Reviewerly/exHarmony huggingface dataset:
Resilience and Energy-Awareness in Constraint-Driven-Controlled Multi-Robot Systems

In the context of constraint-driven control of multi-robot systems, in this paper, we propose an optimization-based framework that is able to ensure resilience and energy-awareness of teams of robots. The approach is based on a novel, frame-theoretic, measure of resilience which allows us to analyze and enforce resilient behaviors of multi-robot systems. The properties of resilience and energy-awareness are encoded as constraints of a convex optimization program which is used to synthesize the robot control inputs. This allows for the combination of such properties with the execution of coordinated tasks to achieve resilient and energy-aware robot operations. The effectiveness of the proposed method is illustrated in a simulated scenario where a team of robots is deployed to execute two tasks subject to energy and resilience constraints.
Coupled Sensor Configuration and Path-Planning in Unknown Environments with Adaptive Cluster Analysis

We present an adaptive fast-approximation for sensor configuration which finds near-optimal placements and sensor field of views (FoV). The fast-approximation, either via partition-based or density-based cluster analysis, adapts based on the relation between statistical uncertainty of the path plan and environmental uncertainty. The sensor configurations are performed over regions of interest which most directly influence the path-planning efforts. These regions of interest can include exploratory paths by sampling the probabilistic environment model. The path-planning efforts aim to decide upon a path which minimizes an agent’s exposure to threats in an unknown static environment. The noisy sensor network observations are used to construct a threat field estimate using Gaussian Process Regression each iteration with a stationary kernel and heteroscedastic gaussian likelihood. The optimization of a task-driven information gain determines optimal sensor configurations when maximized. The numerical performance of the direct optimization and the adaptive cluster analysis method is presented. Finally, we show that the cluster centers can be utilized as a dimensionality reduction technique for FoV optimization whereby we only optimize FoV radial coverage.
An Efficient CNN Training Accelerator Leveraging Transposable Block Sparsity

Convolutional neural network (CNN) training is computationally intensive, requiring a great deal of time and resources. Exploiting data sparsity is a promising method to ac-celerate CNN training. In this work, we propose a novel algorithm for sparse training processes in which the weight matrices are pruned in a fine-grained block-wise manner. Both the forward propagation (FP) and backward propagation (BP) phases use the identical data layout. It can eliminate the matrix transposition procedure, reducing storage space and training time. Based on this pruning approach, we developed an FPGA-based accelerator for CNN training using a systolic array. The architecture can effectively skip the zero values calculation without incurring the imbalance between different processing elements (PEs). Our experimental results indicate that our design achieves 1.024 TOPS and 118.4 GOPS/W in terms of computational throughput and energy efficiency. Our design is 1.41× ~ 4.93× more energy efficient than the state-of-the-art training accelerator.
A Buyer-traceable DNN Model IP Protection Method Against Piracy and Misappropriation

Recently proposed model functionality and attribute extraction techniques have exacerbated unauthorized low-cost reproduction of deep neural network (DNN) models for similar applications. In particular, intellectual property (IP) theft and unauthorized distribution of DNN models by dishonest buyers are very difficult to trace by existing framework of digital rights management (DRM). This paper presents a new buyer-traceable DRM scheme against model piracy and misappropriation. Unlike existing methods that require white-box access to extract the latent information for verification, the proposed method utilizes data poisoning for distributorship embedding and black-box verification. Composite backdoors are installed into the target model during the training process. Each backdoor is created by applying a data augmentation method to some clean images of a selected class. The data-augmented images with a wrong label associated with a buyer are injected into the training dataset. The ownership and distributorship of a backdoor-trained user model can be validated by querying the suspect model with a set of composite triggers. A positive suspect will output the dirty labels that pinpoint the dishonest buyer while an innocent model will output the correct labels with high confidence. The tracking accuracy and robustness of the proposed IP protection method are evaluated on CIFAR-10, CIFAR-100 and GTSRB datasets for different applications. The results show an average of 100% piracy detection rate, 0% false positive rate and 96.81 % traitor tracking success rate with negligible model accuracy degradation.
Characteristic Comparison of Korean Unstructured Dialogue Corpora by Morphological Analysis

Natural language processing (NLP) has globally attracted researchers' attention. Many unstructured dialogue corpora in other languages as well as English and Chinese have been collected for NLP research. Those corpora show various characteristics depending on the relationship between speakers, the dialogue topic, how dialogues are gathered, etc. Analyzing their characteristics is therefore mandatory to comprehend the corpora for studying natural language dialogue. In this paper, we choose six different Korean unstructured dialogue corpora for their characteristic comparison, and identify the average numbers of utterances, proper nouns and pronouns per dialogue using MeCab-ko, a Korean morpheme analyzer.
A hybrid approach to determine patient’s critical situation using deep learning algorithm

