Reviewerly/exHarmony huggingface dataset:
Robust Estimation of FPGA Resources and Performance from CNN Models

Field-Programmable Gate Arrays (FPGAs) are becoming increasingly popular for implementing convolutional neural networks (CNNs) due to their low latency and very high energy efficiency. However, most FPGAs are resource-scarce and efficient mapping of CNN can quickly become a challenging task. The requirement of FPGA resources, latency, and power is affected by many parameters, including the CNN architecture and the level of computational parallelism. In practice, a software designer first explores various CNN architectures in software to improve architecture’s validation accuracy. Once an architecture is finalized, the designer ports the architecture design to FPGA for inference acceleration. The mapping process undergoes performance optimization by tweaking many design-related parameters during the design space exploration and changing the operating frequencies. The entire process is highly time-consuming. In this paper, we have presented a machine learning-based two-stage estimator for assisting in designing an FPGA-based CNN accelerator. Our Global Predictor assists in making accurate estimates of FPGA resource requirements and design latency from CNN architecture and hyperparameters expressed in Python. This assists in choosing a subset of high validation accuracy and feasible designs for mapping on FPGA. Our Detailed Predictor assists in making accurate estimates of FPGA post-route resource requirements, design latency, and final clock period for the chosen subset of designs after applying high-level synthesis level (pragma and frequency) optimizations. Our proposed estimation methodology enables a software engineer to obtain rapid and accurate estimates of the final implementation Quality of Results without executing FPGA design flows. We trained and tested our model for Xilinx Zynq Ultrascale + and Kintex-7 devices. We achieved an average prediction error of less than 9% and 4% for stages one and two, respectively.
Image Completion using a Sparse Probabilistic Spin Logic Network

Probabilistic Spin Logic (PSL) is a recently proposed computational paradigm implemented using unstable stochastic units called probabilistic bits, or pbits. The stochastic nature of the pbits is responsible for its unique invertibility property. In this paper, we have exploited the invertibility property to complete a partial image. The digit image set consists of ten images of digits 0 to 9 of size $5 \times 3$ pixels for training the PSL network. Results show that the fully connected PSL (FC-PSL) network successfully recovers the unclamped pixels for all the digits. Moreover, we propose an area-efficient sparsely connected PSL (SC-PSL) network with ~ 42% of the original connections. First, we perform the sparsification on an FC-PSL network using the conventional weight pruning methods. Next, we propose a method to predict the SC-PSL network structure using mutual information based on the perspective of information theory.
HeapSafe: Securing Unprotected Heaps in RISC-V

RISC-V is a promising open-source architecture primarily targeted for embedded systems. Programs compiled using the RISC-V toolchain can run bare-metal on the system, and, as such, can be vulnerable to several memory corruption vulnerabilities. In this work, we present HeapSafe, a lightweight hardware assisted heap-buffer protection scheme to mitigate heap overflow and use-after-free vulnerabilities in a RISC-V SoC. The proposed scheme tags pointers associated with heap buffers with metadata indices and enforces tag propagation for commonly used pointer operations. The HeapSafe hardware is decoupled from the core and is designed as a configurable coprocessor and is responsible for validating the heap buffer accesses. Benchmark results show a 1.5X performance overhead and 1.59% area overhead, while being 22% faster than a software protection. We further implemented a HeapSafe-nb, an asynchronous validation design, which improves performance by 27% over the synchronous HeapSafe.
DeepQMLP: A Scalable Quantum-Classical Hybrid Deep Neural Network Architecture for Classification

Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks (e.g., classification/regression). The search for ideal QML models is an active research field. This includes identification of efficient classical-to-quantum data encoding scheme, construction of parametric quantum circuits (PQC) with optimal expressivity and entanglement capability, and efficient output decoding scheme to minimize the required number of measurements, to name a few. However, most of the empirical/numerical studies lack a clear path towards scalability. Any potential benefit observed in a simulated environment may diminish in practical applications due to the limitations of noisy quantum hardware (e.g., under decoherence, gate-errors, and crosstalk). We present a scalable quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired by classical deep neural network architectures. In DeepQMLP, stacked shallow Quantum Neural Network (QNN) models mimic the hidden layers of a classical feed-forward multi-layer perceptron network. Each QNN layer produces a new and potentially rich representation of the input data for the next layer. This new representation can be tuned by the parameters of the circuit. Shallow QNN models experience less decoherence, gate errors, etc. which make them (and the network) more resilient to quantum noise. We present numerical studies on a variety of classification problems to show the trainability of DeepQMLP. We also show that DeepQMLP performs reasonably well on unseen data and exhibits greater resilience to noise over QNN models that use a deep quantum circuit. DeepQMLP provided up to 25.3% lower loss and 7.92% higher accuracy during inference under noise than QMLP.
Paper Beats Rock: Elaborating the Best Machine Learning Classifier for Hand Gesture Recognition

