Reviewerly/exHarmony huggingface dataset:
Application and Evaluation of Quantization for Narrow Bit-width Resampling of Sequential Monte Carlo

As a preliminary step to reduce the resampling circuit size of the Sequence Monte Carlo (SMC) method, an evaluation of convergence feasibility by preprocessing is presented in this paper. SMC is a powerful numerical algorithm widely applied to Bayesian estimation and Kalman filter. However, SMC is computationally expensive and difficult to apply to huge amounts of data. Several previous studies have already reported methods to accelerate SMC using GPGPUs and FPGAs. In this study, we attempted to find a threshold that converges even with a narrow register width by quantizing the weights used for resampling through various pre-processing methods. Experimental results showed that with pre-processing, 8-bit unsigned integer can achieve the same performance as 32-bit floating point.
A Faster Procedure for Estimating SEMs Applying Minimum Distance Estimators With a Fixed Weight Matrix

This study presents a separable nonlinear least squares (SNLLS) implementation of the minimum distance (MD) estimator employing a fixed-weight matrix for estimating structural equation models (SEMs). In contrast to the standard implementation of the MD estimator, in which the complete set of parameters is estimated using nonlinear optimization, the SNLLS implementation allows a subset of parameters to be estimated using (linear) least squares (LS). The SNLLS implementation possesses a number of benefits, such as faster convergence, better performance in ill-conditioned estimation problems, and fewer required starting values. The present work demonstrates that SNLLS, when applied to SEM estimation problems, significantly reduces the estimation time. Reduced estimation time makes SNLLS particularly useful in applications involving some form of resampling, such as simulation and bootstrapping.
Data-Centric Machine Learning Pipeline for Hardware Verification

In hardware verification, constrained random testing is used to cover the vast verification space efficiently. Verification engineers adjust testbench settings to guide test behavior. Since this method produces many tests, we can train a machine learning (ML) model with test outcome to identify tests with high probability of having bugs, leading to increase in testing efficiency. However, maintaining good model performance after deployment is challenging. The data types are heterogeneous, change over time, and have multiple interpretations. I developed a data-centric ML pipeline that preprocesses custom verification-specific data types automatically and enables tuning data preprocessing methods ("data tuning"). This ML pipeline is adaptive to changes in future data and thus increases deployment scalability and robustness. Real-world benchmark testing shows that data tuning with this pipeline improves model performance.
INAS: Incremental Neural Architecture Search

Sharing the weights of Super-Net is a common method to improve the efficiency of model performance evaluation in Neural architecture search (NAS). Existing weight-sharing NAS typically uses a fixed Super-Net to be search space, but the Super-Net design relies on the valuable experience of experts for different tasks. When the training dataset or metric is changed, the expert has to design again to obtain optimized architecture. Therefore, we argue that dynamic Super-Net is beneficial to discover promising architecture with less manual intervention during the search stage. Furthermore, we observed that most evolved NAS algorithms ignore the potential connection between the input node and neural operator. This paper describes an incremental neural architecture search (INAS) method that searches for an outstanding neural network architecture by dynamically increasing Super-Net size. And we proposed an improved Heredity Algorithm (HA) as a search strategy, in which overwriting operator is used to process input node and neural operator coding. The overwriting operator can further preserve the outstanding individual genes to offspring and accelerate search convergence. Finally, we performed the experiment on CIFARIO. The search model from our INAS method achieves the highest accuracy 97.97%, beyond the state-of-the-art methods. At the same time, we retrained the same architecture on CIFARIOO and obtained 86.17% accuracy on the test set, which proves that the architecture obtained by the algorithm has good generalization ability.
Post-Processing Refinement for Semi-Global Matching Algorithm Based on Real-Time FPGA

The Semi-Global Matching (SGM) algorithms and their hardware accelerators, which emphasize stereo matching rather than occlusion filling, have been developed in the last few years. However, filling occlusions is indispensable for many real-world applications. This work presents a pixel-level pipeline architecture for the post-processing of SGM, which refines disparity through a left-right check, and multi-directional occlusion filling refinement. The hardware architecture based on optimization algorithms is on the Stratix-IV platform, and it consumes about 5720 LUTs, 12961 registers, and 2.15M bits of on-chip memory. The maximum working frequency can reach up to 95.15 MHz for the 640×480 resolution video and 128 disparity range with the power dissipation of 1.46 W and 320 frames per second processing speed.
Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework

