Reviewerly/exHarmony huggingface dataset:
Novel Design of Lightweight Aerial Manipulator for Solar Panel Cleaning Applications

In this research, a novel design of a lightweight aerial manipulator system is proposed for solar panel cleaning with active (CoG) compensation mechanism. Recently, separate solar panel arrays or units are commonly installed on residential, commercial rooftops or roads, making it inconvenient for land robots to perform the cleaning tasks. The proposed light weight solar panel cleaning aerial manipulator with the gravity compensation mechanism is intended to be attached beneath a drone to increase its stability during operation. The manipulator workspace given the proposed system is analyzed under CoG shift constraints. The kinematics and dynamics of the aerial manipulator coupled with the compensation mechanism are presented, and a path-planning scheme for solar panel cleaning is detailed. A dynamic control law based on pitch and counterweight position reduced-order dynamics is derived, and its equivalence to the static compensation law is shown. An experimental test bench is used to simulate the aerial manipulation during operations to validate the performance of the proposed manipulator and its stability. Its tilting pitch angle is collected and examined during operation. The results show that the system is less susceptible to unwanted tilting. A tilt angle reduction of 2 degrees was observed between an uncompensated and compensated system, with a difference in shift of CoG location of 1.72% of the total system length. The CoG location shift is also simulated without the presence of a slider mechanism and shows a difference of 24.5% with the compensated system. The compensation mechanism significantly reduces the tilt angle, avoiding potential instability, and consequently decreases the power required by the carrying drone.
Dual-Stage Super-Resolution for Edge Devices

To reduce memory usage, edge devices such as TVs use Super-resolution(SR) with dedicated hardware networks. Dedicated hardware has the disadvantage of being difficult to change and difficult to improve performance. We propose a dual-structured SR system that can extend this dedicated hardware, suggesting a way to improve the restoration performance for compression with minimal change in hardware. Edge devices such as general TVs deal with moving videos rather than still images. In this case, the video includes video compression, and a preprocessing step of restoring deterioration is useful. We propose as the main idea that these steps can be handled while removing only pixel shuffle layers from existing networks. Our proposed SR method consists of two stages: 1) restoring from the video compression without changing the size of LR images and 2) increasing the resolution. The first stage can be learned by reducing the difference between video-LR (Low-Resolution video images with codec degradation) and downscaled-HR (video images made without codec degradation by simply reducing the size of High-Resolution video). The second stage, resolution enhancement, performs the same task as the traditional SISR task, except that it focuses on restoring the output of the first stage rather than a downscaled-HR. Our new dataset for this processing, HD2UHD, consists of (video-LR, downscaled-HR, and HR) tuples. We also propose a new scheme of input distillation that utilizes video-LR and downscaled-HR at the same time.
RawSpectrogram: On the Way to Effective Streaming Speech Anti-Spoofing

Traditional anti-spoofing systems cannot be used straightforwardly with streaming audio because they are designed for finite utterances. Such offline models can be applied in streaming with the help of buffering; however, they are not effective in terms of memory and computational consumption. We propose a novel approach called RawSpectrogram that makes offline models streaming-friendly without a significant drop in quality. The method was tested on RawNet2 and AASIST, resulting in new architectures called RawRNN (RawLSTM and RawGRU), RS-AASIST, and TAASIST. The RawRNN-type models are much smaller and achieve a better Equal Error Rate than their base architecture, RawNet2. RS-AASIST and TAASIST have fewer parameters than AASIST and achieve similar quality. We also proved our concept for models with time-frequency transform front-ends and automatic speaker verification systems by proposing RECAPA-TDNN based on ECAPA-TDNN. RS-AASIST and RECAPA-TDNN were combined into the first streaming-friendly spoofing-aware speaker verification system reported in the literature. This joint system achieves significantly better quality than the corresponding offline solutions. All our models require far fewer floating-point operations for score updates. RawSpectrogram usage significantly reduces the latency of the prediction and allows the system to update the probability with each new chunk from the stream, preserving all information from the past. To the best of our knowledge, TAASIST is the most successful voice anti-spoofing system that employs a vanilla Transformer trained using supervised learning.
Safe Non-Stochastic Control of Control-Affine Systems: An Online Convex Optimization Approach