More and more digital experiences, such as Serious Games, rely on gesture control as a means of natural human communication. Therefore we investigated the suitability of the Senso Glove: DK2 data glove and a Support Vector Machine for recognizing static hand gestures of the popular game Rock-Paper-Scissors in a previous work. Building on this, we now want to increase the scope of training and testing data and evaluate different kinds of Machine-Learning classifiers in addition to the Support Vector Machine. For this purpose, we ingested two different datasets, optimized them using grid search, and evaluated all user data in such a way that each user dataset was used individually for testing (leave-one-out) in order to obtain the most possible representative and user-independent result. Our results show that for a small number of gestures, Logistic Regression has the highest accuracy (97.6%) in predicting the results quickly. For a larger dataset, Random Forest achieves the highest accuracy (82.4%). Random Forest and Logistic Regression give very good results on both datasets (average 89.7%, both), but Logistic Regression is significantly faster overall. If the training and test data are not separated by user and are thus user-dependent, the results for both data sets improve to 99.2% and 99.5% with Support Vector Machine, respectively, and again Random Forest performs very well, with Logistic Regression showing small weaknesses here. In addition, we investigated how the accuracy of the classifiers performed when we gradually reduced the number of gestures from 25 to three in a dataset with 25 gestures and found that up to 11 gestures, a high accuracy of more than 94% could be achieved. The data recorded in the course of this work are public available in https://github.com/serious-games-darmstadt/dataglove_senso-glove-dk2_rps-gestures (Last visited on 30 April 2022).
Drawing Attention on (Visually) Competitive Online Shopping Platforms – An Eye-Tracking Study Analysing the Effects of Visual Cues on the Amazon Marketplace

Amazon has become the market leader among online shopping platforms. Potential customers can search for products on Amazon and compare different offers. However, the highly (visually) competitive marketplace can make it difficult for sellers to stand out from the crowd. In an eye-tracking experiment, we investigate how visual cues (e.g. "Bestseller" badge) influence customers' behaviour by attracting attention, and how they affect their product choice. The experiment with a sample size of N = 60 participants was conducted on a German university campus. With the obtained eye-tracking data, we use a lognormal mixed-effects model and perform a logistic regression for estimating the effects of visual cues on Amazon search pages. The results indicate that visual cues marginally influence the viewing duration and decision time of customers but can have a considerable impact on the product choice.
Infrared Image Deblurring Based on Lp-Pseudo-Norm and High-Order Overlapping Group Sparsity Regularization

A traditional total variation (TV) model for infrared image deblurring amid salt-and-pepper noise produces a severe staircase effect. A TV model with low-order overlapping group sparsity (LOGS) suppresses this effect; however, it considers only the prior information of the low-order gradient of the image. This study proposes an image-deblurring model (Lp_HOGS) based on the LOGS model to mine the high-order prior information of an infrared (IR) image amid salt-and-pepper noise. An Lp-pseudo-norm was used to model the salt-and-pepper noise and obtain a more accurate noise model. Simultaneously, the second-order total variation regular term with overlapping group sparsity was introduced into the proposed model to further mine the high-order prior information of the image and preserve the additional image details. The proposed model uses the alternating direction method of multipliers to solve the problem and obtains the optimal solution of the overall model by solving the optimal solution of several simple decoupled subproblems. Experimental results show that the model has better subjective and objective performance than Lp_LOGS and other advanced models, especially when eliminating motion blur.
Optimization of Residential Landscape Design and Supply Chain System Using Intelligent Fuzzy Cognitive Map and Genetic Algorithm