With the surge of the number of cars, road traffic accidents occur frequently because of drivers’ distracted attention and abnormal behaviors, which causes huge losses to people’s lives and property. To alleviate this issue, an improved deep learning algorithm based on YOLOX framework was proposed in this research to detect driving behavior changes in live. An attention mechanism – Convolutional Block Attention Module (CBAM) - was introduced in multiple scales of feature layers to form the backbone of YOLOX network. A widely used data science competition platform was adopted for distracted behavior model training. The State Farm Distracted Driver Detection Dataset was used for model validation and performance benchmarking. Experimental results have indicated promising performance gain using the devised model over the original YOLOX framework in terms of mAP and inference time.
OISSR: Optical Image Stabilization Based Super Resolution on Smartphone Cameras

Multi-frame super-resolution methods can generate high resolution images by combining multiple captures of the same scene; however, the performance of merged results are susceptible to degradation due to a lack of precision in image registration. In this study, we sought to develop a robust multi-frame super resolution method (called OISSR) for use on smartphone cameras with a optical image stabilizer (OIS). Acoustic injection is used to alter the readings from the built-in MEMS gyroscope to control the lens motion in the OIS module (note that the image sensor is fixed). We employ a priori knowledge of the induced lens motion to facilitate optical flow estimation with sub-pixel accuracy, and the output high-precision pixel alignment vectors are utilized to merge the multiple frames to reconstruct the final super resolution image. Extensive experiments on a OISSR prototype implemented on a Xiaomi 10Ultra demonstrate the high performance and effectiveness of the proposed system in obtaining the quadruple enhanced resolution imaging.
Performance Analysis of Naïve Bayes Classifier Algorithm with Chi-Square and Confix Stripping Stemmer Selection Features In Hadits Translation Classification System

Hadith is the second source of Islamic law after the Qur'an. Hadith classification supports the authentication of a hadith. The hadith classification process is carried out by the computer research community, while hadith authentication is carried out by hadith scholars. Text classification is grouping a certain text into one of the classes given a category based on the content of the text. One of the text mining classification algorithms is Naïve Bayes Classifier. In this study, researchers simulated a combination of the Naïve Bayes Classifier algorithm with Confix Stripping Stemmer for stemming, and Chi-Square as a feature selection algorithm to classify the translation of Sahih Bukhari's hadith into three classes, namely suggestions, prohibitions, and information with 300 data training data, and 30 testing data. System performance testing uses a confusion matrix by calculating the value of accuracy, precision, recall, and f-measure. The results show the best performance is a combination of Naïve Bayes Classifier and Confix Stripping Stemmer.
PRNG Using Primitive Roots of Primes and its Utilization in Chess-based Image Encryption

Recently, number theory has proved its importance in cryptography because of its well-known hard problems. For instance, a primitive root for a prime number shows a special property of uniqueness when raised to different powers mod the prime number. In this paper, a Pseudorandom Number Generator (PRNG) is designed based on this property using a prime number and some of its primitive roots. The PRNG is, first, validated for utilization in cryptography applications using histograms, correlation coefficients, and the National Institute of Standards and Technology (NIST) statistical test suite. Then, the PRNG is utilized in an image encryption system and the system security is tested using statistical measures, differential attack measures, and sensitivity to one-bit change. The results are promising and in the expected good ranges.
Constrained Iterative Nonlinear Optimization for Robot Control Applications

Numerical optimization provides a computational tool widely used to control robotic systems subject to constraints during their motion. However, many of the methods used to solve these problems lack treatment of physical constraints i.e., limitations on the control inputs and constraints on the temporal aspect of the motion, common in practical application. Here we present a computational method to nonlinear dynamic optimization which enables us to take inequality constraints on the control commands and the simultaneously optimized task duration rigorously into account. The presented approach uses state augmentation to reduce the original free time horizon optimization to a fixed time horizon problem. Following this transformation we derive a minimalistic constraint linear-quadratic sub-problem which is iteratively solved to find the solution of the original constrained nonlinear dynamic optimization problem. The proposed approach is used to solve high-dimensional test problems in simulation, a low-dimensional, non-convex robot control problem tested in an feedback control experiment and a high-dimensional, unstable robot planning problem in simulation. These examples indicate high accuracy, demonstrate scalability, and suggest wide range applicability of the proposed approach.
Scale-flow

