Reviewerly/exHarmony huggingface dataset:
Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using Bayesian Networks

In solving real-world problems like changing healthcare-seeking behaviors, designing interventions to improve downstream outcomes requires an understanding of the causal links within the system. Causal Bayesian Networks (BN) have been proposed as one such powerful method. In real-world applications, however, confidence in the results of BNs are often moderate at best. This is due in part to the inability to validate against some ground truth, as the DAG is not available. This is especially problematic if the learned DAG conflicts with pre-existing domain doctrine. At the policy level, one must justify insights generated by such analysis, preferably accompanying them with uncertainty estimation. Here we propose a causal extension to the datasheet concept proposed by Gebru et al (2018) to include approximate BN performance expectations for any given dataset. To generate the results for a prototype Causal Datasheet, we constructed over 30,000 synthetic datasets with properties mirroring characteristics of real data. We then recorded the results given by state-of-the-art structure learning algorithms. These results were used to populate the Causal Datasheet, and recommendations were automatically generated dependent on expected performance. As a proof of concept, we used our Causal Datasheet Generation Tool (CDG-T) to assign expected performance expectations to a maternal health survey we conducted in Uttar Pradesh, India.
Multi-kernel Support Vector Data Description with boundary information

The One-Class Classification (OCC) exists in many real-world applications, such as novelty detection, outlier detection, facial verification, and anomaly detection. SVDD is an efficient method to solve the OCC problem. How to describe a hypersphere with minimized volume that encloses almost all the target class samples is the key point of the SVDD. However, the existing SVDD-based methods generally neglect the boundary information of samples in the construction of the classifier. To fully utilize the boundary information to guide the training process, this paper introduces Multi-Kernel Learning (MKL) into the traditional Support Vector Data Description (SVDD) based on the boundary information, proposes a novel method called MKL-SVDD. The proposed MKL-SVDD first determines the boundary samples based on the geometrical and statistical information, and assigns the special weight for the boundary samples to improve the effect of the boundary samples in the optimization process. Meanwhile, to enhance the feature expression capabilities, the proposed MKL-SVDD utilizes the location information of samples as the supervised signal to design the kernel weights for multiple kernels to obtain the optimal kernel combination. Extensive experiments on 11 UCI datasets and 7 KEEL datasets demonstrate the superiority of the MKL-SVDD over the other state-of-the-art methods. The Bayesian analysis of the experiment results theoretically prove that the MKL-SVDD is superior to other methods on UCI and KEEL datasets with 100% and over 96% probability respectively.
FPGA Realization of Spherical Chaotic System with Application in Image Transmission

This paper considers a three-dimensional nonlinear dynamical system capable of generating spherical attractors. The main activity is the realization of a spherical chaotic attractor on Intel and Xilinx FPGA boards, with a focus on implementation of a secure communication system. The first major contribution is the successful synchronization of two chaotic spherical systems, in VHDL program, in a master-slave topology using Hamiltonian forms. The synchronization errors show that the two spherical chaotic systems synchronize in a very short time after which the error signals become zero. The second major contribution is the FPGA realization of a spherical chaotic attractor-based secure communication system, which involves encrypting both grayscale and RGB images with chaos and diffusion key at the transmitting system, sending the encrypted image via the state variables, and reconstructing the encrypted image at the receiving system. The Intel Stratix III and Xilinx Artix-7 AC701 results are the same as those of MATLAB. The statistical analyses of the encrypted and received images show that the implemented system is very effective, as it reveals high degree of randomness in the encrypted images with the entropy test, and the obtained correlation coefficient, which is zero, removes relativity between the original and encrypted images. Finally, the transmission system fully recovers the original grayscale and RGB images without loss of information.
Machine Learning Techniques Applied to Electronic Healthcare Records to Predict Cancer Patient Survivability