This work intends to optimize residential landscape design and Supply Chain (SC) network systems. First, Fuzzy Cognitive Map (FCM) intelligent assistance and genetic algorithm (GA) are used to study residential landscape design and its integration with SC deeply. Weight matrix interactions are employed to implement iterative inference for FCM. The functions are transformed to unify variables of different scopes. Subsequently, a weighting method is proposed to deal with the disadvantage of the simple average method being too general. In addition, the Hebbian learning algorithm is used to adjust the state nodes and the connection weights. Finally, according to the fitness function of the GA and logistic regression (LR) model, residential landscape design and SC are combined. The simulation experiment results show that the causal relationship analysis between SC networks under fuzzy cognition shows that the state errors of each specific situation are 0.21, 0.16, and 0.24, respectively. The total average error is 0.21 in the case of multiple iterations. The average error of the result vector under fuzzy cognition and the operation of the actual result is 0.20, 0.15, and 0.24, respectively, and the error value is much reduced. The simulation accuracy of the GA-LR method for residential landscape design is improved from 77% to 84.7%. The "kappa coefficient" is also improved to 82.3%. The conclusion shows that the weight matrix is used to analyze the high-quality performance of landscape design according to the specific situation of SC. For each specific case, FCM is effective in reducing errors over multiple iterations. Under the GA-LR method, fewer geographic location types and larger accuracy deviations can improve the simulation accuracy.
A Lightweight Vision-Based Measurement for Hand Gesture Information Acquisition

The utilization of hand gesture recognition (HGR) can achieve a simpler and more humanized human–computer interaction (HCI) system, which can be used to simplify some aspects of production life and can also be used for sign language interpretation robots. However, HGR has a highly customized design. It is a very time-consuming process to design and train a separate model for each set of datasets. Therefore, designing a hand gesture information (HGI) acquisition instrument to analyze the rigid body motion and internal posture of the hand at the same time is a necessary process. This article presents a lightweight vision-based measurement (VBM) system to achieve this function. The system is implemented based on the convolutional neural network (CNN) structure, the improved Kalman filter (KF) is used to compensate error of fast moving, and the testing effect is good. After testing, the improved hand position detection system has the highest recall rate in the single-stage network. Compared with the original you look only once (YOLO)-v3, the number of parameters is reduced by 46.7M, but the recall rate is increased by 0.28%, which solves the loss of hand detection under complex background. With the help of the improved KF algorithm, the average precision (AP) metric of the prediction box is improved by 2.0 compared with the previous one. Compared with the simple baseline, the area under curve (AUC) metric of the keypoints detector is improved by 0.5%.
C+Ref-UNet: A Novel Approach for Medical Image Segmentation Based on Multi-Scale Connected UNet and CBAM

Background and Objective: In recent years, segmentation details and computing efficiency have become more important in medical image segmentation for clinical applications. UNet is one of the most commonly used models in image segmentation. UNet 3+ was designed as a modified UNet by adopting the architecture of full-scale skip connections. However, full-scale feature fusion can result in excessively redundant computations. This study aimed to reduce the network parameters of UNet 3+ while further improving the feature extraction capability.Methods: To improve the computational efficiency, we propose an optimized multi-scale skip connections segmentation model named Ref-UNet. In addition, we use the attention module called Convolutional Block Attention Module (CBAM) to capture more essential features and thus improve the feature expression capabilities. The model is named C+Ref-UNet.Results: The performance of the proposed models are validated by three different types of datasets: skin cancer segmentation, breast cancer segmentation, and lung segmentation. The parameters are reduced by about 36% and 18% compared to UNet and UNet3+, respectively. These results imply that the segmentation performance of Ref-UNet does not degrade compared to UNet 3+, and C+Ref-UNet not only outperformed the comparison models in a variety of evaluation metrics but also achieved more accurate segmentation results.Conclusions: The proposed models have lower network parameters, which enhance feature extraction and improve segmentation performance efficiently. Furthermore, the models have great potential for application in medical imaging computer-aided diagnosis.
Multi-Depth Deep Similarity Learning for Person Re-Identification

Detecting same people in different surveillance cameras, named person re-identification, has become a challenging and critical task in image processing. Since surveillance images usually have low resolution and different viewpoints, matching persons on them is still difficult. In this paper, a proposed method for person re-identification is introduced based on exploring similarity in different depth layers of convolutional neural network (CNN). To this end, after determining each person as a category for training CNN, optimum filters are obtained to find the best discriminative feature maps based on them. Smoothed discriminative features (SDF) are defined to compute similarity between persons. Experimental results, performed on CUHK01 database, demonstrate that the proposed method outperforms state-of-the-art feature extraction methods for person re-identification.
Spectral estimation for Hamiltonians: a comparison between classical imaginary-time evolution and quantum real-time evolution

