Reviewerly/exHarmony huggingface dataset:
Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system

Postoperative patients are at risk of life-threatening complications such as hemodynamic decompensation or arrhythmia. Automated detection of patients with such risks via a real-time clinical decision support system may provide opportunities for early and timely interventions that can significantly improve patient outcomes. We utilize multimodal features derived from digital signal processing techniques and tensor formation, as well as the electronic health record (EHR), to create machine learning models that predict the occurrence of several life-threatening complications up to 4 hours prior to the event. In order to ensure that our models are generalizable across different surgical cohorts, we trained the models on a cardiac surgery cohort and tested them on vascular and non-cardiac acute surgery cohorts. The best performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0.94 on training and 0.94 and 0.82, respectively, on testing for the 0.5-hour interval. The AUROCs only slightly dropped to 0.93, 0.92, and 0.77, respectively, for the 4-hour interval. This study serves as a proof-of-concept that EHR data and physiologic waveform data can be combined to enable the early detection of postoperative deterioration events.
An Improved Data-Efficiency Algorithm Based on Combining Isolation Forest and Mean Shift for Anomaly Data Filtering in Wind Power Curve

A wind turbine working in a harsh environment is prone to generate abnormal data. An efficient algorithm based on the combination of an Isolation Forest (I-Forest) and a mean-shift algorithm is proposed for data cleaning in wind power curves. The I-Forest is used for detecting the local anomalies in each power and wind speed interval after data preprocessing. The contamination of I-Forest can be flexibly adjusted according to the data distribution of the wind turbine data. The remaining stacked data is eliminated by the mean-shift algorithm. To verify the filtering performance of the proposed combined method, five different algorithms, including the quartile and k-means (QK), the quartile and density-based spatial clustering (QD), the mathematical morphology operation (MMO), the fast data cleaning algorithm (FA), and the proposed one, are applied to the wind power curves of a prototype wind farm for comparisons. The numerical results have positively confirmed the reliability of the universal framework provided by the proposed algorithm.
Cryptocurrencies Advantages and Disadvantages: A Review

With the rapid spread of technology in life and the necessary need to increase the speed of payment processes, confidentiality, and privacy, cryptocurrencies appeared. A cryptocurrency is a virtual and intangible currency, in which transactions are made through the internet. These currencies are characterized by decentralization, transparency, and privacy. Because transactions are carried out through a cryptography process and depend on Blockchain technology it is highly protected. Blockchain generally is a distributed ledger or a decentralized database. The Blockchain architecture combines advanced cryptography, consensus mechanisms, and a complex system of incentives. In cryptocurrency, transactions are created, transferred, and verified through an integrated process called mining. Blockchain technology architecture has given cryptocurrencies many advantages and features that increase their strength and distinction from regular financial transactions such as decentralization, confidentiality, anonymity, very low fees, unrestricted by geography, transparency, protection from Inflation, and the peer-to-peer network. The misuse of powerful features in cryptocurrencies and Blockchain technology has led to many disadvantages such as the risks of lack of knowledge, the lack of wide acceptance, the high risk of investment, its volatile nature, and the inability to return missing payments. This study concentrates on cryptocurrencies in terms of advantages and disadvantages.
An Encryption Algorithm for Region of Interest in Medical DICOM Based on One-Dimensional eλ-cos-cot Map

Today, with the rapid development of the Internet, improving image security becomes more and more important. To improve image encryption efficiency, a novel region of interest (ROI) encryption algorithm based on a chaotic system was proposed. First, a new 1D eλ-cos-cot (1D-ECC) with better chaotic performance than the traditional chaotic system is proposed. Second, the chaotic system is used to generate a plaintext-relate keystream based on the label information of a medical image DICOM (Digital Imaging and Communications in Medicine) file, the medical image is segmented using an adaptive threshold, and the segmented region of interest is encrypted. The encryption process is divided into two stages: scrambling and diffusion. In the scrambling stage, helical scanning and index scrambling are combined to scramble. In the diffusion stage, two-dimensional bi-directional diffusion is adopted, that is, the image is bi-directionally diffused row by column to make image security better. The algorithm offers good encryption speed and security performance, according to simulation results and security analysis.
Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification

Breast cancer (BC) is the second leading cause of death in developed and developing nations, accounting for 8% of deaths after lung cancer. Gene mutation, constant pain, size fluctuations, colour (roughness), and breast skin texture are all characteristics of BC. The University of Wisconsin Hospital donated the WDBC dataset, which was created via fine-needle aspiration (biopsies) of the breast. We have implemented multilayer perceptron (MLP), K-nearest neighbor (KNN), genetic programming (GP), and random forest (RF) on the WBCD dataset to classify the benign and malignant patients. The results show that RF has a classification accuracy of 96.24%, which outperforms all the other classifiers.
Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF

Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffic data are dimensioned down with UMAP algorithm. The random forest algorithm is improved based on parameter optimization, and the improved random forest algorithm is used to classify the network traffic data, distinguishing normal data from abnormal data and classifying nine different types of network attacks from the abnormal data. Experimental results on the UNSW-NB15 dataset, which are significant improvements compared to traditional machine-learning methods, show that the UMAP-RF hybrid model can perform network attack traffic detection effectively, with accuracy and recall rates of 92.6% and 91%, respectively.
A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center

Background: Diabetes mellitus (DM) is a major public health problem worldwide. It involves dysfunction of blood sugar regulation resulting from insulin resistance, inadequate insulin secretion, or excessive glucagon secretion. Methods: This study collated 971,401 drug usage records of 51,009 DM patients. These data include patient identification code, age, gender, outpatient visiting dates, visiting code, medication features (included items, doses, and frequencies of drugs), HbA1c results, and testing time. We apply a random forest (RF) model for feature selection and implement a regression model with the bidirectional long short-term memory (Bi-LSTM) deep learning architecture. Finally, we use the root mean square error (RMSE) as the evaluation index for the prediction model. Results: After data cleaning, the data included 8,729 male and 9,115 female cases. Metformin was the most important feature suggested by the RF model, followed by glimepiride, acarbose, pioglitazone, glibenclamide, gliclazide, repaglinide, nateglinide, sitagliptin, and vildagliptin. The model performed better with the past two seasons in the training data than with additional seasons. Further, the Bi-LSTM architecture model performed better than support vector machines (SVMs). Discussion & Conclusion: This study found that Bi-LSTM models is a well kernel in a CDSS which help physicians' decision-making, and the increasing the number of seasons will negative impact the performance. In addition, this study found that the most important drug is metformin, which is recommended as first-line treatment OHA in various situations for DM patients.
Self-Assessment of Digital Competence for Educators: a Brazilian Study with University Professors

Educators' digital competence is essential for the promotion of teaching strategies concatenated with the digital society's demands. The aim of this study is to analyze the digital competence of 39 university professors in Santo Amaro, Bahia State, Brazil, pointing out formation paths for development of educational practices able to promote learning in digital environments. It is configured as an exploratory and descriptive research, with a quantitative approach, using the self-assessment scale from Digital Competence of Educators (DigCompEdu). Out of the 39 responding university teachers, 18 were of the female gender (46.2%) and 21 male gender (53.8%), with ages varying between 25 and 62 years. The results related to the different scopes revealed a mean value (44 points) that locates the participating educators in level B1 (integrator), close to level B2 (which begins with 48 points). The professors surveyed revealed gaps at the digital competence related with the role of the online reflective practice; application of digital technologies in the teaching and learning process and assessment practices with the support of digital technologies; indicating the need for continuing professor education in order to mitigate the evidenced gaps.
Efficient Floating Point Arithmetic for Quantum Computers

One of the major promises of quantum computing is the realization of SIMD (single instruction - multiple data) operations using the phenomenon of superposition. Since the dimension of the state space grows exponentially with the number of qubits, we can easily reach situations where we pay less than a single quantum gate per data point for data-processing instructions which would be rather expensive in classical computing. Formulating such instructions in terms of quantum gates, however, still remains a challenging task. Laying out the foundational functions for more advanced data-processing is therefore a subject of paramount importance for advancing the realm of quantum computing. In this paper, we introduce the formalism of encoding so called-semi-boolean polynomials. As it turns out, arithmetic Z/2<sup><i>n</i></sup>Z ring operations can be formulated as semi-boolean polynomial evaluations, which allows convenient generation of unsigned integer arithmetic quantum circuits. For arithmetic evaluations, the resulting algorithm has been known as Fourier-arithmetic. We extend this type of algorithm with additional features, such as ancilla-free in-place multiplication and integer coefficient polynomial evaluation. Furthermore, we introduce a tailormade method for encoding signed integers succeeded by an encoding for arbitrary floating-point numbers. This representation of floating-point numbers and their processing can be applied to any quantum algorithm that performs unsigned modular integer arithmetic. We discuss some further performance enhancements of the semi boolean polynomial encoder and finally supply a complexity estimation. The application of our methods to a 32-bit unsigned integer multiplication demonstrated a 90% circuit depth reduction compared to carry-ripple approaches.
Sentiment Analysis on Amazon Product Reviews using the Recurrent Neural Network (RNN)

In this paper, the problem of sentiment analysis on Amazon products is tackled.In fact, sentiment analysis systems are applied in all business and social fields.This is because the opinions are at the center of all human activities, and they are key influencers of our behaviors.In this study, the recurrent neural network (RNN) model is used to classify the reviews.Three Amazon review datasets were applied to predict the sentiments of the authors.In conclusion, our results are comparable to the best state of the art models with an accuracy of 85%, 70% and 70% for three datasets.
Implementation of Support Vector Regression for Polkadot Cryptocurrency Price Prediction

