Reviewerly/exHarmony huggingface dataset:
Study on Dynamic Performance Parameters of Laser Tracker Based on Self-Developed Circular Trajectory Generator System

The laser tracker has characteristics of high measurement accuracy and wide measurement range. Laser tracker technology, as an effectively large-scale measuring approach, plays a critical role in dynamic measurement. Currently, the static performance of laser trackers has been well studied. However, the dynamic characteristics of the laser tracker remain unclear in terms of evaluating its dynamic performance. The circular trajectory generator measurement system can quantify the dynamic performance of the laser tracker. We developed a standard circular trajectory generator using a stable servo system and then conducted an in-depth study on the dynamic performance of the laser tracker through statistical analysis. Numerous experiments have shown that if the laser tracker is set at equal spacing, the dynamic indication error is smallest when the measurement distance is 3 m, indicating that the fitted diameter at a distance of 3 m is closest to the diameter of the circular trajectory generator. If the laser tracker is set with equal sampling frequency, the dynamic indication error is smallest when the measurement distance is 5 m. When the circular trajectory generator is at low speed, the measurement spacing of sampling points of the laser tracker is fixed proportional to the number of measurement points, while at low or high speed the sampling frequency of the laser tracker is fixed proportionally to the number of measurement points. These conclusions will facilitate the application of the laser tracker in dynamic measurement.
Multimodal AutoML via Representation Evolution

With the increasing amounts of available data, learning simultaneously from different types of inputs is becoming necessary to obtain robust and well-performing models. With the advent of representation learning in recent years, lower-dimensional vector-based representations have become available for both images and texts, while automating simultaneous learning from multiple modalities remains a challenging problem. This paper presents an AutoML (automated machine learning) approach to automated machine learning model configuration identification for data composed of two modalities: texts and images. The approach is based on the idea of representation evolution, the process of automatically amplifying heterogeneous representations across several modalities, optimized jointly with a collection of fast, well-regularized linear models. The proposed approach is benchmarked against 11 unimodal and multimodal (texts and images) approaches on four real-life benchmark datasets from different domains. It achieves competitive performance with minimal human effort and low computing requirements, enabling learning from multiple modalities in automated manner for a wider community of researchers.
Training Artificial Intelligence Algorithms with Automatically Labelled UAV Data from Physics-Based Simulation Software

Machine-learning (ML) requires human-labeled “truth” data to train and test. Acquiring and labeling this data can often be the most time-consuming and expensive part of developing trained models of convolutional neural networks (CNN). In this work, we show that an automated workflow using automatically labeled synthetic data can be used to drastically reduce the time and effort required to train a machine learning algorithm for detecting buildings in aerial imagery acquired with low-flying unmanned aerial vehicles. The MSU Autonomous Vehicle Simulator (MAVS) was used in this work, and the process for integrating MAVS into an automated workflow is presented in this work, along with results for building detection with real and simulated images.
Exploring Student Perceptions and Expectations of Cyber Security

Designing cyber security modules in Higher Education can be a balancing act. We aim to ensure students develop an understanding of key cyber security concepts such that they are able to contribute to security practices within the workplace. We also aim to develop understanding of more advanced and theoretical aspects of cyber security to meet a range of accreditation requirements and ensure those who wish to go on to work in security are suitably prepared. Additionally, students often have existing expectations and perceptions which must be managed. However, many general computing science degrees address security in isolated modules. As a result addressing these requirements can be challenging in the timeframe available. In this paper we present an activity designed explore student expectations of cyber security classes at two UK Universities in order to highlight the concerns of students such that curricula can consequently be amended to better meet student expectations.
Hyperbolic Timbre Embedding for Musical Instrument Sound Synthesis Based on Variational Autoencoders

In this paper, we propose a musical instrument sound synthesis (MISS) method based on a variational autoencoder (VAE) that has a hierarchy-inducing latent space for timbre. VAE-based MISS methods embed an input signal into a low-dimensional latent representation that captures the characteristics of the input. Adequately manipulating this representation leads to sound morphing and timbre replacement. Although most VAE-based MISS methods seek a disentangled representation of pitch and timbre, how to capture an underlying structure in timbre remains an open problem. To address this problem, we focus on the fact that musical instruments can be hierarchically classified on the basis of their physical mechanisms. Motivated by this hierarchy, we propose a VAE-based MISS method by introducing a hyperbolic space for timbre. The hyperbolic space can represent treelike data more efficiently than the Euclidean space owing to its exponential growth property. Results of experiments show that the proposed method provides an efficient latent representation of timbre compared with the method using the Euclidean space.
Replay Attack Detection Based on Voice and Non-voice Sections for Speaker Verification

