Reviewerly/exHarmony huggingface dataset:
Rotation-aware dynamic temporal consistency with spatial sparsity correlation tracking

Recently, discriminative correlation filter (DCF)-based trackers have been widely applied to visual tracking. However, a significant problem of DCF-based trackers is that the model uses the fixed patterns of temporal modeling and fails to suppress the distractive features. To obviate the issue, we propose the dynamic temporal consistency with spatial sparsity correlation filter. The dynamics refers to the adaptive temporal consistency hidden in the response maps and dynamic lasso constraint moderated by the prior knowledge. Unlike the classical temporal modeling method applied to filter model, we exploit the consistency of the response maps to perceive adaptive temporal continuity modeling to enable the filter to have self-regularized ability. Temporal modeling and spatial sparsity are incorporated in a unified optimization learning model and optimized together with ADMM algorithm and Sherman-Morrison formula. In particular, the tracking performance is hindered by the weak capacity to rotation variation. For the sake of estimating the rotation angle accurately to maintain rotation invariance, we explore the coarse-to-fine rotation estimation module. The coarse rotation is supported by the angle pool and the part-based tracker. By introducing the connected hyper ellipse fitting strategy to eliminate fake distractors to ensure a pure target region, the fine level attempts to achieve the optimal rotation angle with the minimum second order statistical bias. The design enables the filter to train with the recovered rotated image instead of axis-aligned bounding box, which attributes to alleviating the impact of ambiguous region and concentrating on the interested target. Extensive experimental results validate the superiority of the proposed method against other state-of-the art trackers and exhibit a remarkable generality in the rotated challenging scenarios.
Search-based cost-sensitive hypergraph learning for anomaly detection

Detecting anomaly plays a vital role in ensuring the reliability and safety of many applications. However, this task is challenging due to the unclear correlation underneath the testing data and the class imbalanced issue between anomalous and normal data. Moreover, it is noted that the misclassification of anomalous and normal data is usually associated with different costs in real applications. To solve these issues, we propose a search-based cost-sensitive hypergraph learning method (SCSHL) for anomaly detection. More specifically, to deal with the imbalanced issue, we first construct a target-specific subset of training data according to the testing samples and conduct a cost-sensitive feature selection method to select the effective features. To explore the unclear high-order correlation underneath the data, we employ the hypergraph structure to formulate the complex relationship among the testing data and further combine the cost information into hypergraph learning and conduct label propagation on the cost-sensitive hypergraph structure. To evaluate the effectiveness of the proposed method, we have conducted experiments on industry anomaly detection datasets, software defect prediction (SDP) datasets and outlier detection datasets (ODDS). Experimental results and comparisons with state-of-the-art methods have shown the superiority of our method.
Concentrate on Hardware Imperfection via Aligning Reconstructed States

Specific emitter identification (SEI) is to authenticate communication devices via analyzing unintentional modulation (UIM) from hardware imperfection. A great challenge existing in current methods is the interference of intentional modulation (IM) and channel noise. To address this issue, this letter firstly analyzes how IM, UIM, and additive noise (AN) affect reconstructed states, which describes the dynamic characteristics of communication devices. The findings indicate that IM and UIM bring a scaling transform, while AN corresponds to a translation transform. Based on theoretical analysis, a novel SEI method concentrating on UIM is proposed. An advanced PointNet architecture is designed with stacked spatial transformer networks (STN), namely PointNet-Alignment Point by Point (PointNet-APBP). PointNet-APBP aligns reconstructed states into a canonical space and extracts spatial interaction as fingerprint features. Simulated and real-world experiments validate that the proposed method can mitigate the effects of IM as well as AN, concentrate feature extractors on UIM, and in turn achieve higher accuracy and stronger robustness.
Noise-Robust Vibration Phase Compensation for Satellite ISAL Imaging by Frequency Descent Minimum Entropy Optimization

Inverse synthetic aperture ladar (ISAL) can perform high-resolution imaging for satellites. However, due to the short wavelength of the laser, satellite micro-vibration will introduce space-variant vibration phase error (SVVPE) and space-invariant vibration phase error (SIVVPE) in the echoes, which seriously blur the ISAL image. In this paper, we propose a noise-robust vibration phase compensation algorithm to accurately estimate and correct these two types of vibration phase errors by frequency descent minimum entropy optimization. Firstly, considering the characteristics of the micro-vibration of satellites, we establish a novel phase error model based on the Fourier series theory, which only contains low-frequency vibration components. The estimation of the phase errors is then translated into the estimation of the model’s Fourier coefficients, which can be achieved by a multi-dimensional minimum entropy optimization. After that, a frequency descent method (FD) is proposed to transform the multi-dimensional optimization into a group of two-dimensional optimizations so that the proposed algorithm can achieve monotonic iterative convergence. In addition, we introduce a solution space adaptive reduction operation to reduce the computational burden when solving the two-dimensional minimum entropy optimizations by the genetic algorithm (GA) to obtain the global optimal solution. Finally, experiments based on the simulated data and the real measured data confirm the effectiveness of the proposed algorithm. Compared with the traditional methods, the proposed algorithm achieves higher phase error estimation accuracy and better image quality.
Temporal-stochastic tensor features for action recognition