Cryptocurrency investment is an investment instrument that has high risk but also has a greater advantage than other investment instruments. To make a big profit, investors need to analyze cryptocurrency investments to predict the price of the cryptocurrency to be purchased. The highly volatile movement of cryptocurrency prices makes it difficult for investors to predict those prices. Data mining is the process of extracting large amounts of information from data by collecting, using data, the history of data relationship patterns, and relationships in large data sets. Support Vector Regression has the advantage of doing accurate cryptocurrency price predictions and can overcome the problem of overfitting by itself. Polkadot is one of the cryptocurrencies that are often used as investment instruments in the world of cryptocurrencies. Polkadot cryptocurrency price prediction analysis using the Support Vector Regression algorithm has a good predictive accuracy value, including for Polkadot daily closing price data, namely with a radial basis function (RBF) kernel with cost parameters C = 1000 and gamma = 0.001 obtained model accuracy of 90.00% and MAPE of 5.28 while for linear kernels with parameters C = 10 obtained an accuracy of 87.68% with a MAPE value of 6.10. It can be concluded that through parameter tuning, the model formed has an accuracy value and the best MAPE is to use a radial kernel basis function (RBF) with cost parameters C = 1000 and gamma = 0.001. The results show that the Support Vector Regression method is quite good if used for the prediction of Polkadot cryptocurrencies.
How reparametrization trick broke differentially-private text representation learning

As privacy gains traction in the NLP community, researchers have started adopting various approaches to privacy-preserving methods.One of the favorite privacy frameworks, differential privacy (DP), is perhaps the most compelling thanks to its fundamental theoretical guarantees.Despite the apparent simplicity of the general concept of differential privacy, it seems non-trivial to get it right when applying it to NLP.In this short paper, we formally analyze several recent NLP papers proposing text representation learning using DPText (Beigi et al., 2019a,b;Alnasser et al., 2021;Beigi et al., 2021) and reveal their false claims of being differentially private.Furthermore, we also show a simple yet general empirical sanity check to determine whether a given implementation of a DP mechanism almost certainly violates the privacy loss guarantees.Our main goal is to raise awareness and help the community understand potential pitfalls of applying differential privacy to text representation learning.
EchoGen: Generating Conclusions from Echocardiogram Notes

Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length. In this work, we focus on echocardiogram notes that is longer and more complex compared to previous note types. We formally define the task of echocardiography conclusion generation (EchoGen) as generating a conclusion given the findings section, with emphasis on key cardiac findings. To promote the development of EchoGen methods, we present a new benchmark, which consists of two datasets collected from two hospitals. We further compare both standard and state-of-the-art methods on this new benchmark, with an emphasis on factual consistency. To accomplish this, we develop a tool to automatically extract concept-attribute tuples from the text. We then propose an evaluation metric, FactComp, to compare concept-attribute tuples between the human reference and generated conclusions. Both automatic and human evaluations show that there is still a significant gap between human-written and machine-generated conclusions on echo reports in terms of factuality and overall quality.
A High-Performance ECC Processor Over Curve448 Based on a Novel Variant of the Karatsuba Formula for Asymmetric Digit Multiplier

In this paper, we present a high-performance architecture for elliptic curve cryptography (ECC) over Curve448, which to the best of our knowledge, is the fastest implementation of ECC point multiplication over Curve448 to date. Firstly, we introduce a novel variant of the Karatsuba formula for asymmetric digit multiplier, suitable for typical DSP primitive with asymmetric input. It reduces the number of required DSPs compared to previous work and preserves the performance via full parallelization and pipelining. We then construct a 244-bit pipelined multiplier and interleaved fast reduction algorithm, yielding a total of 12 stages of pipelined modular multiplication with four stages of input delay. Additionally, we present an efficient Montgomery ladder scheduling with no additional register is required. The implementation on the Xilinx 7-series FPGA: Virtex-7, Kintex-7, Artix-7, and Zynq 7020 yields execution times of 0.12, 0.13, 0.24, and 0.24 ms, respectively. It increases the throughput by 242% compared to the best previous work on Zynq 7020 and by 858% compared to the best previous work on Virtex-7. Furthermore, the proposed architecture optimizes nearly 63% efficiency improvement in terms of Area&#x00D7;Time tradeoff. Lastly, we extend our architecture with well-known side-channel protections such as scalar blinding, base-point randomization, and continuous randomization.
Mixed Noise Removal for Hyperspectral Image With ${l_{0}}$-${l_{1 - 2}}$SSTV Regularization