Breast cancer (BCa) and prostate cancer (PCa) are the two most common types of cancer. Various factors play a role in these cancers, and discovering the most important ones might help patients live longer, better lives. This study aims to determine the variables that most affect patient survivability, and how the use of different machine learning algorithms can assist in such predictions. The AURIA database was used, which contains electronic healthcare records (EHRs) of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland. In total, there were 178 features for BCa and 143 for PCa. Six feature selection algorithms were used to obtain the 21 most important variables for BCa, and 19 for PCa. These features were then used to predict patient survivability by employing nine different machine learning algorithms. Seventy-five percent of the dataset was used to train the models and 25% for testing. Cross-validation was carried out using the StratifiedKfold technique to test the effectiveness of the machine learning models. The support vector machine classifier yielded the best ROC with an area under the curve (AUC) = 0.83, followed by the KNeighborsClassifier with AUC = 0.82 for the BCa dataset. The two algorithms that yielded the best results for PCa are the random forest classifier and KNeighborsClassifier, both with AUC = 0.82. This study shows that not all variables are decisive when predicting breast or prostate cancer patient survivability. By narrowing down the input variables, healthcare professionals were able to focus on the issues that most impact patients, and hence devise better, more individualized care plans.
Optical Charater Recognition using Tesseract and Classification

Optical Character Recognition (OCR) is a process or technology in which text within a digital image is recognized. It is mainly used for converting the transcribed, handwritten or any printed text to the text data that can be edited and reused. With rapid pace of technology, people want quicker, handy and reliable tools, which can fulfil their daily needs. With this moto we had gone forward and analyzed the existing tools and made up this WebApp, which provides seamless experience (No ads and easy-to-use), and great accuracy. While OCR technology was originally developed for recognizing the printed text, it can be used to recognize and verify handwritten text as well. The objective of this project is to allow automatic extraction of the information that a user wants from the paper document and using it wherever it is needed. This leads to reduction or sometimes eliminating the work of costly data entry. We also aimed to enable a way in which processing of the documents will lead to eliminate the human touches and therefore dramatically reducing the process time and the cost.
Fusion of latent categorical prediction and sequential prediction for session-based recommendation

Session-based recommendation is to predict the next item for an anonymous item sequence. Most of recent neural models have focused on how to learn sessions’ sequential representations based on the assumption that items can be projected into a single latent embedding space to describe their latent attributes. In this paper, we argue that an item can also be described by some latent categorical abstractions. To examine our argument, we first mine items’ latent categorical distributions via random walk on an item graph constructed from sessions. We design a new neural model which consists of two prediction modules: One is to learn a session’s latent categorical representation; The other is to learn a session’s sequential representation. Each module independently makes a next item prediction, and their predictions are fused as the final recommendation result. Experiments on three public datasets validate that our model achieves performance improvements over the recent state-of-the-art algorithms.
A pilot study of Global ICT strategy applications in sustainable continuing education

In the area of continuing education, computer network has been an important part of people’s lives in the growth and advancement of quality education. This challenge explores the function of the information system in sustaining technology workers for social and economic growth in human communities worldwide and analyzes computer network applications and major features of continuing education. Continuing education is anticipated to employ the usage of other new wireless technologies in the immediate future, through the use of modern cell technology, smartphones or related products. However, this does not sideline the use of the simple computer network to support information, as ever less developed countries also keep up with today’s and future sophisticated technology in e-education, especially ongoing education for working people and adults, without leaving their own offices or homes. This paper explores the issue of global ICT and its implementation in sustainable education.
Encrypted MPC based on ADMM real-time iterations

Encrypted control enables confidential controller evaluations in cloud-based or networked control systems. Technically, an encrypted controller is a modified control algorithm that is capable of computing encrypted control actions based on encrypted system states without intermediate decryption. The realization of such controllers, e.g., using homomorphic encryption, is non-trivial. Nevertheless, even optimization-based model predictive control (MPC) has already been implemented in an encrypted fashion. However, the existing schemes either require an explicit solution of the parametric optimal control problem (OCP) or they can only consider input constraints. In this paper, we present a novel encrypted MPC that allows to include state and input constraints without the requirement of an explicit solution of the OCP. The approach builds on the encrypted implementation of a single iteration of the alternating direction method of multipliers (ADMM) per time step, i.e., ADMM real-time iterations.
Deep Neural Networks Classification via Binary Error-Detecting Output Codes