This paper addresses the problem of normalized scene flow (NSF): given a pair of RGB video frames, estimating the 3D motion, which consisted of optical flow and motion-in-depth estimation. NSF is a powerful tool for action prediction and autonomous robot navigation, presenting the advantage of only needing a monocular and uncalibrated camera. However, most existing methods directly regress motion-in-depth from two RGB frames or optical flow, resulting in sub-accurate and non-robust results. Our key insight is the scale matching scheme-establishing correlations between two frames containing objects in different scales, to estimate dense and continuous motion-in-depth. Based on the scale matching, we propose a unified framework: Scale-flow, which combines scale matching and optical flow estimation. This combination makes optical flow estimation can use dense and continuous scale information for the first time, so that the moving foreground objects can be estimated more accurately. On KITTI, our monocular approach achieves the lowest error in the foreground scene flow task, even compared with the multi-camera method. Moreover, on the motion-in-depth estimation task, Scale-flow reduces the error by 34% compared with the best-published method. Code will be available.
Segmentation of Broken Khmer Characters

Preprocessing is the first step in handwritten characters recognition. Segmentation is an important preprocessing step for separate group of words into single character. Broken character is one of the most difficult segmentation cases which arise when handwritten characters are being segmented. Therefore, this paper focuses on the segmentation of broken characters. The proposed character segmentation workflow consists of finding the middle layer of words by the projection analysis and bounding box analysis which is initially employed to segment the document image into images of isolated characters and images of broken characters. The thinning algorithm is then applied to extract the skeleton of the characters in middle layer and file loop, junction point, vertex shape and straight line by chain code algorithm. Finally, the rules for combining characters from the aspect ratio value (height/width), together with the separated pieces of the broken Khmer characters, are put back to reconstruct one isolated characters. The proposed algorithm achieves an accuracy of 79.5%.
Safe, efficient waypoint manipulation for path planning of non-holonomic robots

During the pandemic, health and safety concerns kept people indoors and increased the demand for same-day delivery. This put a huge stress on the e-commerce industry. Adoption of warehouse automation skyrocketed, leading to an increased emphasis on efficiency. In warehouses where large industrial robots like forklifts are deployed, efficiency and safety, both are important. Conventional methods like Reeds-Shepp (RS) that consider the motion model are often sub-optimal as they do not consider obstacles, while those that are optimal like Dijkstra and A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> are difficult to implement due to kinematic constraints. This paper proposes a hierarchical structure consisting of an A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> -based global planner which goes through a pre-processing step. The local planner then generates a Reeds-Shepp curve to a waypoint chosen using a ”cost” parameter.
Multi-Task Learning for Time Series Forecasting Using NARMAX-LSTM

Traditional time series forecasting models based on neural networks are usually designed for a single task, which ignores the source of knowledge or information from other related tasks. On the other hand, multitask learning (MTL) offers a new perspective where one model is not only for one forecasting case or task but several tasks at the same time. The MTL model proposed in this study is based on the Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) model and Long-Short Term Memory (LSTM) Neural Networks. The NARMAX-LSTM model is tested and validated using three different time horizons and different cases/tasks. The model forecasting performance is compared with that of three other linear and nonlinear models. The comparison results show that the multi-task NARMAX-LSTM model does not only have better prediction performance but also avoid suffering from negative transfer and overfitting.
Heterogeneous Recognition of Human Activity with CNN and RNN-based Networks using Smartphone and Smartwatch Sensors

With ongoing advancements in remote sensor technologies, the concept of wearable sensors has gained widespread acceptance and grown ubiquitous due to its broad applicability in situations including ambient assisted living, innovative healthcare, and home appliances. In this context, the demand for Heterogeneous Human Activity Recognition (HHAR) has expanded considerably, as individuals desire their daily activities to be collected and analyzed to provide valuable information. This wearable sensing data is taken in the form of time-series data utilizing different wearable sensors, such as accelerometers, gyroscopes, and magnetometers, to offer other movement characteristics for each individual tracked from their smartphones and smartwatches. This data may be analyzed using different deep learning architectures to provide an additional platform for capturing and classifying the numerous sensor-based actions an individual could be undertaking. This research compares HHAR models based on deep learning techniques. We study the influence of smartphone and wristwatch sensor data on model performance and classification of human activities. We train and evaluate deep learning models to achieve our study objective using a publically available standard dataset (HHAR) with a 5-fold cross-validation technique. Using smartphone sensors, research findings demonstrate that the RNN-based model is superior to CNN-based models, with the most fantastic accuracy of 98.42%.
Attribute Guided Graph Convolutional Network with Stronger Generalization for Person Re-identification

Person re-identification(ReID) aims to match and track people in a surveillance system with non-overlapping camera views. It is a key challenge for person ReID to learn robust and discriminative person representations. However, in the real world, similar person appearance, different image angles and changing person attributes make the task very difficult. To tackle this problem, we propose the attribute guided graph convolutional networks(AG-GCN) to design a model with stronger generalization. Specifically, an attribute transfer module is introduced into the framework to revise person attributes to obtain diverse person feature expression. In addition, we apply graph convolutional networks to combine attributes with body parts as a more fine-grained representation of the person. The experiment results conducted on Market 1501 and DukeMTMCReID datasets show that our method outperforms state-of-the-art attribute-based methods on a single dataset and generalizes better on other datasets.
Analysis of surface code under the restricted number of CNOT gates

