Reviewerly/exHarmony huggingface dataset:
GFLibPy: An Open-Source Python Toolbox for Genetic Folding Algorithm

In this paper, a toolbox is proposed for the genetic folding algorithm for classification and regression problems using Python programming language. Although the Genetic Folding algorithm represents the brain activity in the toolbox, it uses various machine learning techniques. The Support Vector Machine (SVM) for classification and regression was applied to the UCI dataset. The toolbox enables the use of the activity to classify both UCI binary and multi-classification datasets and also to do regression on some other UCI datasets. In the research work, the objective was to find the best GF model that improve results through various generic GF chromosomes. The 23 python files implemented and 15 datasets were used in this research and contributed to the GFLibPy of the GF algorithm. Scaling the UCI dataset was used in the toolbox. The datasets were inputted into two machine learning algorithms: Genetic Folding (GF) and Support Vector Machines (SVMs). This paper shows that this toolbox holds some promise for the classification of various datasets, which can be used in any dataset in the context of classifying and predicting models. The focus of the work will be on the relationship between the kernel generations and the predefined based kernels and classifications such as binary and multi-classifications. The paper defined the simulation performance of the GF based kernels in a detailed analysis of multi and binary classifications. At present, there is nothing available for free tools to include any of the basic tools for GF algorithm in Python language. For different continuous goal, GF kernels are competitive with their respective generated models.
Few-Sample Generation of Amount in Figures for Financial Multi-Bill Scene Based on GAN

Recognition of amount in figures in the financial multi-bill scenes is crucial for the automatic banking business. However, the diversity of banking business and the limitation of customer data privacy determine that it is difficult to collect a large number of sample datasets. Aiming at the problem of insufficient training data in multi-bill scenes and the low accuracy of the detection model, this article proposes a new generative adversarial network (GAN) to generate new samples and to expand the bill dataset, which is then adopted to train a framework for recognition of the bill amount. In the proposed WGAN-SA, a residual block is adopted as the basic structure of the generator and the discriminator, and the self-attention mechanism is also utilized to improve the generation performance. In addition, Wasserstein distance is utilized to measure the distance between real and synthetic samples. Experimental results on the benchmark dataset and comparisons with state-of-the-art works show that our proposed WGAN-SA can effectively improve the few-sample learning performance. Besides, experiments on the bill dataset verify that our method can solve the problem of model collapse and has the ability to generate images of the amount in figures with better fidelity and variety, which is also helpful to achieve better bill amount recognition performance compared with other latest works.
A Literature Review on Blockchain Technology: Risk in Supply Chain Management

As blockchain technology is continuously evolving, questions arise regarding the possible risk of implementing blockchain technology in supply chain management (SCM). Prominent among these concerns is the risk that may negatively influence SCM and consider the potential solution to managing the risk. This study adopts an integrative review method. An online search is performed by considering relevant keywords related to “blockchain technology risks” closely connected with the SCM context within the period of 2017 until 2021. Several major risks are identified—privacy, regulatory, financial, technological, fraud, and operational risk. This article adopts an integrative literature review method to select relevant literature related to blockchain technology implementation in the supply chain. The integrative literature review method is suitable for this study since the risk of implementing blockchain technology is often shadowed by the benefits of implementing blockchain technology in SCM. The findings are highlighted by categorizing the potential risk that must be recognized in SCM to accompany the blockchain technology and the importance of rectifying the risk according to the organization's capacity and resources.
Multi-sensor Fusion Tracking Algorithm by Square Root Cubature Kalman Filter for Intelligent Vehicle

With the development of unmanned driving technology, the demand for tracking accuracy is increasing. To rely on a single sensor to obtain detection results in complex environments is limited in accuracy, and multi-sensor fusion is an effective method. Therefore, the environmental sensing technology based on multi-sensor fusion is one of the present research hotspots. This paper proposes a multi-sensor fusion tracking algorithm based on the square root Cubature Kalman filter (SRCKF) for the purpose of nonlinearity of the vehicle target tracking system. This method establishes the equation of state and the measurement equation using the dynamic model, and utilizes the multi sensor measurement signal. A data fusion method based on cubature Kalman filter (CKF) with nonlinear system to avoid errors caused by linearization of nonlinear systems by extended Kalman filter (EKF). In the filtering process, the covariance square root matrix is used in place of the covariance matrix participating in the iterative operation. It effectively avoids the divergence of the filter and improves the convergence speed and stability of the filtering algorithm.
Identity-Expression Ambiguity in 3D Morphable Face Models