Patients are considered to be one of the most vulnerable persons. When it comes to critical patients their movements and behaviors need to be monitored constantly as simple negligence could result in severe consequences. It is almost impossible to monitor a patient 24/7 without making any slight error. Therefore, this paper will establish a simple but effective solution to this issue by creating a heuristic approach system that can detect a patient's facial expressions and postural movement with the assistance of deep learning algorithms. This is a hybrid approach as we have combined Convolutional Neural Network & BlazePose GHUM 3D to create a robust model which can be used for image analysis in order to get precise monitoring results for critical situations by following specific sequences that would not have been possible without the hybrid model. Our facial expression recognition CNN model achieved 66% accuracy. On the other hand, the posture detection method achieved 86% accuracy on different scenarios.
Enhancement of Radar Signal Detection using Double-Density Dual-Tree DWT

This study aims to improve the signal-to-noise ratio (SNR) while reducing noise by employing wave decomposition and threshold processing to detect better radar targets. Because of the characteristics of Discrete Wavelet Transform (DWT) regarding energy concentration of signals, it became of interest to utilize it since it possesses the vital energy concentration of retrieved radar echo signals from targets since such signals are very general weak and accompanied by noise. Furthermore, the punctual time of radar signal pulse transmission is very short of ensuring signal security.
Sample-Specific Backdoor based Active Intellectual Property Protection for Deep Neural Networks

Recently, a number of researches have been proposed to protect the intellectual property (IP) of Deep Neural Network (DNN) models. However, most existing works are passive protection methods as they attempt to extract watermark from the pirated model after piracy occurs. In this paper, we propose an active IP protection method for DNN in which we utilize a variant of sample-specific backdoor attack to implement active authorization control for DNN models. During training, we mislabel all the clean images and keep the labels of backdoor instances as their ground-truth labels. Different from general backdoor trigger, we train a U-Net model to generate sample-specific trigger. This kind of trigger is sample-specific and invisible, which works as the secret key for each image and is hard to be noticed. Moreover, compared with existing active DNN IP protection methods, the proposed method can be applied in the black-box scenario. Experimental results on ImageNet and YouTube Aligned Face datasets demonstrate the effectiveness and robustness of the proposed method.
YOLOv5-based ALPR Improvement using Selective SR-GAN

Automatic License-Plate Recognition (ALPR) has widespread use in Intelligent Transportation Systems (ITS), security and surveillance systems, and crime investigation. However, the lack of a fixed setup and involvement of cameras with different qualities give rise to the generation of low-resolution (LR) images. Therefore, more recently introduced deep-learning resolution-upgrade algorithms are essential in modern ALPR systems. In this paper, we propose an additional selective Generative-Adversarial-Network (GAN) Super Resolution (SR) step between the two state-of-the-art You-Only-Look-Once (YOLO)v5-based License-Plate Detection (LPD) and Character Recognition (CR) steps. SR-GAN is proposed due to its perceptual information maintenance. Furthermore, selectiveness is suggested to avoid unnecessary high time-complexity impact. The experimental results show a significant accuracy increase of 18% and an average runtime of 86ms, suitable for many real-time applications.
Stain-free Holographic Detection of Circulating Tumor Cells Using A Deep Feature Fusion Neural Network

Circulating tumor cells (CTCs) have important reference value in cancer diagnostics. As the existing fluorescence-based CTC detection method faces the limitation of tedious staining steps and photobleaching, it's necessary to develop a stain-free identification method. This work demonstrated a stain-free holographic detection of CTCs using a deep feature fusion neural network. By utilizing the subtle difference between CTCs internal structures captured by the holographic microscope, the proposed neural network fuses low-level features extracted by the shallow layers with high-level features extracted by deep layers into fused features for CTCs identification, and reached an accuracy of 94%. Compared with the previous fluorescent stain-based method, this work provides a potential solution for high accuracy stain-free CTC detections.
Lightweight and Efficient Neural Network Using Progressively Greedy Search

In this paper, we propose a progressively greedy search (PGS) method for lightweight and efficient neural networks. We search neural network architectures in a restricted design space to reduce the search cost without sampling models. The searched models have regular weights which is similar to structural pruning. Our search strategy can automatically find a suitable channel number in each layer without manual setting. We pay more attention to the architecture, not the model weights, which is different from the pruning methods. Compared to RegNetX-200MF [1], our searched model, PGS 178MF, reduces 21.65MFLOPs and increases 0.93% accuracy in the same training cost. Compared to conventional width scaling, the architecture we have searched on MobileNet-V3-large improves accuracy by 0.72% under a similar model size.
Grand Challenges for Pervasive Technology to Transform Pervasive Education