In this paper, we propose Temporal-Stochastic Product Grassmann Manifold (TS-PGM), an efficient method for tensor classification in tasks such as gesture and action recognition. Our approach builds on the idea of representing tensors as points on Product Grassmann Manifold (PGM). This is achieved by mapping tensor modes to linear subspaces, where each subspace can be seen as a point on a Grassmann Manifold (GM) of the corresponding mode. Subsequently, it is possible to unify factor manifolds of respective modes in a natural way via PGM. However, this approach possibly discards discriminative information by treating all modes equally, and not considering the nature of temporal tensors such as videos. Therefore, we introduce Temporal-Stochastic Tensor features (TST features) to extract temporal information from tensors and encode them in a sequence-preserving TST subspace. These features and regular tensor modes can then be simultaneously used on PGM. Our framework addresses the problem of classification of temporal tensors while inheriting the unified mathematical interpretation of PGM because the TST subspace can be naturally integrated into PGM as a new factor manifold. Additionally, we enhance our method in two ways: (1) we improve the discrimination ability by projecting subspaces onto a Generalized Difference Subspace, and (2) we utilize kernel mapping to construct kernelized subspaces able to handle nonlinear data distribution. Experimental results on gesture and action recognition datasets show that our methods based on subspace representation with explicit TST features outperform pure spatio-temporal approaches.
Security of Machine Learning-Based Anomaly Detection in Cyber Physical Systems

With the emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) services and applications in the Cyber Physical Systems (CPS), the methods of protecting CPS against cyber threats is becoming more and more challenging.Various security solutions are implemented to protect CPS networks from cyber attacks.For instance, Machine Learning (ML) methods have been deployed to automate the process of anomaly detection in CPS environments.The core of ML is deep learning.However, it has been found that deep learning is vulnerable to adversarial attacks.Attackers can launch the attack by applying perturbations to input samples to mislead the model, which results in incorrect predictions and low accuracy.For example, the Fast Gradient Sign Method (FGSM) is a white-box attack that calculates gradient descent oppositely to maximize the loss and generates perturbations by adding the gradient to unpolluted data.In this study, we focus on the impact of adversarial attacks on deep learning-based anomaly detection in CPS networks and implement a mitigation approach against the attack by retraining models using adversarial samples.We use the Bot-IoT and Modbus IoT datasets to represent the two CPS networks.We train deep learning models and generate adversarial samples using these datasets.These datasets are captured from IoT and Industrial IoT (IIoT) networks.They both provide samples of normal and attack activities.The deep learning model trained with these datasets showed high accuracy in detecting attacks.An Artificial Neural Network (ANN) is adopted with one input layer, four intermediate layers, and one output layer.The output layer has two nodes representing the binary classification results.To generate adversarial samples for the experiment, we used a function called the 'fast gradient method' from the Cleverhans library.The experimental result demonstrates the influence of FGSM adversarial samples on the accuracy of the predictions and proves the effectiveness of using the retrained model to defend against adversarial attacks.
Cloud Computing Privacy: Concept , Issues And Solutions

Today, the internet is used not only to obtain information, but also to store personal data and to make use of the software and technology that is available on the internet. The most essential challenge in this context is how to keep cloud administrators from seeing customer data stored in the cloud, as well as how to ensure that security-related functions are separate from cloud management functions. The proposed architecture is presented in this paper to address the majority of privacy issues in cloud computing. The primary idea behind the proposed architecture is to fragment data and store it on many clouds using third parties.
Bengali Social Media Post Sentiment Analysis using Deep Learning and BERT Model

Social media platforms such as Facebook, Twitter, and others are becoming incredibly popular for expressing sentiments and thoughts. People use these platforms to express not only their happy moments, but also their feelings when they are depressed. Using sentiment analysis in natural language processing to analyse these social media posts, one’s emotional state can be determined, such as happy, sad, or angry at a particular time. The majority of research in this topic is conducted in English, therefore sentiment analysis from Bengali is not very accurate. So, our goal is to work on this topic using Bengali datasets obtained from various social media posts to improve sentiment detection accuracy. This work can be used to help building a system in our country’s mental health sector. In this research, we first gathered social media data. Then we used a number of feature selection and extraction techniques like Word2Vec, GloVe etc. and applied a number of deep learning model, such as RNN, LSTM, GRU etc. We have also applied hybrid and transformer-based BERT models like CNN-BiLSTM Bangla-BERT, mBERT etc and finally got the highest accuracy of 88.59% for the CNN-BiLSTM hybrid model using the GloVe feature vector.
An overview on mobile manipulator in nuclear applications