3D Morphable Models are a class of generative models commonly used to model faces. They are typically applied to ill-posed problems such as 3D reconstruction from 2D data. Several ambiguities in this problem's image formation process have been studied explicitly. We demonstrate that nonorthogonality of the variation in identity and expression can cause identity-expression ambiguity in 3D Morphable Models, and that in practice expression and identity are far from orthogonal and can explain each other surprisingly well. Whilst previously reported ambiguities only arise in an inverse rendering setting, identity-expression ambiguity emerges in the 3D shape generation process itself. We demonstrate this effect with 3D shapes directly as well as through an inverse rendering task, and use two popular models built from high quality 3D scans as well as a model built from a large collection of 2D images and videos. We explore this issue's implications for inverse rendering and observe that it cannot be resolved by a purely statistical prior on identity and expression deformations.
User Authentication Method via Speaker Recognition and Speech Synthesis Detection

As the Internet has been developed, various online services such as social media services are introduced and widely used by many people. Traditionally, many online services utilize self-certification methods that are made using public certificates or resident registration numbers, but it is found that the existing methods pose the risk of recent personal information leakage accidents. The most popular authentication method to compensate for these problems is biometric authentication technology. The biometric authentication techniques are considered relatively safe from risks like personal information theft, forgery, etc. Among many biometric-based methods, we studied the speaker recognition method, which is considered suitable to be used as a user authentication method of the social media service usually accessed in the smartphone environment. In this paper, we first propose a speaker recognition-based authentication method that identifies and authenticates individual voice patterns, and we also present a synthesis speech detection method that is used to prevent a masquerading attack using synthetic voices.
Gradient Boosted Neural Decision Forest

Tree-based models and deep neural networks are two schools of effective classification methods in machine learning. While tree-based models are robust irrespective of data domain, deep neural networks have advantages in handling high-dimensional data. Adding a differentiable neural decision forest to the neural network can generally help exploit the benefits of both models. Therefore, traditional decision trees diverge into a bagging version (i.e., random forest) and a boosting version (i.e., gradient boost decision tree). In this work, we aim to harness the advantages of both bagging and boosting by applying gradient boost to a neural decision forest. We propose a gradient boost that can learn the residual using a neural decision forest, considering the residual as a part of the final prediction. Besides, we design a structure for learning the parameters of neural decision forest and gradient boost module in contiguous steps, which is extendable to incorporate multiple gradient-boosting modules in an end-to-end manner. Our extensive experiments on several public datasets demonstrate the competitive performance and efficiency of our model against a series of baseline methods in solving various machine learning tasks.
A Framework for Self-Explaining Systems in the Context of Intensive Care

Ventilated intensive care patients represent a sizable group in the intensive care unit that requires special attention. Although intensive care units are staffed with more nurses per patient than regular wards, the situation is often precarious. A situation that has become more acute during the COVID-19 pandemic. Weaning from mechanical ventilation as well as the limited communication abilities pose substantial stress to the patients. The incapability to impart even basic needs may negatively impact the healing process and can lead to delirium and other complications. To support the communication and information of weaning patients as well as to foster patient autonomy, we are developing a smart environment that is tailored to the intensive care context. While the provision and connection of smart objects and applications for this purpose can be time-consuming, self-organization and self-explainability may present helpful tools to reduce the effort. In this paper, we present a framework for self-explaining and semi-automatically interconnected ensembles of smart objects and ambient applications (that are integrated into smart spaces) used to realize the assistive environment. Based on a description language for these components, ensembles can be dynamically connected and tailored to the needs and abilities of the patients. Our framework has been developed and evaluated iteratively and has been tested successfully in our laboratory.
Evaluating Deep Learning Networks for Modulation Recognition