One-hot encoding is the prevalent method used in neural networks to represent multi-class categorical data. Its success stems from its ease of use and interpretability as a probability distribution when accompanied by a softmax activation function. However, one-hot encoding leads to very high dimensional vector representations when the categorical data’s cardinality is high. The Hamming distance in one-hot encoding is equal to two from the coding theory perspective, which does not allow detection or error-correcting capabilities. Binary coding provides more possibilities for encoding categorical data into the output codes, which mitigates the limitations of the one-hot encoding mentioned above. We propose a novel method based on Zadeh fuzzy logic to train binary output codes holistically. We study linear block codes for their possibility of separating class information from the checksum part of the codeword, showing their ability not only to detect recognition errors by calculating non-zero syndrome, but also to evaluate the truth-value of the decision. Experimental results show that the proposed approach achieves similar results as one-hot encoding with a softmax function in terms of accuracy, reliability, and out-of-distribution performance. It suggests a good foundation for future applications, mainly classification tasks with a high number of classes.
Relating Adversarially Robust Generalization to Flat Minima

Adversarial training (AT) has become the de-facto standard to obtain models robust against adversarial examples. However, AT exhibits severe robust overfitting: cross-entropy loss on adversarial examples, so-called robust loss, decreases continuously on training examples, while eventually increasing on test examples. In practice, this leads to poor robust generalization, i.e., adversarial robustness does not generalize well to new examples. In this paper, we study the relationship between robust generalization and flatness of the robust loss landscape in weight space, i.e., whether robust loss changes significantly when perturbing weights. To this end, we propose average- and worst-case metrics to measure flatness in the robust loss landscape and show a correlation between good robust generalization and flatness. For example, throughout training, flatness reduces significantly during overfitting such that early stopping effectively finds flatter minima in the robust loss landscape. Similarly, AT variants achieving higher adversarial robustness also correspond to flatter minima. This holds for many popular choices, e.g., AT-AWP, TRADES, MART, AT with self-supervision or additional unlabeled examples, as well as simple regularization techniques, e.g., AutoAugment, weight decay or label noise. For fair comparison across these approaches, our flatness measures are specifically designed to be scale-invariant and we conduct extensive experiments to validate our findings.
Cells image generation method based on VAE-SGAN

Although it has entered the era of big data, the image data with important research value in the field of medical imaging is often scarce due to various reasons such as difficult acquisition. To increase the image data in these fields, people have developed computer simulations to generate images Methodological research. At present, there are two main types of image generation models with potential, namely, Variational Autoencoders (VAE) [1] and Generative Adversarial Networks (GAN) [2]. In the process of classifying white blood cells, there are often insufficient sample data and unbalanced data. How to effectively expand the sample size and solve the imbalance of training data has always been a hot issue in research. In order to solve this problem, this paper proposes an image generation method based on the fusion network of VAE and GANs, which avoids the shortcomings of the two generation methods when used alone, and makes the generated images have higher generation quality.
Neural Machine Translation for Low Resource Assamese–English

Neural machine translation (NMT) attracts attention to the machine translation (MT) community because of its potential to persist sequential data over variable lengths of input and output sentences. With the attention mechanism, the NMT system achieves state-of-the-art technique which allows focusing on specific input vectors of the input sentence instead of learning a single vector for each input sentence. Although, the NMT system improves translation quality by handling long-term dependency issue and facilitate the ability of contextual analysis, and it requires adequate parallel train corpus, which is difficult in the context of low resource languages. This paper contributes an Assamese–English parallel corpus and builds two NMT systems, sequence-to-sequence recurrent neural network (RNN) with attention mechanism (NMT-1) and a transformer model having self-attention mechanism (NMT-2). The NMT-2 system has achieved a higher bilingual evaluation understudy (BLEU) score of 10.03 for English-to-Assamese and BLEU score 13.10 for Assamese-to-English translation, respectively.
Is Private Learning Possible with Instance Encoding?

A private machine learning algorithm hides as much as possible about its training data while still preserving accuracy. In this work, we study whether a non-private learning algorithm can be made private by relying on an instance-encoding mechanism that modifies the training inputs before feeding them to a normal learner. We formalize both the notion of instance encoding and its privacy by providing two attack models. We first prove impossibility results for achieving a (stronger) model. Next, we demonstrate practical attacks in the second (weaker) attack model on InstaHide, a recent proposal by Huang, Song, Li and Arora [ICML’20] that aims to use instance encoding for privacy.
Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging

<h2>Abstract</h2><h3>Purpose</h3> There are currently few specific artificial intelligence (AI) studies for Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions. This study aimed to establish an AI diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging and to compare its performance with that of radiologists. <h3>Methods</h3> The ultrasound images of 1311 lesions were evaluated by radiologists according to the BI-RADS categories, using pathology results as reference. Two classification standards (standards 1 and 2) for benign and malignant lesions were defined and used to calculate the diagnostic performance of radiologists, altogether and individually. The breast lesion images were also used to develop an AI diagnostic model. <h3>Results</h3> The diagnostic performance of AI and that of the radiologists were compared using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). All parameters of diagnostic performance, except for sensitivity and NPV, improved with standard 2. For the 202 lesions in the test set, the diagnostic performance of the AI model had 77.0% accuracy, 82.0% sensitivity, 71.7% specificity, 79.3% PPV, 75.1% NPV, and an AUC of 0.846. When the AI model was used to analyze category 4A lesions, the PPV was 9.3%, which was better than that of the radiologists, although not significantly. <h3>Conclusions</h3> Deep learning technology shows a good performance in classifying benign and malignant breast lesions. It may be potentially used in practice to improve diagnostic accuracy and reduce unnecessary biopsies of breast lesions.
Detection and Recovery of Higher Tampered Images Using Novel Feature and Compression Strategy

Due to the availability of powerful image-editing software and the growing amount of multimedia data that is transmitted via the Internet, integrity verifications and confidentiality of the data are becoming critical issues. However, currently, the accuracy of detecting and the recovery capability of the tampered images by the existing methods through watermarking strategy is still not at the required level, especially at a higher tampered rate. This paper proposes a new blind and fragile watermarking method to detect tampering and better recovery of tampered images. To improve the quality of both the watermarked and the recovered images, a new feature extraction scheme is introduced which will produce a short but comprehensive recovery code using a new compression strategy. If a block in the image tampers, the proposed embedded feature allows the original data to be extracted for recovery. To overcome tamper coincidence, every block’s watermarked data contains not only the recovery code belonging to the block itself but also its neighbor’s data as a second layer of recovery. Various size blocks were investigated to see the performance and compare their efficiency for recovering an image after different tampering rates. The test showed the smaller block sizes may be more suitable for locating tampering, where the bigger ones are more suitable when the tampering rate is higher. The bigger block sizes in the proposed method can recover an image even after a 60% tampering rate with high quality (more than 31 dB). The experimental results prove that the proposed method can have better efficiency for detecting tampering, and recovery of the original image, compared to the relevant existing methods.
Automated imbalanced classification via meta-learning

• The first automated machine learning approach to imbalanced domains. • 95% reduction of computational efforts concerning workflow selection and optimisation. • Competitive results in comparison to state-of-the-art AutoML methods. Imbalanced learning is one of the most relevant problems in machine learning. However, it faces two crucial challenges. First, the amount of methods proposed to deal with such problem has grown immensely, making the validation of a large set of methods impractical. Second, it requires specialised knowledge, hindering its use by those without such level of experience. In this paper, we propose the Automated Imbalanced Classification method, ATOMIC . Such a method is the first automated machine learning approach for imbalanced classification tasks. It provides a ranking of solutions most likely to ensure an optimal approximation to a new domain, drastically reducing associated computational complexity and energy consumption. We carry this out by anticipating the loss of a large set of predictive solutions in new imbalanced learning tasks. We compare the predictive performance of ATOMIC against state-of-the-art methods using 101 imbalanced data sets. Results demonstrate that the proposed method provides a relevant approach to imbalanced learning while reducing learning and testing efforts of candidate solutions by approximately 95%.
Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique s... | Find, read and cite all the research you need on Tech Science Press
How to Measure Scalability of Distributed Stream Processing Engines?

Scalability is promoted as a key quality feature of modern big data stream processing engines. However, even though research made huge efforts to provide precise definitions and corresponding metrics for the term scalability, experimental scalability evaluations or benchmarks of stream processing engines apply different and inconsistent metrics. With this paper, we aim to establish general metrics for scalability of stream processing engines. Derived from common definitions of scalability in cloud computing, we propose two metrics: a load capacity function and a resource demand function. Both metrics relate provisioned resources and load intensities, while requiring specific service level objectives to be fulfilled. We show how these metrics can be employed for scalability benchmarking and discuss their advantages in comparison to other metrics, used for stream processing engines and other software systems.