Voice can represent a person's identity. Thus, it can be used in automatic speaker verification (ASV) systems for authenticating secure applications. Unfortunately, existing ASV systems are vulnerable to spoofing attacks. A replay attack is a widely used spoofing technique because it is simple but difficult to detect. Hence, many methods are proposed for countermeasures against replay attacks. Most work inseparably considers voice and non-voice sections in the detection's performance. In this work, we investigate the spoof detection performances when the voice, non-voice, and both with different percentages of voice are used to obtain the optimal section. We also propose a method for detecting replay attacks using the optimal section of a signal. Mel-frequency cepstral coefficients are calculated from the optimal section as a feature, and the ResNet-34 model is used for classification. We evaluated the proposed method using a dataset from the ASVspoof 2019 challenge. The results depict that the optimal section for replay attack detection is when 10% and 20% of voice are included in the non-voice sections. It also showed that the proposed method outperforms the baselines with a 7.52% relatively improvement or an equal error rate of 1.72%.
Gated fusion of handcrafted and deep features for robust automatic pronunciation assessment

Automatic pronunciation assessment plays an important role in Computer-Assisted Pronunciation Training (CAPT). Traditional methods utilize handcrafted speech features such as Goodness of pronunciation (GOP), which may not provide sufficient information for the pronunciation proficiency assess-ment. The deep feature-based method suffers from overfitting due to data scarcity and large variations in pronunciation. In this paper, we propose a method for automatic pronunciation assessment by fusing both handcrafted and deep features to boost the robustness of feature representations. To better model the complementary relationship between GOP-based and deep features, we fuse the GOP-based features with deep features based on concatenation or addition with multiplicative or additive gated fusion, respectively. Experimental results based on the dataset recorded by Chinese English-as-second-Ianguage (ESL) learners and the Speechocean762 dataset demonstrate that the proposed method outperforms the previous work based on either GOP or deep features in the Pearson correlation coefficient (PCC).
Smart Scissor

Scaling down the resolution of input images can greatly reduce the computational overhead of convolutional neural networks (CNNs), which is promising for edge AI. However, as an image usually contains much spatial redundancy, e.g., background pixels, directly shrinking the whole image will lose important features of the foreground object and lead to severe accuracy degradation. In this paper, we propose a dynamic image cropping framework to reduce the spatial redundancy by accurately cropping the foreground object from images. To achieve the instance-aware fine cropping, we introduce a lightweight foreground predictor to efficiently localize and crop the foreground of an image. The finely cropped images can be correctly recognized even at a small resolution. Meanwhile, computational redundancy also exists in CNN architectures. To pursue higher execution efficiency on resource-constrained embedded devices, we also propose a compound shrinking strategy to coordinately compress the three dimensions (depth, width, resolution) of CNNs. Eventually, we seamlessly combine the proposed dynamic image cropping and compound shrinking into a unified compression framework, Smart Scissor, which is expected to significantly reduce the computational overhead of CNNs while still maintaining high accuracy. Experiments on ImageNet-1K demonstrate that our method reduces the computational cost of ResNet50 by 41.5% while improving the top-1 accuracy by 0.3%. Moreover, compared to HRank, the state-of-the-art CNN compression framework, our method achieves 4.1% higher top-1 accuracy at the same computational cost. The codes and data are available at https://github.com/ntuliuteam/smart-scissor
Physics-Aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks

Diffractive optical neural networks (DONNs) have attracted lots of attention as they bring significant advantages in terms of power efficiency, parallelism, and computational speed compared with conventional deep neural networks (DNNs), which have intrinsic limitations when implemented on digital platforms. However, inversely mapping algorithm-trained physical model parameters onto real-world optical devices with discrete values is a non-trivial task as existing optical devices have non-unified discrete levels and non-monotonic properties. This work proposes a novel device-to-system hardware-software codesign framework, which enables efficient physics-aware training of DONNs w.r.t arbitrary experimental measured optical devices across layers. Specifically, Gumbel-Softmax is employed to enable differentiable discrete mapping from real-world device parameters into the forward function of DONNs, where the physical parameters in DONNs can be trained by simply minimizing the loss function of the ML task. The results have demonstrated that our proposed framework offers significant advantages over conventional quantization-based methods, especially with low-precision optical devices. Finally, the proposed algorithm is fully verified with physical experimental optical systems in low-precision settings.
Fast and Compact Interleaved Modular Multiplication Based on Carry Save Addition