We study how to safely control nonlinear control-affine systems that are corrupted with bounded <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-stochastic noise</i> , i.e., noise that is unknown a priori and that is <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</u> necessarily governed by a stochastic model. We focus on safety constraints that take the form of time-varying convex constraints such as collision-avoidance and control-effort constraints. We provide an algorithm with bounded <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic regret</i> , i.e., bounded suboptimality against an optimal clairvoyant controller that knows the realization of the noise a priori. We are motivated by the future of autonomy where robots will autonomously perform complex tasks despite real-world unpredictable disturbances such as wind gusts. To develop the algorithm, we capture our problem as a sequential game between a controller and an adversary, where the controller plays first, choosing the control input, whereas the adversary plays second, choosing the noise's realization. The controller aims to minimize its cumulative tracking error despite being unable to know the noise's realization a priori. We validate our algorithm in simulated scenarios of (i) an inverted pendulum aiming to stay upright, and (ii) a quadrotor aiming to fly to a goal location through an unknown cluttered environment.
SwinAnomaly: Real-Time Video Anomaly Detection Using Video Swin Transformer and SORT

Detecting anomalous events in videos is a challenging task due to their infrequent and unpredictable nature in real-world scenarios. In this paper, we propose SwinAnomaly, a video anomaly detection approach based on a conditional GAN-based autoencoder with feature extractors based on Swin Transformers. Our approach encodes spatiotemporal features from a sequence of video frames using a 3D encoder and upsamples them to predict a future frame using a 2D decoder. We utilize patch-wise mean squared error and Simple Online and Realtime Tracking (SORT) for real-time anomaly detection and tracking. Our approach outperforms existing prediction-based video anomaly detection methods and offers flexibility in localizing anomalies through several parameters. Extensive testing shows that SwinAnomaly achieves state-of-the-art performance on public benchmarks, demonstrating the effectiveness of our approach for real-world video anomaly detection. Furthermore, our proposed approach has the potential to enhance public safety and security in various applications, including crowd surveillance, traffic monitoring, and industrial safety.
Fault Probability Correlation Analysis Based on Secondary Filtering

Correlation power analysis (CPA) is a classical method in side-channel attacks. Based on the power consumption model, the correlation between the power consumption of cryptographic devices and the assumed intermediate value is analyzed to recover the key. Theoretically, only a few power traces are required to recover the key when the noise hypothesis is known. However, in the high-frequency and high-noise environment, the completion of CPA requires more power traces, and the computational complexity also increases. Therefore, this paper proposes a fault probability correlation analysis method based on secondary filtering (2F-FPCA), which selects the fault probability traces according to the Hamming Weight of the intermediate value and reduces the number of sampling points by selecting points of interest. This method does not need to access ciphertext and is little affected by noise. Moreover, it can recover the key with fewer fault probability traces and lower computational complexity, improving the attack efficiency of CPA. In this paper, 2F-FPCAs are carried out based on the AES-128 algorithm of the Micro Controller Unit (MCU). The key can be recovered successfully using 10 fault probability traces, and the computational complexity is reduced by 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> times.
Context-Based Adaptive Multimodal Fusion Network for Continuous Frame-Level Sentiment Prediction

Recently, video sentiment computing has become the focus of research because of its benefits in many applications such as digital marketing, education, healthcare, and so on. The difficulty of video sentiment prediction mainly lies in the regression accuracy of long-term sequences and how to integrate different modalities. In particular, different modalities may express different emotions. In order to maintain the continuity of long time-series sentiments and mitigate the multimodal conflicts, this paper proposes a novel Context-Based Adaptive Multimodal Fusion Network (CAMFNet) for consecutive frame-level sentiment prediction. A Context-based Transformer (CBT) module was specifically designed to embed clip features into continuous frame features, leveraging its capability to enhance the consistency of prediction results. Moreover, to resolve the multi-modal conflict between modalities, this paper proposed an Adaptive multimodal fusion (AMF) method based on the self-attention mechanism. It can dynamically determines the degree of shared semantics across modalities, enabling the model to flexibly adapt its fusion strategy. Through adaptive fusion of multimodal features, the AMF method effectively resolves potential conflicts arising from diverse modalities, ultimately enhancing the overall performance of the model. The proposed CAMFNet for consecutive frame-level sentiment prediction can ensure the continuity of long time-series sentiments. Extensive experiments illustrate the superiority of the proposed method especially in multimodal conflicts videos.
Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning With Transformers