As the use of wireless communication expands demand for radio spectrum, so does the need for effective automatic modulation recognition (AMR). Current methods of AMR include feature extractions, maximum likelihood algorithms, and deep learning (DL) networks primarily based on CNN structures. Many methods are limited by the slow training and testing time, the need for massive amounts of training data, and low probability of correct classification in the presence of noise. Our research proposes using a fully connected dense network instead of a convolutional one to mitigate some of these challenges. To test modulation classification accuracy, we used in-phase and quadrature samples from data sets at various signal-to-noise levels to evaluate 5 DL networks and a matched filter approach. Our experiments show that compared to traditional convolutional networks, our fully connected network improves training and testing times by order of magnitude, has an accuracy within 5% of the most accurate convolutional network, and uses a factor of 32 fewer parameters. We also demonstrate that using a bank of matched filters remains challenging, as correctly discriminating amongst several positive matches is not straightforward.
A Cross-Country Analysis of Macroeconomic Responses to COVID-19 Pandemic Using Twitter Sentiments

COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes of the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to COVID-19 economic crisis, and (3) compare their expected economic reaction to COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the social sentiments of the Twitter data to approximate the macroeconomic statistics. The results show that in the near future, the main concern for all the countries is high inflation rate. Moreover, South Africa and Nigeria need to deal with high unemployment rates. Our work demonstrates that data from country-specific Twitter can be used to better understand concerns and sentiments around the macroeconomic situations at the local level. This can potentially lead to more targeted and publicly acceptable policies based on social media content.
Learn A Compression for Objection Detection - VAE with a Bridge

Recent advances in sensor technology and wide deployment of visual sensors lead to a new application whereas compression of images are not mainly for pixel recovery for human consumption, instead it is for communication to cloud side machine vision tasks like classification, identification, detection and tracking. This opens up new research dimensions for a learning based compression that directly optimizes loss function in vision tasks, and therefore achieves better compression performance vis-a-vis the pixel recovery and then performing vision tasks computing. In this work, we developed a learning based compression scheme that learns a compact feature representation and appropriate bitstreams for the task of visual object detection. Variational Auto-Encoder (VAE) framework is adopted for learning a compact representation, while a bridge network is trained to drive the detection loss function. Simulation results demonstrate that this approach is achieving a new state-of-the-art in task driven compression efficiency, compared with pixel recovery approaches, including both learning based and handcrafted solutions.
Selective Prediction With Long Short-term Memory Using Unit-Wise Batch Standardization for Time Series Health Data Sets: Algorithm Development and Validation

In any health care system, both the classification of data and the confidence level of such classifications are important. Therefore, a selective prediction model is required to classify time series health data according to confidence levels of prediction.This study aims to develop a method using long short-term memory (LSTM) models with a reject option for time series health data classification.An existing selective prediction method was adopted to implement an option for rejecting a classification output in LSTM models. However, a conventional selection function approach to LSTM does not achieve acceptable performance during learning stages. To tackle this problem, we proposed a unit-wise batch standardization that attempts to normalize each hidden unit in LSTM to apply the structural characteristics of LSTM models that concern the selection function.The ability of our method to approximate the target confidence level was compared by coverage violations for 2 time series of health data sets consisting of human activity and arrhythmia. For both data sets, our approach yielded lower average coverage violations (0.98% and 1.79% for each data set) than those of the conventional approach. In addition, the classification performance when using the reject option was compared with that of other normalization methods. Our method demonstrated superior performance for selective risk (12.63% and 17.82% for each data set), false-positive rates (2.09% and 5.8% for each data set), and false-negative rates (10.58% and 17.24% for each data set).Our normalization approach can help make selective predictions for time series health data. We expect this technique to enhance the confidence of users in classification systems and improve collaborative efforts between humans and artificial intelligence in the medical field through the use of classification that considers confidence.
Role of Graphs in Blending Physical and Mathematical Meaning of Partial Derivatives in the Context of the Heat Equation

From literature, we know that making the connections between mathematics and physics is not trivial for most students, even at the advanced level. In the specific context of partial derivatives in thermodynamics, research suggests that making explicit connections between the mathematics and the physics is necessary to foster student understanding. In this paper, we investigate how graphical reasoning can help undergraduate students in making connections between the partial derivatives of temperature with respect to position and to time and their respective physical meaning in the context of one-dimensional systems modeled by the heat equation. We conducted task-based, think aloud interviews with four pairs of undergraduate students majoring in physics or mathematics and use dynamic conceptual blending diagrams to analyze their reasoning processes and the role of the graphs therein. The results in this paper indicate that stimulating graphical reasoning is a promising way to promote the blending of mathematical and physical knowledge. Specifically, our data shows that constructing graphs can be a catalyst which helps students to give physical meaning to the partial derivatives $$\partial T/\partial t$$ and/or $$\partial T/\partial x$$ . Therefore, in teaching/learning activities, actively promoting graph construction and reasoning based on those graphs offers perspectives to support the blending of mathematics and physics.
Multiple Myeloma Prediction from Bone-Marrow Blood Cell images using Machine Learning

