Reviewerly/exHarmony huggingface dataset:
Self-organizing Mobile Robots Swarm Movement Control Simulation

The control principle of mobile robots, self-organized in convoy structure, is considered. It is shown that in this case a high autonomy of swarm operation may be achieved, due to such parameters of motion, as swarm azimuth angle and velocity of movement may be set only for the master robot, while slave robots follow master one at the pre-determined distance. The flowchart of the swarm control system is worked out, according which all slave robots measure their own deviation from the direction on previous swarm unit and distance till it, and actuate mechanics of the robot for zeroing azimuth angles difference and setting pre-determined distance. Mathematical models of the swarm, as the united object under control, and of the distributed Von Neumann controller, which closes control system, are worked out. It is shown, that characteristic equation of closed system have complex exponent at the left side, which is due to time delays, born by Von Neumann type controllers. Method of evaluation of time delays, based on semi-Markov simulation of control algorithm, is proposed. Theoretical results are confirmed by modeling the motion control of a convoy, including pair mobile robots.
Discriminative Local Representation Learning for Cross-Modality Visible-Thermal Person Re-Identification

Visible-thermal person re-identification (VTReID) is a rising and challenging cross-modality retrieval task in intelligent video surveillance systems. Most attention architectures cannot explore the discriminative person representations for VTReID, especially in the thermal modality. In addition, the fine-grained middle-level semantic information has received much less attention in the part-based approaches for the cross-modality pedestrian retrieval task, resulting in limited generalization capability and poor representation robustness. This paper proposes a simple yet powerful discriminative local representation learning (DLRL) model to capture the robust local fine-grained feature representations and explore the rich semantic relationship between the learned part features. Specifically, an efficient contextual attention aggregation module (CAAM) is designed to strengthen the discriminative capability of the feature representations and explore the contextual cues for visible and thermal modalities. Then, an integrated middle-high feature learning (IMHF) method is introduced to capture the part-level salient representations, which handles the ambiguous modality discrepancy in both discriminative middle-level and robust high-level information. Moreover, a part-guided graph convolution module (PGCM) is constructed to mine the structural relationship among the part representations within each modality. The quantitative and qualitative experiments on the two benchmark datasets demonstrate that the proposed DLRL model significantly outperforms state-of-the-art methods and achieves rank-1/mAP accuracy of 92.77%/82.05% on the RegDB dataset and 63.04%/60.58% on the SYSU-MM01 dataset.
Design Eye-Tracking Augmented Reality Headset to Reduce Cognitive Load in Repetitive Parcel Scanning Task

Repetitive tasks widely exist in applied fields of human-computer interaction. One underestimated example is parcel scanning, which has consistent operation difficulty but comprises multiple processes (e.g., label seeking and scanning, result confirming, and parcel relocating), involving respective cognitive requirements. Many devices are developed to facilitate repetitive operations, but few are to reduce fluctuating cognitive load throughout task processes. We present the eye-tracking augmented reality headset that integrates foveated vision detection and smooth pursuit of eye tracking and investigate how it can reduce cognitive load in the repetitive task. In total, 33 participants completed a set of parcel scanning tasks with the headset and their visual and cognitive performance were assessed. The results show that the headset maintained high scanning efficiency and lower cognitive load across the tasks with varying difficulties and it significantly reduced the participants’ cognitive load during the processes of barcode seeking and scanning and result confirmation. The headset demonstrated good usability and ease of use. Implications for how the case study result could be used in generalizing applications are discussed.
Dynamic Temperature Scaling in Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition

The use of deep neural networks in sensor-based Human Activity Recognition has led to considerably improved recognition rates in comparison to more traditional techniques. Nonetheless, these improvements usually rely on collecting and annotating massive amounts of sensor data, a time-consuming and expensive task. In this paper, inspired by the impressive performance of Contrastive Learning approaches in Self-Supervised Learning settings, we introduce a novel method based on the SimCLR framework and a Transformer-like model. The proposed algorithm addresses the problem of negative pairs in SimCLR by using dynamic temperature scaling within a contrastive loss function. While the original SimCLR framework scales similarities between features of the augmented views by a constant temperature parameter, our method dynamically computes temperature values for scaling. Dynamic temperature is based on instance-level similarity values extracted by an additional model pre-trained on initial instances beforehand. The proposed approach demonstrates state-of-the-art performance on three widely used datasets in sensor-based HAR, namely MobiAct, UCI-HAR and USC-HAD. Moreover, it is more robust than the identical supervised models and models trained with constant temperature in semi-supervised and transfer learning scenarios.
Realization of a Real-Time Image Denoising System for Dashboard Camera Applications