Nuclear power is worldwide popular and keeps rapidly growing, but manual operation in nuclear facilities is challenging by safety and workload issues. Mobile manipulators are ideal to replace human works in nuclear applications. This article reviews the development of mobile manipulators in nuclear applications over time. Then, we summarize three tasks for robots in nuclear applications and corresponding requirements, followed by an overview of the design of selected specific models. Based on the existing design, we propose the challenges for future mobile manipulators in nuclear applications.
Real-Time Human Falling Recognition via Spatial and Temporal Self-Attention Augmented Graph Convolutional Network

Currently, the skeleton-based human action recognition (e.g. walking, sitting and falling down) has achieved great interest, because the skeleton graph is robust to complex background and illumination changes compared to images. In this paper, a complete solution to real-time falling recognition task for intelligent monitoring has been provided. First, a manually annotated skeleton dataset for falling down action recognition is published. Then, a real-time self-attention augmented graph convolutional network (ST-SAGCN) is proposed. The network contains two novel architectures: a spatial self-attention module and a temporal self-attention module, which can effectively learn intra-frame correlations between different body parts, and inter-frame correlations between different frames for each joint. Finally, extensive comparative experiments on the dataset have proven that the proposed model can achieve remarkable improvement on falling recognition task. When the model is deployed in intelligent monitoring system, it achieves an inference speed over 40 fps and meets the demand of practical applications.
Behavioral Dynamics-based Impedance Control for Collision Avoidance of Human-Following Robots

Human-following is an important part of man-machine interaction and has wide application value. In Human-following, the following stability, human comfort and obstacle avoidance are considered comprehensively. To solve the above problems, we propose a human behavioral dynamics-based impedance control for collision avoidance of human-following robot, which take the comfort and the following stability into consideration. Specifically, an impedance control method combined with virtual interaction force is introduced to simulate the human social dynamic characteristics to improve the comfort of human-following. The uniformly ultimately bounded property of the impedance control is proved by using the Lyapunov stability theorem. Moreover, an obstacle component constructed by behavioral dynamics is proposed to extend the impedance controller for collision avoidance. Behavioral dynamics compared with an artificial potential field method possess better collision avoidance performance during humanfollowing. The simulation results validate that the human-following system integrated with collision avoidance is effective.
An ensemble model for idioms and literal text classification using knowledge-enabled BERT in deep learning

Literal and metaphorical meanings can both be found in language as a system of communication. The literal sense is not difficult, but the figurative sense includes ideas like metaphors, similes, proverbs, and idioms to create a distinctive impact or imaginative description. Idioms are phrases whose meaning differs from that of the words that make up the phrase. Due to its non-compositional character, idiom detection in NLP tasks like text categorization is a significant difficulty. Inaccurate idiom recognition has reduced the model's performance in one of the crucial text categorization tasks, such as cyberbullying and sentiment analysis. Using language representation models that have already been trained, such as BERT (Bidirectional Encoder Representation from Transformer) and RoBERTa, the current system categorises the phrases as literals or idioms (Robustly Optimised BERT Pretraining Approach). The current system performs more accurately than the baseline models. We propose a method for categorising idioms and literals is developed utilizing K-BERT (Knowledge-enabled BERT), a Deep Learning algorithm that injects knowledge-graphs (KGs) into the sentences as domain knowledge. Additionally, it will be ensembled utilizing the stacking ensemble approach with baseline models like BERT and RoBERTa. Trofi Metaphor dataset was utilised in this study for the model's training, while a brand-new internal dataset was used for testing.
Differential Private Discrete Noise Adding Mechanism: Conditions and Properties

Differential privacy is a golden standard to quantify the degree of privacy protection. Many application scenarios of differential privacy in daily life are based on discretely distributed datasets, e.g., traffic data, health records. However, the adoption of differential privacy under discrete distributions is rare. This paper focuses on the discrete random noise adding mechanisms. We first propose basic differential privacy conditions for the general discrete noise adding mechanisms. Then, we theoretically analyze the differential privacy properties for the proposed mechanisms. More concretely, we derive a sufficient and necessary condition for discrete ϵ-differential privacy and a sufficient condition for discrete (ϵ, δ) -differential privacy, with the numerical estimation of differential privacy parameters. These conditions can be applied to analyze the differential privacy properties for the discrete random mechanisms with various noises, e.g., the discrete Gaussian and the discrete Laplacian noise distributions. Further, we design a discrete noise adding mechanism with the Staircase-shaped probability distribution to guarantee arbitrary differential privacy level.
Swarm Intelligence in Cooperative Environments: N-Step Dynamic Tree Search Algorithm Extended Analysis