Improving fully homomorphic encryption computation by designing specialized hardware is an active topic of research. The most prominent encryption schemes operate on long polynomials requiring many concurrent modular multiplications of very big numbers. Thus, it is crucial to use many small and efficient multipliers. Interleaved and Montgomery iterative multipliers are the best candidates for the task. Interleaved designs, however, suffer from longer latency as they require a number comparison within each iteration; Montgomery designs, on the other hand, need extra conversion of the operands or the result. In this work, we propose a novel hardware design that combines the best of both worlds: Exhibiting the carry save addition of Montgomery designs without the need for any domain conversions. Experimental results demonstrate improved latency-area product efficiency by up to 47% when compared to the standard Interleaved multiplier for large arithmetic word sizes.
WSQ-AdderNet

Convolutional neural networks (CNNs) have been widely adopted for various machine intelligence tasks. Nevertheless, CNNs are still known to be computational demanding due to the convolutional kernels involving expensive Multiply-ACcumulate (MAC) operations. Recent proposals on hardware-optimal neural network architectures suggest that AdderNet with a lightweight ℓ1-norm based feature extraction kernel can be an efficient alternative to the CNN counterpart, where the expensive MAC operations are substituted with efficient Sum-of-Absolute-Difference (SAD) operations. Nevertheless, it lacks an efficient hardware implementation methodology for AdderNet as compared to the existing methodologies for CNNs, including efficient quantization, full-integer accelerator implementation, and judicious resource utilization of DSP slices of FPGA devices. In this paper, we present WSQ-AdderNet, a generic framework to quantize and optimize AdderNet-based accelerator designs on embedded FPGA devices. First, we propose a weight standardization technique to facilitate weight quantization in AdderNet. Second, we demonstrate a full-integer quantization hardware implementation strategy, including weight and activation quantization methodologies. Third, we apply DSP packing optimization to maximize the DSP utilization efficiency, where Octo-INT8 can be achieved via DSP-LUT co-packing. Finally, we implement the design using Xilinx Vitis HLS (high-level synthesis) and Vivado to Xilinx Kria KV-260 FPGA. Our experimental results of ResNet-20 using WSQ-AdderNet demonstrate that the implementations achieve 89.9% inference accuracy with INT8 implementation, which shows little performance loss as compared to the FP32 and INT8 CNN designs. At the hardware level, WSQ-AdderNet achieves up to 3.39× DSP density improvement with nearly the same throughput as compared to INT8 CNN design. The reduction in DSP utilization makes it possible to deploy large network models on resource-constrained devices. When further scaling up the PE sizes by 39.8%, WSQ-AdderNet can achieve 1.48× throughput improvement while still achieving 2.42× DSP density improvement.
CASU

Guaranteeing runtime integrity of embedded system software is an open problem. Trade-offs between security and other priorities (e.g., cost or performance) are inherent, and resolving them is both challenging and important. The proliferation of runtime attacks that introduce malicious code (e.g., by injection) into embedded devices has prompted a range of mitigation techniques. One popular approach is Remote Attestation (RA), whereby a trusted entity (verifier) checks the current software state of an untrusted remote device (prover). RA yields a timely authenticated snapshot of prover state that verifier uses to decide whether an attack occurred.
TorchQuantum Case Study for Robust Quantum Circuits

Quantum Computing has attracted much research attention because of its potential to achieve fundamental speed and efficiency improvements in various domains. Among different quantum algorithms, Parameterized Quantum Circuits (PQC) for Quantum Machine Learning (QML) show promises to realize quantum advantages on the current Noisy Intermediate-Scale Quantum (NISQ) Machines. Therefore, to facilitate the QML and PQC research, a recent python library called TorchQuantum has been released. It can construct, simulate, and train PQC for machine learning tasks with high speed and convenient debugging supports. Besides quantum for ML, we want to raise the community's attention on the reversed direction: ML for quantum. Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency.
YOLO-Based Deep-Learning Gaze Estimation Technology by Combining Geometric Feature and Appearance Based Technologies for Smart Advertising Displays