Noise interference during the acquisition of digital images can severely degrade image quality, particularly for images captured under low-light conditions; however, the removal of image noise requires sophisticated digital image processing systems. This study presents a hardware-based solution to real-time image denoising using an existing algorithm designed for the removal of mixed impulse noise (salt-and-pepper and random-valued impulse noise), while preserving image edge details and image borders, without the need for additional computation time or memory capacity. Note that mixed impulse noise is typical of most real-world situations, such as the video noise associated with dashboard cameras. The proposed design was implemented using 180 nm complementary metal-oxide-semiconductor (CMOS) technology, consuming only 21.7 mW when operated at 200 MHz. This operating frequency allows the proposed chip to process noisy video streams with resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1920\mathbf {\times }1080$ </tex-math></inline-formula> at 60 frames per second in real time. In terms of image restoration, the proposed algorithm achieved image quality on par with that achieved using software simulation. We also demonstrated the efficacy of the proposed scheme in denoising noisy video images from a dashboard camera.
Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

While recommender systems are highly successful at helping users find relevant information online, they may also exhibit a certain undesired bias of mostly promoting only already popular items. Various approaches of quantifying and mitigating such biases were put forward in the literature. Most recently, calibration methods were proposed that aim to match the popularity of the recommended items with popularity preferences of individual users. In this paper, we show that while such methods are efficient in avoiding the recommendation of too popular items for some users, other techniques may be more effective in reducing the popularity bias on the platform level. Overall, our work highlights that in practice choices regarding metrics and algorithms have to be made with caution to ensure the desired effects.
Concretely efficient secure multi-party computation protocols: survey and more

Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on their private inputs, and reveals nothing but the output of the function. In the last decade, MPC has rapidly moved from a purely theoretical study to an object of practical interest, with a growing interest in practical applications such as privacy-preserving machine learning (PPML). In this paper, we comprehensively survey existing work on concretely efficient MPC protocols with both semi-honest and malicious security, in both dishonest-majority and honest-majority settings. We focus on considering the notion of security with abort, meaning that corrupted parties could prevent honest parties from receiving output after they receive output. We present high-level ideas of the basic and key approaches for designing different styles of MPC protocols and the crucial building blocks of MPC. For MPC applications, we compare the known PPML protocols built on MPC, and describe the efficiency of private inference and training for the state-of-the-art PPML protocols. Furthermore, we summarize several challenges and open problems to break though the efficiency of MPC protocols as well as some interesting future work that is worth being addressed. This survey aims to provide the recent development and key approaches of MPC to researchers, who are interested in knowing, improving, and applying concretely efficient MPC protocols.
A Multilingual Approach to Scene Text Visual Question Answering

Scene Text Visual Question Answering (ST-VQA) has recently emerged as a hot research topic in Computer Vision. Current ST-VQA models have a big potential for many types of applications but lack the ability to perform well on more than one language at a time due to the lack of multilingual data, as well as the use of monolingual word embeddings for training. In this work, we explore the possibility to obtain bilingual and multilingual VQA models. In that regard, we use an already established VQA model that uses monolingual word embeddings as part of its pipeline and substitute them by FastText and BPEmb multilingual word embeddings that have been aligned to English. Our experiments demonstrate that it is possible to obtain bilingual and multilingual VQA models with a minimal loss in performance in languages not used during training, as well as a multilingual model trained in multiple languages that match the performance of the respective monolingual baselines.
3D Gradient Reconstruction-Based Path Planning Method for Autonomous Vehicle With Enhanced Roll Stability

Path planning has received more and more attention due to its indispensability in autonomous vehicle (AV). Generally speaking, the stability of AV is not fully considered in path planning, therefore, the planned path may be detrimental for maintaining the stability. And this problem is even more acute for roll stability in complex 3D environment, such as off-road terrain environment. To enhance the roll stability on off-road terrain, a 3D gradient reconstruction-based path planning method (3DGRB-PPM) is presented in this work. The 3DGRB-PPM can keep the roll angle at a low level even in complex 3D environment, thus greatly enhancing the roll stability. The 3DGRB-PPM includes two parts, a gradient reconstruction unit and an adaptive fusion unit. In order to ensure the roll stability of AV in path planning, the gradient reconstruction unit is designed by constructing two potential fields, a joint potential field of gradient and roll angle for enhancing the roll stability and a 3D artificial potential field for reaching the destination. In order to coordinate these two potential fields, an adaptive fusion unit is designed by fuzzy inference rule. The simulation is implemented on the Matlab-Carsim co-simulation platform, and the simulation results show that the path planned by 3DGRB-PPM has good performance with enhanced roll stability.
New Observations on Zipf’s Law in Passwords

