Reviewerly/exHarmony huggingface dataset:
Analysis of Machine Learning Techniques for Breast Cancer Prediction

The most frequently happening cancer among Indian women is breast cancer, which is the second most exposed cancer in the world. Here is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. [2] Breast cancer is the second most severe cancer among all of the cancers already unveiled. A machine learning technique discovers illness which helps clinical staffs in sickness analysis and offers dependable, powerful, and quick reaction just as diminishes the danger of death. In this paper, we look at five administered AI methods named Support vector machine (SVM), K-closest neighbours, irregular woodlands, fake/ Artificial neural organizations (ANNs). The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value. Furthermore, these strategies were evaluated on exactness review region under bend and beneficiary working trademark bend. At last in this paper we analysed some of different papers to find how they are predicted and what are all the techniques they were used and finally we study the complete research of machine learning techniques for breast cancer.
New Bounds For Distributed Mean Estimation and Variance Reduction

We consider the problem of distributed mean estimation (DME), in which n machines are each given a local d-dimensional vector xv∈Rd, and must cooperate to estimate the mean of their inputs μ=1n∑v=1nxv, while minimizing total communication cost. DME is a fundamental construct in distributed machine learning, and there has been considerable work on variants of this problem, especially in the context of distributed variance reduction for stochastic gradients in parallel SGD. Previous work typically assumes an upper bound on the norm of the input vectors, and achieves an error bound in terms of this norm. However, in many real applications, the input vectors are concentrated around the correct output μ, but μ itself has large norm. In such cases, previous output error bounds perform poorly. In this paper, we show that output error bounds need not depend on input norm. We provide a method of quantization which allows distributed mean estimation to be performed with solution quality dependent only on the distance between inputs, not on input norm, and show an analogous result for distributed variance reduction. The technique is based on a new connection with lattice theory. We also provide lower bounds showing that the communication to error trade-off of our algorithms is asymptotically optimal. As the lattices achieving optimal bounds under l2-norm can be computationally impractical, we also present an extension which leverages easy-to-use cubic lattices, and is loose only up to a logarithmic factor in d. We show experimentally that our method yields practical improvements for common applications, relative to prior approaches.
3D-GMNet: Single-View 3D Shape Recovery as A Gaussian Mixture.

In this paper, we introduce 3D-GMNet, a deep neural network for 3D object shape reconstruction from a single image. As the name suggests, 3D-GMNet recovers 3D shape as a Gaussian mixture. In contrast to voxels, point clouds, or meshes, a Gaussian mixture representation provides an analytical expression with a small memory footprint while accurately representing the target 3D shape. At the same time, it offers a number of additional advantages including instant pose estimation and controllable level-of-detail reconstruction, while also enabling interpretation as a point cloud, volume, and a mesh model. We train 3D-GMNet end-to-end with single input images and corresponding 3D models by introducing two novel loss functions, a 3D Gaussian mixture loss and a 2D multi-view loss, which collectively enable accurate shape reconstruction as kernel density estimation. We thoroughly evaluate the effectiveness of 3D-GMNet with synthetic and real images of objects. The results show accurate reconstruction with a compact representation that also realizes novel applications of single-image 3D reconstruction.
The Resistance to Label Noise in K-NN and DNN Depends on its Concentration.

We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise. We first show empirically that a DNN's prediction for a given test example depends on the labels of the training examples in its local neighborhood. This motivates us to derive a realizable analytic expression that approximates the multi-class K-NN classification error in the presence of label noise, which is of independent importance. We then suggest that the expression for K-NN may serve as a first-order approximation for the DNN error. Finally, we demonstrate empirically the proximity of the developed expression to the observed performance of K-NN and DNN classifiers. Our result may explain the already observed surprising resistance of DNN to some types of label noise. It also characterizes an important factor of it showing that the more concentrated the noise the greater is the degradation in performance.
A fast and precise three-dimensional measurement system based on multiple parallel line lasers