Technologies already provide people with sophisticated and powerful digital ecosystems for lifelong and life-wide education. This article identifies grand challenges for ubiquitous and pervasive computing researchers to transform future education. It begins with a scenario to characterize learning on a day in 2030. Then it overviews broader educational needs and the pervasive computing elements for realizing them: mobile devices; embedded devices; importantly pervasive data infrastructures; and multidisciplinary foundations. Then it presents the grand challenges: 1) smart learning environments that feel safe, trusted, scrutable and controllable; 2) harness long-term, rich data for self-knowledge and shared understanding; 3) long-term personal digital learning companions; 4) augment classroom teacher’s awareness and memory; 5) smart classrooms that seamlessly fit into teaching and learning; 6) metaresearch to understand if and when technology really supports learning.
Real-Time Visual Target Detection and Tracking Via Unmanned Ground Vehicle

Vision-based autonomous robot field is becoming rapidly popular, according to the Artificial Intelligence revolution. The proposed system is a composition of Unmanned Ground Vehicle vision-based target tracking robot. Target tracking is useful in multiple real-life issues such as the assistance and security fields. Acquitted visual input processing is applied through using OpenCV library. The object detection process is done based on a pre-built deep learning object detection model. YOLOv3-tiny is used to detect objects, which is a light computation-cost version comparing to the original YOLOv3 model and the other complex deep-learning networks. Object to track is specified to be a human only. The target tracking algorithm is based on a sequence of mathematical equations with Region of Interest and stream’s frame coefficients. coefficients refer to the values of locations according to x-axis and y-axis of the frame. A simple mathematical technique is used for the delayed feedback issue. Ultra-sound technique is used for collision avoidance. The locomotion of UGV is based on transmitted commands from algorithm to the motors through local network connection. The results show an autonomous behavior, streamlined and accurate locomotion of tracking.
A Real-time Super-resolution Accelerator Using a big. LITTLE Core Architecture

Image super-resolution (SR) networks have shown a remarkable restoration performance but come with a huge memory bandwidth requirement due to a large-size input and lack of a pooling layer. As a result, SR accelerators usually adopted a streaming-like scheme to fit a highly customized and small SR network to available resources, which causes a large accuracy drop. To address this problem, this work proposes an SR accelerator using a big.LITTLE core architecture which is able to execute various networks in real-time. The proposed SR processor achieves an inference speed of 36.63 frames per second and a throughput of 221.79 GOPs at 200MHz for ×2 SR (from 960×540 to 1920×1080) while using only 1,280 eight-bit multipliers and 330 KB on-chip SRAM.
Class Attention Transfer for Semantic Segmentation

This paper proposes a knowledge transfer method using class attention maps (CAM) that are class-discriminative for training lightweight semantic segmentation networks. Since semantic segmentation classifies for each pixel, it is difficult to focus on the discriminative regions for each class in a single channel attention map. Thus, we generate attention maps for each class by using weights obtained from feature maps and class masks for squeezing the channels of the feature maps and then forcing a student network to generate the CAM that mimic the CAM of a teacher network. Our proposed method improves the state-of-the-art HRNetV2-W18+OCR by 4.78% in mIoU on the Cityscapes dataset.
A Real-Time Sparsity-Aware 3D-CNN Processor for Mobile Hand Gesture Recognition

A sparsity-aware 3D convolution neural network (CNN) accelerator is proposed for the real-time mobile hand gesture recognition (HGR) system. The complex computation of 3D convolution with the video data makes it difficult for real-time operation, especially in the resource-constrained mobile platform. To facilitate real-time implementation of HGR, this paper proposes two key features: 1) the inter-frame differential aware input method and IDANet to reduce the MAC operation and the external bandwidth by 84 % and 95.2 %, respectively, and achieve 79.97% accuracy on NvGesture dataset; 2) a low-latency 3D-CNN accelerator that utilizes activation and weight sparsity, achieving 31× faster inference latency than the state-of-the-art. The proposed processor is designed in 65 nm CMOS technology. It consumes 262 mW of power and achieves 599 GOPS/W of energy efficiency. As a result, the system realized 1.584 ms inference latency for real-time HGR in a mobile platform.
A Winograd-Based Highly-Parallel Convolution Engine for 8-bit CNN Acceleration

Convolutional neural network (CNN) accelerators for computational imaging typically use 8-bit fixed-point models for efficient computation, but the convolution engine still dominates the chip area. Quantizing models in lower bitwidths can cut down resource demand effectively, but it results in a significant loss of output quality. Another approach to reducing computational complexity is through Winograd convolution which lessens the demand for logic gates without diminishing model quality. Nevertheless, the resource reduction ratio of Winograd convolution declines with input bitwidths, and it needs even more gates than direct convolution at 8-bit. In this paper, we realize an area-efficient convolution engine for 8-bit computational imaging models by considering Winograd convolution and quantization jointly. First, we elaborate hardware sharing techniques for highly-parallel Winograd convolution. Then we propose an un-even scheme for Winograd-domain quantization that yields only up to 0.16 dB of PSNR drop on computational imaging models. Finally, we implement a highly-parallel Winograd convolution engine for 8-bit CNN inference. Synthesized with TSMC 40nm technology, the engine uses 2.17M of logic gates for delivering 5.12 TOPS of inference capability, saving 29.5% and 41.1 % of logic gates compared to a direct convolution engine and a naïve Winograd implementation respectively. On modified FFDNet and EDSR baselines, it achieves up to Full HD 20 fps with merely 0.09 dB of PSNR drop on average.
Agile Locomotion and Backflip Demonstrations on Mini Cheetah