We present a classical Monte Carlo (MC) scheme which efficiently estimates an imaginary-time, decaying signal for stoquastic (i.e. sign-problem-free) local Hamiltonians. The decay rates in this signal correspond to Hamiltonian eigenvalues (with associated eigenstates present in an input state) and can be classically extracted using a classical signal processing method like ESPRIT. We compare the efficiency of this MC scheme to its quantum counterpart in which one extracts eigenvalues of a general local Hamiltonian from a real-time, oscillatory signal obtained through quantum phase estimation circuits, again using the ESPRIT method. We prove that the ESPRIT method can resolve S = poly(n) eigenvalues, assuming a 1/poly(n) gap between them, with poly(n) quantum and classical effort through the quantum phase estimation circuits, assuming efficient preparation of the input state. We prove that our Monte Carlo scheme plus the ESPRIT method can resolve S = O(1) eigenvalues, assuming a 1/poly(n) gap between them, with poly(n) purely classical effort for stoquastic Hamiltonians, requiring some access structure to the input state. However, we also show that under these assumptions, i.e. S = O(1) eigenvalues, assuming a 1/poly(n) gap between them and some access structure to the input state, one can achieve this with poly(n) purely classical effort for general local Hamiltonians. These results thus quantify some opportunities and limitations of classical Monte Carlo methods for spectral estimation of Hamiltonians. We numerically compare the MC eigenvalue estimation scheme (for stoquastic Hamiltonians) and the QPE eigenvalue estimation scheme by implementing them for an archetypal stoquastic Hamiltonian system: the transverse field Ising chain.
Utility and Disclosure Risk for Differentially Private Synthetic Categorical Data

This paper introduces two methods of creating differentially private (DP) synthetic data that are now incorporated into the synth pop package for R. Both are suitable for synthesising categorical data, or numeric data grouped into categories. Ten data sets with varying characteristics were used to evaluate the methods. Measures of disclosiveness and of utility were defined and calculated. The first method is to add DP noise to a cross tabulation of all the variables and create synthetic data by a multinomial sample from the resulting probabilities. While this method certainly reduced disclosure risk, it did not provide synthetic data of adequate quality for any of the data sets. The other method is to create a set of noisy marginal distributions that are made to agree with each other with an iterative proportional fitting algorithm and then to use the fitted probabilities as above. This proved to provide useable synthetic data for most of these data sets at values of the differentially privacy parameter $$\epsilon $$ as low as 0.5. The relationship between the disclosure risk and $$\epsilon $$ is illustrated for each of the data sets. Results show how the trade-off between disclosiveness and data utility depend on the characteristics of the data sets.
Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively.
Learning Related-Variables According to Online Classroom Types that Influence the General English Speaking Classroom

The influence between the diverse learning variables affecting the academic achievement of learners according to class types should be studied extensively for the effectiveness of the Korean online general English classroom. Therefore, within the overall online non face-to-face class situation due to the prolonged COVID-19 pandemic, for 15 weeks 80 Korean university students experienced general English classes according to the non face-to-face class format. The purpose of this study was to investigate learner interactions (student-to-student vs. student-to-instructor), their motivation and satisfaction levels, and their English speaking achievement with a particular focus on lecture types (real-time Zoom classes vs. video-recorded classes). In addition, learning variables which can affect English speaking achievement and satisfaction were measured using a survey. By studying these variables, which have been insufficient so far, the intention was to provide some implications for the effective online class operation for the university general English classroom.&lt;/br&gt;Correlation analysis, t-test, and multiple linear regression were conducted, and the results were as follows: First, students’ interaction and academic achievements showed significant correlation and student-to-student and student-to-instructor interactions showed significant correlation. However, there was not any significant correlation between student-to-student interaction and their English speaking achievement. Second, significantly different lecture types had an impact on student interactions, their learning motivation, and academic achievements, but not on their level of satisfaction. Third, there were not any significant variables affecting course satisfaction, and student-to-instructor and student-to-student interactions had a significant effect on academic achievement. The 54.4% of English speaking achievement is explained through student-to-instructor and studentto-student interactions.&lt;/br&gt;Therefore, lecturers should take into regard the students’ diverse learning related-variables, which can affect the online English speaking class, and try to enhance the students’ satisfaction, motivation, and interaction levels. By doing so, we hope to maximize the educational effect of the online English speaking classes.