This paper conducts a trade-off between efficiency and accuracy of three-dimensional (3D) shape measurement based on the triangulation principle, and introduces a flying and precise 3D shape measurement method based on multiple parallel line lasers. Firstly, we establish the measurement model of the multiple parallel line lasers system, and introduce the concept that multiple base planes can help to deduce the unified formula of the measurement system and are used in simplifying the process of the calibration. Then, the constraint of the line spatial frequency, which maximizes the measurement efficiency while ensuring accuracy, is determined according to the height distribution of the object. Secondly, the simulation analyzing the variation of the systemic resolution quantitatively under the circumstance of a set of specific parameters is performed, which provides a fundamental thesis for option of the four system parameters. Thirdly, for the application of the precision measurement in the industrial field, additional profiles are acquired to improve the lateral resolution by applying a motor to scan the 3D surface. Finally, compared with the line laser, the experimental study shows that the present method of obtaining 41220 points per frame improves the measurement efficiency. Furthermore, the accuracy and the process of the calibration are advanced in comparison with the existing multiple-line laser and the structured light makes an accuracy better than 0.22 mm at a distance of 956.02 mm.
Milestone Developments in Quantum Information and No-Go Theorems

In this article we present milestone developments in the theory and application of quantum information from historical perspectives. The domain of quantum information is very promising to develop quantum computer, quantum communication and varieties of other applications of quantum technologies. We also give the light on experimental manifestations of major theoretical developments. In addition, we present important no-go theorems frequently used in quantum information along with ideas of their respective mathematical proofs.
EffiDiag: an Efficient Framework for Breast Cancer Diagnosis in Multi-Gigapixel Whole Slide Images

Breast cancer diagnosis in multi-gigapixel whole slide images (WSIs) is an important task that highly relevant to cancer grading and prognosis. In recent years, many computer-aided diagnosis methods were proposed and achieved promising performance. However, they mostly suffer from heavy computational burden that becomes a significant barrier to clinical practice. Efficient solutions are urgently demanded but still less studied. In this paper, we propose a novel framework named EffiDiag for a fast and lightweight breast cancer diagnosis. To this end, a loss-modified U-net is developed at first to enable a fast suspected cancer Region Of Interest (ROI) localization. Therefore the subsequent patch-based classification, which commonly executes at the finest magnification hundreds of thousands times per WSI for cancer identification, could be carried out on these ROIs only rather than the whole WSI for speedup. Meanwhile, a super-efficient convolutional neural network (CNN) is devised to optimize the classification speed and resource consumption per classification. Experiments on the Camelyonl6 benchmark demonstrate, by integrating the two contributions into a well-established approach, 47x inference acceleration is obtained with limited accuracy drop, yet with much less resource consumption even compared to popular lightweight networks.
Development of Risk Breakdown Structure for Online Learning Project during COVID-19 Crisis

This research aims to develop risk breakdown structure specifically for virtual learning projects in higher institution. Transition from physical face-to-face study into virtual learning practices during COVID-19 mitigation phase had been selected as main focus to construct risks identification. Opinions and experiences from 35 stakeholders in university level had been gathered and analyzed using triangulation and meta-language of risk statement methods, in order to maintain statements’ validity and quality based on project management’s standard. All registered risks were listed using universal risk breakdown structure format, which emphasized on three risk areas: internal, external, and technology, in order to categorize risk based on its sources and identify area with high exposure of risk. The result proposed 11 risk statements with highest exposure of risk on technology area. This result indicated that virtual learning organizers need to consider strategies and mitigation process in technology area, specifically on the infrastructure readiness, user capabilities, and communication gap inside the virtual environment.
Design of Garbage Classification System based on RFID

Abstract In order to solve the problem of municipal solid waste and improve the recycling rate of resources, an intelligent garbage classification system is designed. The reader can identify the electronic tag on the garbage bag, automatically judge and open the corresponding garbage bin cover, which can be recycled or non-recyclable, so as to achieve the effect of garbage classification. At the same time, through the built-in distance, temperature and humidity sensors in the dustbin, real-time monitoring of whether the garbage is full, whether there is fire, etc., and the detected information is transmitted to the background through the network, and the alarm will be given in case of abnormality. It can reduce the workload of property cleaning staff and prevent the occurrence of fire and the spread of bacteria.
Privacy‐preserving speaker verification system based on binary I‐vectors