In this study, a YOLO-based deep-learning gaze estimation technology is developed for the application of non-contact smart advertising displays. By integrating the appearance and geometric-features technologies, the output coordinates of facial features inferred by YOLOv3-tiny based models can provide the training data for gaze estimation without the calibration process. In experiments, the input size of YOLOv3-tiny based models is arranged by 608x608 pixels, and the used models have good location performance to detect the facial directions and two facial features. By the YOLOv3-tiny based model with the cross-person test, the proposed method performs the averaged gaze estimation accuracies of nine, six, and four-block modes are 66.38%, 80.87%, 88.34%, respectively with no calibration process.
Towards Physically Adversarial Intelligent Networks (PAINs) for Safer Self-Driving

Automated vehicles' neural networks suffer from overfit, poor generalizability, and untrained edge cases due to limited data availability. Researchers synthesize randomized edge-case scenarios to assist in the training process, though simulation introduces potential for overfit to latent rules and features. Automating worst-case scenario generation could yield informative data for improving self driving. To this end, we introduce a "Physically Adversarial Intelligent Network" (PAIN), wherein self-driving vehicles interact aggressively in the CARLA simulation environment. We train two agents, a protagonist and an adversary, using dueling double deep Q networks (DDDQNs) with prioritized experience replay. The coupled networks alternately seek-to-collide and to avoid collisions such that the "defensive" avoidance algorithm increases the mean-time-to-failure and distance traveled under non-hostile operating conditions. The trained protagonist becomes more resilient to environmental uncertainty and less prone to corner case failures resulting in collisions than the agent trained without an adversary.
FF-MR: A DoH-Encrypted DNS Covert Channel Detection Method Based on Feature Fusion

In this paper, in order to accurately detect Domain Name System (DNS) covert channels based on DNS over HTTPS (DoH) encryption and to solve the problems of weak single-feature differentiation and poor performance in the existing detection methods, we have designed a DoH-encrypted DNS covert channel detection method based on features fusion, called FF-MR. FF-MR is based on a Multi-Head Attention and Residual Neural Network. It fuses session statistical features with multi-channel session byte sequence features. Some important features that play a key role in the detection task are screened out of the fused features through the calculation of the Multi-Head Attention mechanism. Finally, a Multi-Layer Perceptron (MLP) is used to detect encrypted DNS covert channels. By considering both global and focused features, the main idea of FF-MR is that the degree of correlation between each feature and all other features is expressed as an attention weight. Thus, features are re-represented as the result of the weighted fusion of all features using the Multi-Head Attention mechanism. Focusing on certain important features according to the distribution of attention weights improves the detection performance. While detecting the traffic in encrypted DNS covert channels, FF-MR can also accurately identify encrypted traffic generated by the three DNS covert channel tools. Experiments on the CIRA-CIC-DoHBrw-2020 dataset show that the macro-averaging recall and precision of the FF-MR method reach 99.73% and 99.72%, respectively, and the macro-averaging F1-Score reached 0.9978, which is up to 4.56% higher than the existing methods compared in the paper. FF-MR achieves at most an 11.32% improvement in macro-averaging F1-Score in identifying three encrypted DNS covert channels, indicating that FF-MR has a strong ability to detect and identify DoH-encrypted DNS covert channels.
A GAN-BERT Based Approach for Bengali Text Classification with a Few Labeled Examples

Basic machine learning algorithms or transfer learning models work well for language categorization, but these models require a vast volume of annotated data. We need a better model to tackle the problem because labeled data is scarce. This problem may have a solution in GAN-BERT. To classify Bengali text, we have developed a GAN-BERT based model, which is an adapted version of BERT. We have used two different datasets for this purpose. One is a hate speech dataset, while the other is a fake news dataset. To understand how the GAN-BERT and traditional BERT models behave with Bangla datasets, we have experimented with both. With a small quantity of data, we are able to get a satisfactory result using GAN-BERT. We have also demonstrated how the accuracy increases as the number of training samples increases. A comparison of performance between traditional BERT based Bangla-BERT and our GAN-Bangla-BERT model is also shown here, where we can see how these models react to a small number of labeled data.
Cognitive Cloud: The Definition

Cognitive Cloud has drawn increasing attention from practitioners, academics, and funding agencies and has been adopted progressively. However, the concept remains mired in various definitions with different studies providing contrasting descriptions. Therefore, to understand the concept of cognitive cloud and to provide its definition, in this work we conduct a systematic mapping study of the literature investigating 24 papers proposing five main definitions. The main outcome of this work is a complete definition that merges all the common aspects of cognitive cloud, enabling practitioners and researchers to better understand what cognitive cloud is.