As password distribution lays the foundation for various password research, accurately characterizing it receives considerable attention. At IEEE TIFS’17, Wang et al. proposed the CDF-Zipf distribution model with the golden-section-search (GSS) fitting method to find the optimal parameters. Their model has been adopted by over 120 password-related studies. In this paper, we address their remaining, fundamental goodness-of-fit issue of password distribution in a principled approach. First, we prove that the confidence level of the state-of-the-art Monte Carlo approach (MCA, for the goodness-of-fit test) converges asymptotically to 0. By experimenting on 228.92 million real-world passwords, we confirm Wang et al.’s conjecture on the effect of sample size that minor deviations would lead to statistical significance for large-scale datasets. We propose both absolute and relative deviation metrics, and find that 1% random deviations in both metrics suffice to reject CDF-Zipf. Second, we attempt to reduce the non-negligible gap between the empirical and fitted distributions (with the maximum deviation of cumulative distribution function (CDF) being 1.91% on average). We explore eight alternative distribution models in two coordinate systems, and find that three models are more accurate than CDF-Zipf, but none can pass MCA. Particularly, we reveal that stretched-exponential, a variant of CDF-Zipf, can on average reduce the maximum CDF deviation from 1.91% to 1.25%. Third, to replace MCA, we introduce a new goodness-of-fit measure based on log-likelihoods. We find that stretched-exponential constantly has a larger log-likelihood than its counterparts. In all, stretched-exponential fits passwords better and further supports Zipf’s law in passwords.
Moving Obstacle Avoidance: A Data-Driven Risk-Aware Approach

This paper proposes a new structured method for a moving agent to predict the paths of dynamically moving obstacles and avoid them using a risk-aware model predictive control (MPC) scheme.Given noisy measurements of the a priori unknown obstacle trajectory, a bootstrapping technique predicts a set of obstacle trajectories.The bootstrapped predictions are incorporated in the MPC optimization using a risk-aware methodology so as to provide probabilistic guarantees on obstacle avoidance.We validate our methods using simulations of a multi-rotor drone that avoids various moving obstacles.
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation

This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neural machine translation (NMT) model is tested on source languages unseen during supervised training.Following this idea, we present SixT+, a strong many-to-English NMT model that supports 100 source languages but is trained with a parallel dataset in only six source languages.SixT+ initializes the decoder embedding and the full encoder with XLM-R large and then trains the encoder and decoder layers with a simple twostage training strategy.SixT+ achieves impressive performance on many-to-English translation.It significantly outperforms CRISS and m2m-100, two strong multilingual NMT systems, with an average gain of 7.2 and 5.0 BLEU respectively.Additionally, SixT+ offers a set of model parameters that can be further finetuned to other unsupervised tasks.We demonstrate that adding SixT+ initialization outperforms state-of-the-art explicitly designed unsupervised NMT models on Si↔En and Ne↔En by over 1.2 average BLEU.When applied to zero-shot cross-lingual abstractive summarization, it produces an average performance gain of 12.3 ROUGE-L over mBART-ft.We conduct detailed analyses to understand the key ingredients of SixT+, including multilinguality of the auxiliary parallel data, positional disentangled encoder, and the cross-lingual transferability of its encoder.
Comparative Study of Activation Functions and Their Impact on the YOLOv5 Object Detection Model

Object detection is an important aspect of computer vision research, involving determining the location and class of objects within a scene. For an object detection system to run in real-time, it is vital to minimise the computational costs while maintaining an acceptably high accuracy. In a Convolutional Neural Network (CNN) there is a direct correlation between the accuracy and the computational cost incurred by increasing the number of layers. Activation functions play a key role in a CNN to utilise nonlinearity to help balance the computational cost and accuracy. In this paper, a series of improvements are proposed to the state-of-the-art one-stage real-time object detection model, YOLOv5, providing the capability to enhance the overall performance. The validity of replacing the current activation function in YOLOv5, Swish, with a variety of alternative activation functions was investigated to aid in improving the accuracy and lowering the computational costs associated with visual object detection. This research demonstrates the various improvements in accuracy and performance that are achievable by appropriately selecting a suitable activation function to use in YOLOv5, including ACON, FReLU and Hardswish. The improved YOLOv5 model was verified utilising transfer learning on the German Traffic Sign Detection Benchmark (GTSDB) achieving state-of-the-art performance.
Unsupervised Learning of Diffeomorphic Image Registration via TransMorph