Speaker verification is a key technology in many services and applications like smartphones and intelligent digital assistants. These applications usually require users to transmit their recordings, features, or models from their voices over untrusted public networks which stored and processed them on cloud-based infrastructure. Furthermore, the voice signal contains a great deal of the speaker's personal and private information which raises several privacy issues. Therefore, it is necessary to develop speaker verification systems that protect the user's voice against such threats. Herein, the cancellable biometric systems have been introduced as a privacy-preserving solution. A cancellable method for speaker verification systems is proposed using speaker i-vector embeddings. This method includes two stages: (i) i-vector binarisation and (ii) the protection of the binary i-vector with a shuffling scheme derived from a user-specific key. Privacy evaluation of this method according to the standards of biometric information protection (ISO/IEC 24745) shows that the proposed cancellable speaker verification system achieves the revocability, unlinkability, and irreversibility requirements. Moreover, the cancellable system improves biometric performance compared with the unprotected system and makes it resistant to different attack scenarios. Additionally, we demonstrate that this method can also operate to protect deep neural network speaker embeddings such as x-vectors.
New challenges in changing times: the digital educational support for financial literacy education

Globalization, internationalization and international regionalization are the leading trends and the key factors that determine radical shifts and systemic changes in the Russian Federation as well in the other countries to meet quite new perspectives, opportunities and resources for its further cultural, social and economic development in a way of innovations and knowledge society creation. The strategies of social and economic development of the Russian Federation are tightly connected with perspectives of global digitalization, and they have a great impact on the present-day education digital transformation. The article actualizes a problem of financial illiteracy educations and the ways of its improvement by means of digital educational support orientated to development financial competences among students. Quite new educational and pedagogical tools and technologies have recently been introduced in financial education (educational platforms, e-learning, online games, online courses, simulators, digital textbooks, etc.). Under their influence there is a rapid formation and development of new educational environment, virtual and digital. Financial literacy education is becoming more individualized under the influence of its digital transformation. The center of gravity in learning shifts from teaching (instruction) to student activity, therefore, teacher’s professional position in teaching and learning changes. Teaching becomes more virtualized and digitalized under the influence of the virtual and digital educational environment. The functions of digital educational support in financial literacy education are characterized.
Research on Seismic Signal Classification and Recognition Based on EEMD and CNN

The accurate classification of seismic signals is of great significance for updating clear earthquake catalog, early-warning the arrival of strong earthquake, and further researches in the field of seismology. This paper first preprocesses the seismic signal data of two types of events: earthquakes and explosions, and then uses the ensemble empirical mode decomposition (EEMD) method to decompose these preprocessed seismic data. A set of intrinsic mode functions being distributed from high-frequency to low-frequency are obtained from each seismic waveform, and then corresponding Hilbert spectrum is obtained by applying Hilbert transform to each of these intrinsic mode functions (IMFs). The image of Hilbert spectrum is scaled to a suitable and comparable size, by repeated trial-and-error experiments, to the size of 32*32 pixels, and then the 32*32 pixels gray-scale image is fed to a convolutional neural network(CNN) to classify the types of seismic signals. In this paper, 1674 waveforms of 54 earthquake events and 1509 waveforms of 63 explosion events are processed as above procedures. By five-fold cross-validation, 80% of the data is used as the training set, remaining 20% data as testing set. The highest classification accuracy rate of testing set is 94.98% with the average accuracy rate 94.41%,this results are significant higher than those of multi-layer perceptron(MLP) and Support Vector Machine(SVM), which implying stronger classification capability of the scheme unifying EEMD and CNN proposed in this paper.
A Novel Spatio-Temporal 3D Convolutional Encoder-Decoder Network for Dynamic Saliency Prediction

As human beings are living in an always changing environment, predicting saliency maps from dynamic visual stimulus is of importance for modeling human visual system. Compared with human behavior, recent models based on LSTM and 3DCNN are still not good enough due to the limitation in spatio-temporal feature representation. In this paper, a novel 3D convolutional encoder-decoder architecture is proposed for saliency prediction on dynamic scenes. The encoder consists of two subnetworks to extract both spatial and temporal features in parallel with intermediate fusion, respectively. The saliency map is produced in decoder by firstly enlarging features in spatial dimensions and then aggregating temporal information. Specially designed structures can transfer pooling indices from encoder to decoder, which helps the generation of location-aware saliency maps. The proposed network can be trained and inferred in an end-to-end manner. Experimental results on benchmark DHF1K show that the proposed model achieves the state-of-the-art performance on key metrics including both normalized scanpath saliency and Pearson's correlation coefficient.
Intelligent Processing Technology of Cross Media Intelligence Based on Deep Cognitive Neural Network and Big Data