To date, the surface code has become a promising candidate for quantum error correcting codes because of its high threshold, nearest gate operations and low weight stabilizers. Here, we assume that the number of CNOT gates is restricted because of the quantum crosstalk due to residual inter-qubit coupling or max channels per pulse gate. We thus calculated the effective physical error rate in this case and determine the surface code distance accordingly. The optimal code distance was obtained based on the logical error rate performance estimation of the surface code for some T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> time.
Sensorformer: A Memory-efficient Transformer for Industrial Sensor Fusion

The deep learning model, Transformer, and its variants have achieved state-of-the-art performance in many domains, such as natural language processing, vision recognition, protein structure prediction, as well as industrial multi-sensor data processing tasks. However, the high memory usage and $O(N^{2})$ computational complexity of Transformer can result in the high cost and low efficiency in industrial scenarios, especially in the case of edge computing. To mitigate this problem, we propose a novel auto-decorrelation block based on the Fast Fourier Transform and the self-attention mechanism of Transformer to reduce memory occupation and computational complexity. Instead of removing the related data, this model merges the correlated channels progressively throughout the whole inference process. The proposed method was tested on a public dataset based on condition monitoring of a hydraulic system. Results demonstrated that our proposed method reduced memory usage to around one-fifth of its original size while maintaining similar inference accuracy. The inference time can also be 2 times faster. This allows the transformer to be used for industrial multi-sensor data processing tasks in a more resource-efficient and faster manner.
Flying Watchdog: A Drone with Edge AIoT for Residential Safety and Fall Detection by Face and Posture Recognition

The high accuracy of applying deep learning on image recognition induces a variety of useful applications amid which incorporating the maneuverability and mobility of drones or UAVs for real-time monitoring can further extend the coverage of surveillance into some areas that fixed cameras cannot be installed or high resolution image cannot be obtained. Therefore, this paper proposes a system named Flying Watchdog which applying deep learning to online recognition on the images extracted from the video recorded by a drone, the flying watchdog, which can recognize a target of interest and then automatically flies toward it for further inspection. The functions of our system, like a real watchdog, consists of two major themes: home security and autonomous company. The drone with a camera on board to record the real-time surveillance video is the terminal device in an Artificial Intelligence of Things (AIoT) system. Due to the limited computing capability of a drone, through a wireless network, the video then streaming back to a mobile phone or a laptop that acting as an Edge AI computing server to execute the required procedures such as target recognition, decision, adequate alarms as well as user notifications and the drone’s maneuver control for tracking targets. By the mature deep learning model, FaceNet, friends or family members can be accurately distinguished from strangers or intruders and by the model of OpenPose, falling down of people under guard will be immediately detected. Whenever such abnormalities are found, the Edge AI computing server will immediately send notification messages to the mobile App. on corresponding users’ (masters’) mobile phone. Furthermore, the drone, the flying watchdog, can autonomously track the people of interest such as a stranger for further inspection or warning or a person under guard for detecting the possible fall. The proposed system, Flying Watchdog, is well implemented and tested and is proved to be helpful to home security and a loyal, selfless, trustworthy companion to its human masters.
Dynamic Weighted Semantic Correspondence for Few-Shot Image Generative Adaptation

Few-shot image generative adaptation, which finetunes well-trained generative models on limited examples, is of practical importance. The main challenge is that the few-shot model easily becomes overfitting. It can be attributed to two aspects: the lack of sample diversity for the generator and the failure of fidelity discrimination for the discriminator. In this paper, we introduce two novel methods to solve the diversity and fidelity respectively. Concretely, we propose dynamic weighted semantic correspondence to keep the diversity for the generator, which benefits from the richness of samples generated by source models. To prevent discriminator overfitting, we propose coupled training paradigm across the source and target domains to keep the feature extraction capability of the discriminator backbone. Extensive experiments show that our method outperforms previous methods both on image quality and diversity significantly.
Security Analysis of One-step QSDC Protocol with Hyperentanglement

One-step quantum secure direct communication protocol using hyperentanglement enables senders to send 2-bit information by complete Bell state measurement scheme. However, the secrecy capacity suggested by previous research does not consider wiretap channel attack. In this paper, we suggest a new secrecy capacity that considers wiretap channel attack.