Motion planning is integral to robotics applications such as autonomous driving, surgical robots, and industrial manipulators. Existing planning methods lack scalability to higher-dimensional spaces, while recent learning-based planners have shown promise in accelerating sampling-based motion planners (SMP) but lack generalizability to out-of-distribution environments. To address this, we present a novel approach, Vector Quantized-Motion Planning Transformers (VQ-MPT) that overcomes the key generalization and scaling drawbacks of previous learning-based methods. VQ-MPT consists of two stages. Stage 1 is a Vector Quantized-Variational AutoEncoder model that learns to represent the planning space using a finite number of sampling distributions, and stage 2 is an Auto-Regressive model that constructs a sampling region for SMPs by selecting from the learned sampling distribution sets. By splitting large planning spaces into discrete sets and selectively choosing the sampling regions, our planner pairs well with out-of-the-box SMPs, generating near-optimal paths faster than without VQ-MPT's aid. It is generalizable in that it can be applied to systems of varying complexities, from 2D planar to 14D bi-manual robots with diverse environment representations, including costmaps and point clouds. Trained VQ-MPT models generalize to environments unseen during training and achieve higher success rates than previous methods. Videos and code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/ucsd.edu/vq-mpt/home</uri> .
Guidelines for Secure Process Control: Harnessing the Power of Homomorphic Encryption and State Feedback Control

The paper presents applications of homomorphic cryptographic schemes in process control. Homomorphic encryption is a particular type of encryption that allows performing mathematical operations over encrypted data. Therefore the user can use third-party services as controllers without revealing any vulnerable information, like process data. Homomorphic properties make it possible to perform data aggregation or evaluate control actions without having any knowledge about the process data. Our paper explores two fully homomorphic cryptographic schemes, namely Brakerski/Fan-Vercauteren (BFV) and Cheon-Kim-Kim-Song (CKKS) cryptosystems. Each scheme is briefly described with its advantages and drawbacks, conditions, and applications. We present a case study involving linear-quadratic (LQ) control strategy implemented in an encrypted setup. This serves for comparison and a step-by-step guide for implementing encrypted process control.
ConDANet: Contourlet Driven Attention Network for Automatic Nuclei Segmentation in Histopathology Images

Cell nuclei segmentation, the task of identifying the precise boundary of the nucleus in each cell in a histopathology image, is a rudimentary task prior to single-cell analysis. While addressing this task, two of the major challenges are the precise delineation of the small-shaped nucleus structure and the characterization of the edge region of the nucleus for both the regular and deformed nucleus. To overcome these challenges, a contourlet driven attention network, namely ConDANet, is developed which incorporates a novel attention mechanism along with a content-preserving sampling strategy. Instead of using conventional spatial or channel-wise attention, a controlling signal driven attention mechanism is proposed, which is capable of extracting fine edge details of the nuclei regions by focusing on the relevant edge or boundary information of the nuclei structures. The contourlet transform based controlling signal generation scheme is proposed, which not only exploits the advantage of the multi-scale time frequency localization properties of wavelets but also provides a high degree of directionality. Additionally, the wavelet pooling strategy is incorporated to the network which preserves the textural content of the nucleus in histopathology images and prevents the loss of information in the subsequent sampling operation of the encoder-decoder part of the network. Finally, the proposed method is employed for analyzing three publicly available histopathology datasets to manifest its effectiveness in segmenting nuclei from cellular images extracted from a wide variety of organs and patients. Dice scores obtained in these three datasets are 88.9%, 81.71%, and 75.12% which are found to be superior compared to some state-of-the-art methods.
BERT-Based Sentiment Analysis for Low-Resourced Languages: A Case Study of Urdu Language

Sentiment analysis holds significant importance in research projects by providing valuable insights into public opinions. However, the majority of sentiment analysis studies focus on the English language, leaving a gap in research for other low-resourced languages or regional languages, e.g., Persian, Pashto, and Urdu. Moreover, computational linguists face the challenge of developing lexical resources for these languages. In light of this, this paper presents a deep learning-based approach for Urdu Text Sentiment Analysis (USA-BERT), leveraging Bidirectional Encoder Representations from Transformers and introduces an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Urdu Dataset for Sentiment Analysis-23</i> (UDSA-23). USA-BERT first preprocesses the Urdu reviews by exploiting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BERT-Tokenizer</i> . Second, it creates BERT embeddings for each Urdu review. Third, given the BERT embeddings, it fine-tunes a deep learning classifier (BERT). Finally, it employs the Pareto principle on two datasets (the state-of-the-art (UCSA-21) and UDSA-23) to assess USA-BERT. The assessment results demonstrate that USA-BERT significantly surpasses the existing methods by improving the accuracy and f-measure up to 26.09% and 25.87%, respectively.
A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection

Video Surveillance Systems (VSSs) are used in a wide range of applications including public safety and perimeter security. They are deployed in places such as markets, hospitals, schools, banks, shopping malls, offices, and smart cities. VSSs generate a massive amount of surveillance data, and significant research has been published on the use of machine learning algorithms to handle surveillance data. In this paper, we present an extensive overview and a thorough analysis of cutting-edge learning methods used in VSSs. Existing surveys on learning approaches in video surveillance have some drawbacks, such as a lack of in-depth analysis of the learning algorithms, omission of certain methodologies, insufficient critical evaluation, and absence of recent learning algorithms. To fill these gaps, this survey provides a thorough examination of the most recent learning algorithms for anomaly detection. A critical assessment of the algorithms including their strengths, weaknesses, and applicability as well as tailored classifications of anomaly types for different domains are provided. Our study also offers insights into the future development of learning techniques in VSS, positioning itself as a valuable resource for both researchers and practitioners in the field. Finally, we share our thoughts on what we learned and how it can help with new developments in the future.
Obscuring Transaction Information in Decentralized P2P Wireless Networks

This work shows the comprehensive model of obfuscation techniques in decentralized peer-to-peer (P2P) network transactions by consolidating a distributed learning approach and blockchain as the backbone technologies. We provide several protocols by utilizing the CryptoNote protocols to be deployed into the Ethereum blockchain environment that can overcome the demerits of most current blockchain systems in terms of traceability and linkability of data flows. The concerns of traceability and linkability of transactions arise since data record breaches have occurred intermittently, where valuable information is often mishandled and misused, inducing a security threat to personal privacy. Furthermore, We thoroughly analyzed the pros and cons of the constructed protocols along with a systematic security evaluation to satisfy the design objectives and requirements. The constructed protocols are unbiased since the observers cannot extract and associate transactional information with any entity. Our assessment and assumptions neglect the hard fork where a radical change in the blockchain network is required for thorough real-world implementation. Finally, the numerical results and symbolic modelling indicate that the proposed protocols fill the gap of the existing model found in the literature. These findings highlight a fresh concept of privacy awareness in decentralized network environments, the properties that most constructions are lacking.
Key Indicators to Assess the Performance of LiDAR-Based Perception Algorithms: A Literature Review

Perception algorithms are essential for autonomous or semi-autonomous vehicles to perceive the semantics of their surroundings, including object detection, panoptic segmentation, and tracking. Decision-making in case of safety-critical situations, like autonomous emergency braking and collision avoidance, relies on the outputs of these algorithms. This makes it essential to correctly assess such perception systems before their deployment and to monitor their performance when in use. It is difficult to test and validate these systems, particularly at runtime, due to the high-level and complex representations of their outputs. This paper presents an overview of different existing metrics used for the evaluation of LiDAR-based perception systems, emphasizing particularly object detection and tracking algorithms due to their importance in the final perception outcome. Along with generally used metrics, we also discuss the impact of Planning KL-Divergence (PKL), Timed Quality Temporal Logic (TQTL), and Spatio-temporal Quality Logic (STQL) metrics on object detection algorithms. In the case of panoptic segmentation, Panoptic Quality (PQ) and Parsing Covering (PC) metrics are analysed resorting to some pretrained models. Finally, it addresses the application of diverse metrics to evaluate different pretrained models with the respective perception algorithms on publicly available datasets. Besides the identification of the various metrics being proposed, their performance and influence on models are also assessed after conducting new tests or reproducing the experimental results of the reference under consideration.
Local-Global Dynamic Filtering Network for Video Super-Resolution

Video super-resolution (VSR) has been greatly advanced by the use of deep learning techniques, but the challenge of handling motion variability has remained a bottleneck. Many previous methods have treated motions equally, leading to suboptimal alignment. In this paper, we propose a Local-Global Dynamic Filtering Network (LGDFNet) to address this issue. LGDFNet uses a divide-and-conquer strategy to handle motionvarying features, where the overall feature is split into local features and assigned specialized sub-networks to align and fuse them from local to global. To align the features and adaptively aggregate several kernels for calibration, we propose the SelfCalibrated Dynamic Filtering (SCDF) module. Additionally, we introduce the Cross-Attention Feature Fusing (CAFF) module to capture long-range dependencies and fuse each feature. Our extensive experiments on different benchmark datasets demonstrate the effectiveness of LGDFNet, both subjectively and objectively.