In this work, we propose a learning-based framework for unsupervised and end-to-end learning of diffeomorphic image registration. Specifically, the proposed network learns to produce and integrate time-dependent velocity fields in an LDDMM setting. The proposed method guarantees a diffeomorphic transformation and allows the transformation to be easily and accurately inverted. We also showed that, without explicitly imposing a diffeomorphism, the proposed network can provide a significant performance gain while preserving the spatial smoothness in the deformation. The proposed method outperforms the state-of-the-art registration methods on two widely used publicly available datasets, indicating its effectiveness for image registration. The source code of this work is available at: https://bit.ly/3EtYUFN .
Federated Learning via Disentangled Information Bottleneck

Existing Federated Learning (FL) algorithms generally suffer from high communication costs and data heterogeneity due to the use of conventional loss function for local model update and the equal consideration of each local model for global model aggregation. In this paper, we propose a novel FL approach to address the above issues. For local model update, we propose a disentangled Information Bottleneck (IB) principle-based loss function. For global model aggregation, we suggest a model selection strategy based on Mutual Information (MI). Particularly, we design a Lagrangian-based loss function using the IB principle and &#x201C;disentanglement&#x201D; for maximizing MI between the ground truth and model prediction and minimizing MI between the intermediate representations. We calculate MI ratio between the ground truth and model prediction, and between the original input and ground truth to select the effective models for aggregation. We analyze the theoretical optimal cost of the loss function and manifest optimal convergence rate, and quantify the outlier robustness of the aggregation scheme. Experiments demonstrate the superiority of the proposed FL approach, in terms of testing performance and communication speedup (i.e., 3.00-14.88 times for IID MNIST, 2.5-50.75 times for non-IID MNIST, 1.87-18.40 times for IID CIFAR-10, and 1.24-2.10 times for non-IID MIMIC-III).
Automatic Modulation Classification: Decision Tree Based on Error Entropy and Global-Local Feature-Coupling Network Under Mixed Noise and Fading Channels

A decision tree structure is proposed to handle automatic modulation classification under mixed noise and fading channels. In the first layer of the tree, IQ signals are categorized into three groups according to the error entropy. In the second layer, IQ signals preprocessed by Cauchy-Score functions are fed as patterns into the neural network where global and local features are abstracted by Transformer and CNN parallelly and concatenated to make a final decision. Monte-Carlo experiments demonstrate the superiority of the proposed method, strategy and network under mixed noise and fading channels.
THETA: A High-Efficiency Training Accelerator for DNNs With Triple-Side Sparsity Exploration

Training deep neural networks (DNNs) on edge devices has attracted increasing attention in real-world applications for domain adaption and privacy protection. However, deploying DNN training on resource-limited edge devices is challenging as there are massive computations and data transportation in training. To address this issue, we propose an energy-efficient training accelerator in this work by employing a hybrid compression strategy. Here, various data redundancies are fully exploited, and the real triple-side sparsity is achieved. Hence, the computational complexity is drastically reduced with negligible accuracy loss across a range of transfer learning tasks. To facilitate triple-side zero-skipping operations during different training stages, we first present a novel sparse data representation and a triple-sparsity index matching scheme. Second, a sparse tensor processing unit (STPU) arranged in a hierarchical structure is developed, which enables a flexible dataflow to process convolutional (Conv) and fully connected (FC) layers with diverse computational patterns throughout the entire training. Third, an auxiliary processing unit (APU) is designed to execute some postprocessing operations, such as rectified linear unit (ReLU) and on-the-fly pruning. Finally, the training accelerator is implemented under Taiwan Semiconductor Manufacturing Company (TSMC) 28-nm process and evaluated on multiple benchmarks. The experimental results show that THETA achieves 7.28–22.32 tera operations per second (TOPS) and 45.24–133.70 TOPS/W in performance and energy efficiency, reducing 40– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$72\times $ </tex-math></inline-formula> training time and 19– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$63\times $ </tex-math></inline-formula> energy consumption over dense training, respectively. Compared with the prior art, our design offers <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.6\times $ </tex-math></inline-formula> throughput and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.9\times $ </tex-math></inline-formula> energy efficiency, respectively.