Reinforcement learning tree-based planning methods have been gaining popularity in the last few years due to their success in single-agent domains, where a perfect simulator model is available, e.g., Go and chess strategic board games. This paper pretends to extend tree search algorithms to the multi-agent setting in a decentralized structure, dealing with scalability issues and exponential growth of computational resources. The N-Step Dynamic Tree Search combines forward planning and direct temporal-difference updates, outperforming markedly state-of-the-art algorithms such as Q-Learning and SARSA. Future state transitions and rewards are predicted with a model built and learned from real interactions between agents and the environment. As an extension of previous work, this paper analyses the developed algorithm in the Hunter-Pursuit cooperative game against intelligent evaders. The N-Step Dynamic Tree Search aims to adapt the most successful single-agent learning methods to the multi-agent boundaries and demonstrates to be a remarkable advance compared to conventional temporal-difference techniques.
Tiny TCN model for Gesture Recognition With Multi-point Low power ToF-Sensors

Gesture recognition is currently an important field of research, as it provides the basis for the next generation of Human Machine Interface. Especially recognizing gestures with touch-less technology has several advantages including, for example, the possibility of working with gloves either outside or in sterile environments. In this work, we present a tiny machine learning neural network model exploiting a novel, miniaturized and low power Time of flight (ToF)-sensor from STMicroelectronics. The algorithm is based on a combination of temporal convolutional networks and is optimized to work on an embedded small and low power ARM cortex-M4 processor with a few kilo Byte of RAM, as employed in every recent Bluetooth Low Energy module. A dataset has been collected with the ToF sensor for training and evaluation of the proposed algorithm. The model has been evaluated for accuracy and a trade-off between computational resources and accuracy is presented. The gesture recognition achieves a very high accuracy of over 96% over 6 different gestures.
Data-Driven Reliability Models of Quantum Circuit: From Traditional ML to Graph Neural Network

The current advancement in quantum computers has been focusing on increasing the number of qubits and enhancing their fidelity. However, the available quantum devices, known as intermediate scale quantum (NISQ) computers, still suffer from different sources of noise that impact their reliability. Thus, practical noise modeling is of great importance in the development of quantum error mitigation approaches. In this article, we propose a machine learning (ML)-based scheme to predict the output fidelity of the quantum circuit executed on NISQ devices. We show the benefit of using graph neural network (GNN)-based models compared to traditional ML-based models in capturing the quantum circuit structure in addition to its gates’ features, which enable characterizing unpredicted quantum circuit errors. We use different metrics to measure the fidelity of the quantum circuit output. Our experimental results using different quantum algorithms executed on IBM Q Guadalupe quantum computer show the high prediction accuracy of our ML reliability models. Our results also show that our models can guide the single-qubit gate rescheduling to improve the output fidelity of the quantum circuit without the need for prior execution of dedicated calibration circuits.
Analog LSTM for Keyword Spotting

In this paper, we present a novel analog application-specific integrated circuit (ASIC) architecture for edge artificial intelligence. Our novel architecture is a power efficient long short-term memory, which is a deep learning neural network that is especially suited for processing time-series sensor data. Evaluated on a 10-class keyword spotting machine learning task, our neural network achieves a classification accuracy of 91 %, with a model size of 2264 parameters and estimated power consumption of 0.76 µW
Low-power Autonomous Adaptation System with Deep Reinforcement Learning

Recently, a lot of autonomous systems such as self-driving have been as close to human-level due to the rapid improvement of deep neural networks (DNN). However, they only performed well in the pre-trained environment and cannot achieve the same performance for the sudden environmental changes. To resolve this issue, self-adaptation through deep reinforcement learning (DRL) has been highlighted. However, it is hard to utilize DRL in the autonomous system due to the massive memory bandwidth requirements. In this paper, we propose a low-power and high-performance DRL system with an energy-efficient DRL chip. The proposed DRL chip can seamlessly compress both weight and feature map to reduce the number of memory access. The proposed system with DRL chip demonstrates adaptation of humanoid to sudden environmental changes at Mujoco Humanoid-v2. The proposed system shows 10.3 iteration/J of training energy efficiency which is 3.9× higher than NVIDIA TX2.