This paper studies the cross-media intelligent processing technology based on deep neural cognitive network and big data technology. This paper uses data processing and DNN training algorithms to explore deep cognitive neural networks to study cross-intelligence intelligent processing. The algorithm is mainly based on the rating of the item and the type of the item, and based on the similarity of the item type to give recommendations. This paper conducts data processing by investigating users' item preference and user item ratings. The DNN training algorithm used in this article needs to build a model and determine the method. The model specifically includes an input layer, two hidden layers and an output layer. The data in the input layer of the model is user project preferences, the output layer of the model is user project ratings, the hidden layer adopts the Dropout strategy, and the activation function is ReLU. First, establish the correspondence between item types and user item scores for DNN training, then build a multi-class deep neural network model with a dropout strategy, and finally get the predicted score of the target item after model training and reverse coding. Compared with the collaborative filtering of neural network, the algorithm adopted in this paper does not need to rely on the scores of neighboring users, and only needs to generate recommendations based on the user's personal scores and item types, which is conducive to accelerating the speed of intelligence processing.
Empirical Convergence Theory of Harmony Search Algorithm for Box-Constrained Discrete Optimization of Convex Function

The harmony search (HS) algorithm is an evolutionary computation technique, which was inspired by music improvisation. So far, it has been applied to various scientific and engineering optimization problems including project scheduling, structural design, energy system operation, car lane detection, ecological conservation, model parameter calibration, portfolio management, banking fraud detection, law enforcement, disease spread modeling, cancer detection, astronomical observation, music composition, fine art appreciation, and sudoku puzzle solving. While there are many application-oriented papers, only few papers exist on how HS performs for finding optimal solutions. Thus, this preliminary study proposes a new approach to show how HS converges on an optimal solution under specific conditions. Here, we introduce a distance concept and prove the convergence based on the empirical probability. Moreover, a numerical example is provided to easily explain the theorem.
Multiplayer Physical and Virtual Reality Games for Team-based Manufacturing Simulation

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State-Temporal Compression in Reinforcement Learning With the Reward-Restricted Geodesic Metric

It is difficult to solve complex tasks that involve large state spaces and long-term decision processes by reinforcement learning (RL) algorithms. A common and promising method to address this challenge is to compress a large RL problem into a small one. Towards this goal, the compression should be state-temporal and optimality-preserving (i.e., the optimal policy of the compressed problem should correspond to that of the uncompressed problem). In this paper, we propose a reward-restricted geodesic (RRG) metric, which can be learned by a neural network, to perform state-temporal compression in RL. We prove that compression based on the RRG metric is approximately optimality-preserving for the raw RL problem endowed with temporally abstract actions. With this compression, we design an RRG metric-based reinforcement learning (RRG-RL) algorithm to solve complex tasks. Experiments in both discrete (2D Minecraft) and continuous (Doom) environments demonstrated the superiority of our method over existing RL approaches.
Microcalcification detection in mammography image using computer-aided detection based on convolutional neural network

Breast cancer is one of the most common cancer among women and the survival rate tends to be low when its stage found high when treated. To improve breast cancer survival, early detection is critical. There are two ways of detection for breast cancer: early diagnosis and screening. To make an accurate diagnosis in the early stage of breast cancer, the appearance of masses and micro-calcifications on the mammography image are two important indicators. It is time-consuming and challenging to identify micro-calcification from mammogram images by the human eye because of its size and appearance. Several Computer-Aided Detection (CADe) have been developed to support radiologists because the automatic detection of micro-calcification is important for diagnosis and the next recommended treatment. Most of the current CADe systems at this time started using Convolutional Neural Network (CNN) to implement the micro-calcification detection in mammograms and their quantitative results are very satisfying, the average level of accuracy is more than 90%. However, most of the methods used are image fragments from a complete image which are then included in the program. This research conducts an automated approach to detect the location of any micro-calcification in the mammogram images with the complete image and in a simple way. At first, the image preprocessing algorithms were applied to enhance the image quality. After that, the micro-calcification region was labeled using image segmentation based on the Radiologist's expertise. The positive label which contains micro-calcification pixels was taken to train with segmentation network. A total of 354 images from INbreast dataset were used in this research study. Finally, the trained network was utilized to detect the micro-calcification area automatically from the mammogram images. This process can help as an assistant to the radiologist for early diagnosis and increase the detection accuracy of the micro-calcification regions. The proposed system performance is measured according to the error values of Mean Squared Logarithmic Error (MSLE) as the technique to find out the difference between the values predicted by the proposed model and the actual values. The best MSLE loss value obtained was achieved in 0.05 with accuracy 0.95.