Quadrupedal robots have seen an increase in usage in recent years for their superior traversability over unstructured terrains [1] . During the process of navigating in such environments, a stable and reliable controller for enabling these robots to locomote is indispensable. In this experimental demo, we showcase agile locomotion and backflip behaviors on the Mini Cheetah quadrupedal robot [2] . The demonstrated low-level capabilities pave the way for a fully autonomous planning and control pipeline in the future. Research topics such as task-level decision making, advanced navigation algorithms, and perception-informed adaptation to changing environments can be further explored in the context of quadrupedal robots.
An Improved Identity-Based Anonymous Authentication Scheme Resistant to Semi-Trusted Server Attacks

In mobile edge computing, the computing tasks of IoT terminal devices with limited computing power often need to be offloaded to servers for processing. However, there are malicious attacks by adversaries and malicious behaviors of servers in the network, coupled with the use of insecure network channels for data information transmission. These factors seriously threaten the privacy and data security of terminal devices and users. Therefore, it is urgent to use a safe and efficient anonymous authentication key agreement mechanism to verify the legitimacy of the identities of computing participants and ensure the safe transmission of task data. Recently Jia et al. proposed an identity-based authentication scheme, which combines many advantages of previous work and is resistant to various attacks. However, we found that their scheme has security problems, such as offline key guessing attack, internal attack, and user anonymity problems. We classify them as semi-trusted server attacks. In order to solve these security problems, we propose an improved scheme to better realize the authentication function by using flexible and security-enhanced keys for terminal equipment (TE), while ensuring the anonymity of the TE through implicit ID. Furthermore, we provide formal security proof, formal security verification, and security analysis for the improved protocol. Compared with the previous scheme, the scheme has certain improvements in security and performance.
A Lightweight Detector for Small Objects

Object detection is one of the significant applications of computer vision, that combines the work of image classification and localization. It is being used in several areas nowadays like, Autonomous vehicles, face recognition, security cameras, e.t.c. There are some lightweight object detection algorithms based on You Only Look Once (YOLO) [1], and SSD (Single Shot MultiBox Detector) [2]. The drawback of these algorithms is that they consume a larger number of parameters for training, provide less mAP, and also lack detection of small objects. To overcome the above-mentioned issues, a computationally cheap lightweight object detector with a strong network and an enhancer block has been proposed in this paper, which helps in increasing the accuracy and improving the detection of small objects. The proposed object detector is trained from scratch without using any pre-trained image classifier weights. The object detector is trained on Udacity Annotated Driving Dataset, and the result shows that the model uses fewer number parameters (0.43M parameters for training), and is also better at detecting small objects than the conventional models at the time of inference, and provides 4.8% and 0.8% higher mAP than two different conventional models.
Minimum Energy-Time Optimal Control of Wheeled Mobile Robots: Application to Parallel Parking

Optimal controller design for a kinematic model of a wheeled mobile robot (WMR) to minimize input energy and maneuver time in parallel parking maneuvers in an obstacle-free environment is the focus of this paper. A multi-objective optimal control problem is formulated using a weighted combination of maneuver time and energy. The necessary conditions for optimality are derived using the Hamiltonian of the system, resulting in a two point boundary value problem. The analytical expression for the states, costates, and controls are derived using the Jacobi Elliptic functions, which transforms the optimal control problem to a nonlinear optimization problem. It is noted that the controls required for the first half of the maneuvers can be used to derive the second half with sign changes. Although the optimal control problem is formulated without imposing constraints on control inputs, it turns out that the inputs are bounded to lie on a circle, the radius of which is a function of the weighting factor. The unique observations are analytically proven and illustrated experimentally.
Towards Using Fully Observable Policies for POMDPs

Partially Observable Markov Decision Process (POMDP) is a framework applicable to many real world problems. In this work, we propose an approach to solve POMDPs with multimodal belief by relying on a policy that solves the fully observable version. By defininig a new, mixture value function based on the value function from the fully observable variant, we can use the corresponding greedy policy to solve the POMDP itself. We develop the mathematical framework necessary for discussion, and introduce a benchmark built on the task of Reconnaissance Blind TicTacToe. On this benchmark, we show that our policy outperforms policies ignoring the existence of multiple modes.