Myeloma is a type of blood cancer that affects the plasma cells in the bone marrow, which produces antibodies that help the immune system to fight against outside aggression. Myeloma results in production of abnormal antibodies thereby weaking the function of the immune system. Out of the different types of blood cancers, the proposed research provides a robust mechanism for Multiple Myeloma (MM) prediction using 85 Microscopic blood images that were captured from bone marrow aspiration of patients suffering from the disease. The proposed work eradicates the probability of errors in the manual process of feature extraction by employing Convolutional Neural Network (CNN) for it and this is followed by training the model with Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest algorithms for classification. Along with SVM and Random Forest we used Random Search Optimizer for finding the suitable set of hyper parameters for better results. The overall accuracy was recorded to be 100%, for CNN-ANN model. Thus, the model can be used effectively for determining the Multiple Myeloma from the cell images.
Efficient FPGA implementation of RNS Montgomery multiplication using balanced RNS bases

This paper presents a new architecture to perform modular multiplication using Montgomery multiplication in Residue Number System (RNS). Two new balanced four-moduli RNS bases are selected, and the process of RNS Montgomery multiplication, including the required RNS to RNS conversions, is designed and implemented. Field-programmable gate array (FPGA) implementation using the adder-based residue/binary to binary/residue converters and ROM-free in the pipelined architecture has achieved better performance compared to the state-of-the-art works in literature.
Automated Clipping of Soccer Events using Machine Learning

Extracting highlight clips from soccer matches requires tedious, time-consuming, and expensive manual labor. Human operators need to search for appropriate clipping points and trim away the unwanted scenes. In our work, we aim for an automated process for generating event highlights. In particular, we develop AI-models for scene boundary detection and logo detection. Using different datasets, we present two models that automatically find the appropriate time interval for goal event extraction. The models are evaluated quantitatively, and the results show that we find the logo and scene shifts with high accuracy. Our event clipping methodology is a potential building block for a larger, fully-automated sports broadcast production pipeline.
Joint Disparity Estimation and Pseudo NIR Generation From Cross Spectral Image Pairs

Cross spectral stereo matching is a challenging task due to different spectral properties causing unreliable results in correspondence estimation. In this paper, we propose joint disparity estimation and pseudo near infrared (NIR) generation from cross spectral image pairs. To bridge the spectral gap between paired images, we adopt differential map operations and non-local blocks to improve the local attention and global attention of the network. The proposed network is based on unsupervised learning that consists of one encoder and two decoders, which performs both spectral translation and disparity estimation. For cooperative learning, we use difference map operation to connect two decoders, thus improving the inference ability of the decoder in regions even with large spectral differences. Experimental results show that the proposed network achieves good performance in cross spectral stereo matching for unreliable regions such as shadows and glasses. Moreover, the proposed network generates pseudo NIR images nearly the same as the ground truth even in the regions with large spectral difference. Besides, we achieve real-time speed of 27 FPS for <inline-formula> <tex-math notation="LaTeX">$582\times 429$ </tex-math></inline-formula> image pairs on RTX 2060 6G GPU due to the low computational complexity.
Recent Efficiency Gains in Deep Learning: Performance, Power, and Sustainability

Deep learning (DL) continues to develop at a rapid pace with improvements coming from both hardware and software sides. In this work we evaluate the strides made over the last 2 years, during which a new generation of GPU accelerators has been introduced and significant algorithmic progress has been made. We find a dramatic improvement in runtime and power usage for a standard AI training workload. Specifically, a 4x improvement in runtime and energy consumption is demonstrated. The improvements are about equally split between hardware and algorithms. Additionally, we examine further ways to improve AI training power consumption on data center servers and identity 3 system level tunings that make most difference. These yield up to 20% more energy savings without any changes to the user code. Implications for the field and ways to make DL more energy-efficient going forward are also discussed. (Abstract)