Hyperspectral images (HSIs) are usually corrupted by various types of mixed noises, which degrades the qualities of acquired images and limits the subsequent applications.In this paper, we propose a novel denoised method based on a hybrid spatial-spectral total variation (SSTV) regularization, which we refer to as l0-l1-2SSTV.Specifically, l0-l1-2SSTV can be treated as an integrated regularization embedding spatial-spectral l0 gradient model into l1-2SSTV.l1-2SSTV regularization exploits the sparse structures in both spatial and spectral domains.Hence, the correlations within HSIs are fully considered.Due to the good performance of l1-2-norm in image restoration, l1-2SSTV gives a tighter approximation for the gradient domains of HSIs.It can effectively avoid artifacts and oversmoothing caused by the limitation of the SSTV regularization based on l1-norm (l1SSTV).Meanwhile, the l0 gradient regularization controls the number of nonzero gradients to promote the local piecewise smoothness, making denoised images preserve clear edges.With the effective combination of l1-2SSTV and l0 gradient regularization, l0-l1-2SSTV produces high-quality restoration results in the denoising process: better detail preservation and sharper edges.The augmented Lagrangian method (ALM) and the difference of convex algorithm (DCA) are exploited to optimize the proposed model.The results for simulated and real experiments demonstrate the effectiveness and superiority of the proposed method compared with state-of-the-art methods.
Autonomous State-Based Flipper Control for Articulated Tracked Robots in Urban Environments

We demonstrate a hybrid approach to autonomous flipper control, focusing on a fusion of hard-coded and learned knowledge. The result is a sample-efficient and modifiable control structure that can be used in conjunction with a mapping/navigation stack. The backbone of the control policy is formulated as a state machine whose states define various flipper action templates and local control behaviors. It is also used as an interface that facilitates the gathering of demonstrations to train the transitions of the state machine. We propose a soft-differentiable state machine neural network that mitigates the shortcomings of its naively implemented counterpart and improves over a multi-layer perceptron baseline in the task of state-transition classification. We show that by training on several minutes of user-gathered demonstrations in simulation, our approach is capable of a zero-shot domain transfer to a wide range of obstacles on a similar real robotic platform. Our results show a considerable increase in performance over a previous competing approach in several essential criteria. A subset of this work was successfully used in the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge to alleviate the operator of manual flipper control. We autonomously traversed stairs and other obstacles, improving map coverage.
Investigating Failures of Automatic Translationin the Case of Unambiguous Gender

Transformer-based models are the modern work horses for neural machine translation (NMT), reaching state of the art across several benchmarks.Despite their impressive accuracy, we observe a systemic and rudimentary class of errors made by current state-of-the-art NMT models with regards to translating from a language that doesn't mark gender on nouns into others that do.We find that even when the surrounding context provides unambiguous evidence of the appropriate grammatical gender marking, no tested model was able to accurately gender occupation nouns systematically.We release an evaluation scheme and dataset for measuring the ability of NMT models to translate gender morphology correctly in unambiguous contexts across syntactically diverse sentences.Our dataset translates from an English source into 20 languages from several different language families.With the availability of this dataset, our hope is that the NMT community can iterate on solutions for this class of especially egregious errors. 1 https://github.com/arendu/Unambiguous-gender-bias Source/Target Label Src: My sister is a carpenter 4 .Correct Tgt: Mi hermana es carpenteria(f) 4 .Src: That nurse 1 is a funny man .
Critical success factors for assessing the effectiveness of E-CRM systems in online shopping: the mediating role of user satisfaction

The effectiveness of the E-CRM system was examined in this study, involving those with online shopping experience, specifically the regular customers of Carrefour in Jordan. Data were obtained through 550 distributed sets of questionnaires. Four trained and certified research assistants assisted the data gathering process. Data from 320 returned questionnaires were analyzed using Structural Equation Modeling (SEM). System quality, access to information, security, training and customer satisfaction were the examined factors towards the effectiveness of E-CRM systems. Results show positive impact of security, system quality, training, and access to information on user satisfaction, and user satisfaction affected the effectiveness of E-CRM. Nonetheless, the relationship between training and effectiveness of E-CRM on online shopping was not mediated by user satisfaction.
Transformer-based Architecture for Empathy Prediction and Emotion Classification

This paper describes the contribution of team PHG to the WASSA 2022 shared task on Empathy Prediction and Emotion Classification.The broad goal of this task was to model an empathy score, a distress score, and the type of emotion associated with the person who had reacted to the essay written in response to a newspaper article.We have used the RoBERTa model for training, and on top of it, five layers are added to finetune the transformer.We also use a few machine learning techniques to augment and upsample the data.Our system achieves a Pearson Correlation Coefficient of 0.488 on Task 1 (Average of Empathy -0.470 and Distress -0.506) and Macro F1-score of 0.531 on Task 2.