanism to use, and the chosen mechanism including its drawbacks is bound to the MBSPL's entire lifecycle. In contrast, projectional editing has recently shown very promising potential of making the development of classical SPLs (e.g., implemented in C/C++) more flexible. User-editable projections allow developers to switch fluidly between different variability mechanisms based upon a common internal representation known as variational abstract syntax tree.
ICU management based on big data

Purpose of review The availability of large datasets and computational power has prompted a revolution in Intensive Care. Data represent a great opportunity for clinical practice, benchmarking, and research. Machine learning algorithms can help predict events in a way the human brain can simply not process. This possibility comes with benefits and risks for the clinician, as finding associations does not mean proving causality. Recent findings Current applications of Data Science still focus on data documentation and visualization, and on basic rules to identify critical lab values. Recently, algorithms have been put in place for prediction of outcomes such as length of stay, mortality, and development of complications. These results have begun being implemented for more efficient allocation of resources and in benchmarking processes, to allow identification of successful practices and margins for improvement. In parallel, machine learning models are increasingly being applied in research to expand medical knowledge. Summary Data have always been part of the work of intensivists, but the current availability has not been completely exploited. The intensive care community has to embrace and guide the data science revolution in order to decline it in favor of patients’ care.
A New Method and Benchmark for Detecting Co-Saliency Within a Single Image

Recently, saliency detection in a single image and co-saliency detection in multiple images have drawn extensive research interest in the vision and multimedia communities. In this paper, we investigate a new problem of co-saliency detection within a single image, i.e., detecting within-image co-saliency. By identifying common saliency within an image, e.g., highlighting multiple occurrences of an object class with similar appearance, this work can benefit many important applications, such as the detection of objects of interest, more robust object recognition, reduction of information redundancy, and animation synthesis. We propose a new bottom-up method to address this problem. Specifically, a large number of object proposals are first detected from the image. Then we develop an optimization algorithm to derive a set of proposal groups, each of which contains multiple proposals showing good common saliency in the image. For each proposal group, we calculate a co-saliency map and then use a low-rank based algorithm to fuse the maps calculated from all the proposal groups for the final co-saliency map in the image. In the experiment, we collect a new benchmark dataset of 664 color images (two subsets) for within-image co-saliency detection. Experiment results show that the proposed method can better detect the within-image co-saliency than existing algorithms. The experimental results also show that the proposed method can be applied to detect the repetitive patterns in a single image and detect the co-saliency in multiple images.
A new iterative fuzzy clustering approach for incomplete data

AbstractAbstractClustering of data with incomplete information is a difficult task, which is being researched extensively. Several researchers used fuzzy clustering methods for clustering of such data. Since conventional fuzzy clustering uses Euclidean distance metric to derive the information between a cluster center and a data point, it suffers badly when the clusters are divergent in nature. This paper proposes a new iterative Mahalanobis distance based fuzzy clustering to capture the essence of non-spherical shape of data along with incorporation of an imputation technique. The proposed methodology is compared with several imputation based FCM approaches as well as various clustering approaches as suggested by Hathway and Bezdek for incomplete data. Numerical analysis on one synthetic data set and various real life data sets show that the proposed clustering method is significantly better than other imputation as well as non-imputation techniques for these data sets.Subject Classification: 62M10Keywords: FCM ClusteringImputationMissing dataRegressionInterpolation